Big Data

Apache Kafka is known for its ability to process a huge quantity of events in real time. However, to handle millions of events, we need to follow certain best practices while implementing both Kafka producer services and consumer services. Before start using Kafka in your projects, let's understand when to use Kafka: High-volume event streams. When your application/service generates a continuous stream of events like user activity events, website click events, sensor data events, logging events, or stock market updates, Kafka's ability to handle large volumes with low latency is very useful.Real-time analytics. Kafka is especially really helpful in building real-time data processing pipelines, where data needs to be processed as soon as it arrives. It allows you to stream data to analytics engines like Kafka streams, Apache Spark, or Flink for immediate analytics/insights and stream or batch processing.Decoupling applications. While acting as a central message hub, it can decouple different parts of an application, enabling independent development and scaling of services and encouraging the responsible segregation principle.Data integration across systems. When integrating distributed systems, Kafka can efficiently transfer data between different applications across teams/projects, acting as a reliable data broker. Key Differences from Other Queuing Systems Below are the differences of Apache Kafka from systems like ActiveMQ, ZeroMQ, and VerneMQ: Persistent Storage Kafka stores events in a distributed log, allowing the ability to replay data anytime and data persistence even in case of system/node failures, unlike some traditional message queues, which might rely on in-memory storage like Redis. Partitioning Data is partitioned across brokers/topics, enabling parallel processing of large data streams and high throughput. This helps consumer threads to connect to individual partitioning, promoting horizontal scalability. Consumer Groups Multiple consumers can subscribe to the same topic and read from different offsets within a partition, allowing for duplicate consumption patterns for different teams to consume and process the same data for different purposes. Some examples are: User activity consumed by ML teams to detect suspicious activityRecommendation team to build recommendationsAds team to generate relevant advertisements Kafka Producer Best Practices Batch Size and Linger Time By configuring batch.size and linger.ms, you can increase the throughput of your Kafka producer. batch.size is the maximum size of the batch in bytes. Kafka will attempt to batch it before sending it to producers. Linger.ms determines the maximum time in milliseconds that the producer will wait for additional messages to be added to the batch for processing. Configuring batch size and linger.ms settings significantly helps the performance of the system by controlling how much data is accumulated before sending it to processing systems, allowing for better throughput and reduced latencies when dealing with large volumes of data. It can also introduce slight delays depending on the chosen values. Especially, a large batch size with a correct linger.ms can optimize data transfer efficiencies. Compression Another way to increase throughput is to enable compression through the compression.type configuration. The producer can compress data with gzip, snappy, or lz4 before sending it to the brokers. For large data volumes, this configuration helps compression overhead with network efficiency. It also saves bandwidth and increases the throughput of the system. Additionally, by setting the appropriate serializer and key serializer, we can ensure data is serialized in a format compatible with your consumers. Retries and Idempotency To ensure the reliability of the Kafka producer, you should enable retries and idempotency. By configuring retries, the producer can automatically resend any batch of data that does not get ack by the broker within a specified number of tries. Acknowledgments This configuration controls the level of acknowledgment required from the broker before considering a message sent successfully. By choosing the right acks level, you can control your application's reliability. Below are the accepted values for this configuration. 0 – fastest, but no guarantee of message delivery.1 – message is acknowledged once it's written to the leader broker, providing basic reliability.all – message is considered delivered only when all replicas have acknowledged it, ensuring high durability. Configuration-Tuning Based on Workload you should start tracking metrics like message send rate, batch size, and error rates to identify performance bottlenecks. Regularly check and adjust producer settings based on the feature/data modifications or updates. Kafka Consumer Best Practices Consumer Groups Every Kafka consumer should belong to a consumer group; a consumer group can contain one or more consumers. By creating more consumers in the group, you can scale up to read from all partitions, allowing you to process a huge volume of data. The group.id configuration helps identify the consumer group to which the consumer belongs, allowing for load balancing across multiple consumers consuming from the same topic. The best practice is to use meaningful group IDs to easily identify consumer groups within your application. Offset Committing You can control when your application commits offsets, which can help to avoid data loss. There are two ways to commit offsets: automatic and manual. For high-throughput applications, you should consider manual commit for better control. auto.offset.reset – defines what to do when a consumer starts consuming a topic with no committed offsets (e.g., a new topic or a consumer joining a group for the first time). Options include earliest (read from the beginning), latest (read from the end), or none (throw an error). Choose "earliest" for most use cases to avoid missing data when a new consumer joins a group. Controls how a consumer starts consuming data, ensuring proper behavior when a consumer is restarted or added to a group.enable.auto.commit – helps configure to automatically commit offsets periodically. Generally, we set value to false for most production scenarios where we don't need high reliability and manually commit offsets within your application logic to ensure exact-once processing. Provides control to manage offset commits, allowing for more control over data processing.auto.commit.interval.ms – interval in milliseconds at which offsets are automatically committed if enable.auto.commit is set to true. Modify based on your application's processing time to avoid data loss due to unexpected failure. Fetch Size and Max Poll Records This configuration helps control the number of records retrieved in each request, configure the fetch.min.bytes and max.poll.records. Increasing this value can help improve the throughput of your applications while reducing CPU usage and reducing the number of calls made to brokers. fetch.min.bytes – the minimum number of bytes to fetch from a broker in a single poll request. Set a small value to avoid unnecessary network calls, but not too small to avoid excessive polling. It helps optimize the network efficiency by preventing small, frequent requests.fetch.max.bytes – the maximum number of bytes to pull from a broker in a single polling request. Adjust based on available memory to stop overloading the consumer workers. This reduces the amount of data retrieved in a single poll, avoiding memory issues.max.poll.interval.ms – the maximum time to wait for a poll request to return data before timing out. Set a good timeout to avoid consumer hangs/lags if data is not available. It helps prevent consumers from getting stuck waiting for messages for too long. (Sometimes, k8s pods may restart if the liveness probes are impacted). Partition Assignment This is the strategy used to assign partitions (partition.assignment.strategy) to consumers within a group (e.g., range, roundrobin). Use range for most scenarios to evenly distribute partitions across consumers. This enables balanced load distribution among consumers in a group. Here are some important considerations before using Kafka: Complexity. Implementing Kafka requires a deeper understanding of distributed systems concepts like partitioning and offset management due to its advanced features and configurations.Monitoring and management. Implementing monitoring and Kafka cluster management is important to ensure high availability and performance.Security. Implementing robust security practices to protect sensitive data flowing through the Kafka topics is also important. Implementing these best practices can help you scale your Kafka-based applications to handle millions/billions of events. However, it's important to remember that the optimal configuration can vary based on the specific requirements of your application.

What Is Data Governance, and How Do Data Quality, Policies, and Procedures Strengthen It? Data governance refers to the overall management of data availability, usability, integrity, and security in an organization. It encompasses people, processes, policies, standards, and roles that ensure the effective use of information. Data quality is a foundational aspect of data governance, ensuring that data is reliable, accurate, and fit for purpose. High-quality data is accurate, complete, consistent, and timely, which is essential for informed decision-making. Additionally, well-defined policies and procedures play a crucial role in data governance. They provide clear guidelines for data management, ensuring that data is handled properly and complies with relevant regulations. Data Governance Pillars Together, data quality, policies, and procedures strengthen data governance by promoting accountability, fostering trust in data, and enabling organizations to make better data-driven decisions. What Is Data Quality? Data quality is the extent to which data meets a company's standards for accuracy, validity, completeness, and consistency. It is a crucial element of data management, ensuring that the information used for analysis, reporting, and decision-making is reliable and trustworthy. Data Quality Dimensions 1. Why Is Data Quality Important? Data quality is crucial for several key reasons, and below are some of them: Improved Decision-Making High-quality data supports more accurate and informed decision-making. Enhanced Operational Efficiency Clean and reliable data helps streamline processes and reduce errors. Increased Customer Satisfaction Quality data leads to better products and services, ultimately enhancing customer satisfaction. Reduced Costs Poor data quality can result in significant financial losses. Regulatory Compliance Adhering to data quality standards is essential for meeting regulatory requirements. 2. What Are the Key Dimensions of Data Quality? The essential dimensions of data quality are described as follows: Accuracy. Data must be correct and free from errors.Completeness. Data should be whole and entire, without any missing parts. Consistency. Data must be uniform and adhere to established standards.Timeliness. Data should be current and up-to-date.Validity. Data must conform to defined business rules and constraints.Uniqueness. Data should be distinct and free from duplicates. 3. How to Implement Data Quality The following steps will assist in implementing data quality in the organization. Data profiling. Analyze the data to identify inconsistencies, anomalies, and missing values.Data cleansing. Correct errors, fill in missing values, and standardize data formats.Data validation. Implement rules and checks to ensure the integrity of data.Data standardization. Enforce consistent definitions and formats for the data.Master data management (MDM). Centralize and manage critical data to ensure consistency across the organization.Data quality monitoring. Continuously monitor data quality metrics to identify and address any issues.Data governance. Establish policies, procedures, and roles to oversee data quality. By prioritizing data quality, organizations can unlock the full potential of their data assets and drive innovation. Policies Data policies are the rules and guidelines that ensure how data is managed and used across the organization. They align with legal and regulatory requirements such as CCPA and GDPR and serve as the foundation for safeguarding data throughout its life cycle. Data Protection Policies Below are examples of key policies, including those specific to compliance frameworks like the California Consumer Privacy Act (CCPA) and General Data Protection Regulation (GDPR): 1. Protection Against Data Extraction and Transformation Data Validation Policies Define rules to check the accuracy, completeness, and consistency of data during extraction and transformation. Require adherence to data standards such as format, naming conventions, and mandatory fields. Source System Quality Assurance Policies Mandate profiling and quality checks on source systems before data extraction to minimize errors. Error Handling and Logging Policies Define protocols for detecting, logging, and addressing data quality issues during ETL processes. Data Access Policies Define role-based access controls (RBAC) to restrict who can view or modify data during extraction and transformation processes. Audit and Logging Policies Require logging of all extraction, transformation, and loading (ETL) activities to monitor and detect unauthorized changes. Encryption Policies Mandate encryption for data in transit and during transformation to protect sensitive information. Data Minimization Define policies to ensure only necessary data is extracted and used for specific purposes, aligning with GDPR principles. 2. Protection for Data at Rest and Data in Motion Data Profiling Policies Establish periodic profiling of data at rest to assess and maintain its quality. Data Quality Metrics Define specific metrics (e.g., accuracy rate, completeness percentage, duplication rate) that data at rest must meet. Real-Time Monitoring Policies For data in motion, implement policies requiring real-time validation of data against predefined quality thresholds. Encryption Policies Data at rest. Require AES-256 encryption for stored data across structured, semi-structured, and unstructured data formats.Data in motion. Enforce TLS (Transport Layer Security) encryption for data transmitted over networks. Data Classification Policies Define levels of sensitivity (e.g., Public, Confidential, Restricted) and the required protections for each category. Backup and Recovery Policies Ensure periodic backups and the use of secure storage locations with restricted access. Key Management Policies Establish secure processes for generating, distributing, and storing encryption keys. 3. Protection for Different Data Types Structured Data Define rules for maintaining referential integrity in relational databases.Mandate the use of unique identifiers to prevent duplication.Implement database security policies, including fine-grained access controls, masking of sensitive fields, and regular integrity checks. Semi-Structured Data Ensure compliance with schema definitions to validate data structure and consistency.Enforce policies requiring metadata tags to document the origin and context of the data.Enforce security measures like XML/JSON encryption, validation against schemas, and access rules specific to APIs. Unstructured Data Mandate tools for text analysis, image recognition, or video tagging to assess data quality.Define procedures to detect and address file corruption or incomplete uploads.Define policies for protecting documents, emails, videos, and other formats using tools like digital rights management (DRM) and file integrity monitoring. 4. CCPA and GDPR Compliance Policies Accuracy Policies Align with GDPR Article 5(1)(d), which requires that personal data be accurate and up-to-date. Define periodic reviews and mechanisms to correct inaccuracies. Consumer Data Quality Policies Under CCPA, ensure that data provided to consumers upon request is accurate, complete, and up-to-date. Retention Quality Checks Require quality validation of data before deletion or anonymization to ensure compliance. Data Subject Access Rights (DSAR) Policies Define procedures to allow users to access, correct, or delete their data upon request. Third-Party Vendor Policies Require vendors to comply with CCPA and GDPR standards when handling organizational data. Retention and Disposal Policies Align with legal requirements to retain data only as long as necessary and securely delete it after the retention period. Key aspects of data policies include: Access control. Defining who can access specific data sets.Data classification. Categorizing data based on sensitivity and usage.Retention policies. Outlining how long data should be stored.Compliance mandates. Ensuring alignment with legal and regulatory requirements. Clear and enforceable policies provide the foundation for accountability and help mitigate risks associated with data breaches or misuse. Procedures Procedures bring policies to life, and they are step-by-step instructions. They provide detailed instructions to ensure policies are effectively implemented and followed. Below are expanded examples of procedures for protecting data during extraction, transformation, storage, and transit, as well as for structured, semi-structured, and unstructured data: 1. Data Extraction and Transformation Procedures Data Quality Checklists Implement checklists to validate extracted data against quality metrics (e.g., no missing values, correct formats). Compare transformed data with expected outputs to identify errors. Automated Data Cleansing Automated tools are used to detect and correct quality issues, such as missing or inconsistent data, during transformation. Validation Testing Perform unit and system tests on ETL workflows to ensure data quality is maintained. ETL Workflow Monitoring Regularly review ETL logs and audit trails to detect anomalies or unauthorized activities. Validation Procedures Use checksum or hash validation to ensure data integrity during extraction and transformation. Access Authorization Implement multi-factor authentication (MFA) for accessing ETL tools and systems. 2. Data at Rest and Data in Motion Procedures Data Quality Dashboards Create dashboards to visualize quality metrics for data at rest and in motion. Set alerts for anomalies such as sudden spikes in missing or duplicate records. Real-Time Data Validation Integrate validation rules into data streams to catch errors immediately during transmission. Periodic Data Audits Schedule regular audits to evaluate and improve the quality of data stored in systems. Encryption Key Rotation Schedule periodic rotation of encryption keys to reduce the risk of compromise. Secure Transfer Protocols Standardize the use of SFTP (Secure File Transfer Protocol) for moving files and ensure APIs use OAuth 2.0 for authentication. Data Storage Segmentation Separate sensitive data from non-sensitive data in storage systems to enhance security. 3. Structured, Semi-Structured, and Unstructured Data Procedures Structured Data Run data consistency checks on relational databases, such as ensuring referential integrity and no orphan records.Schedule regular updates of master data to maintain consistency.Conduct regular database vulnerability scans.Implement query logging to monitor access patterns and detect potential misuse. Semi-Structured Data Use tools like JSON or XML schema validators to ensure semi-structured data adheres to expected formats.Implement automated tagging and metadata extraction to enrich the data and improve its usability.Validate data against predefined schemas before ingestion into systems.Use API gateways with rate limiting to prevent abuse. Unstructured Data Deploy machine learning tools to assess and improve the quality of text, image, or video data.Regularly scan unstructured data repositories for incomplete or corrupt filesUse file scanning tools to detect and classify sensitive information in documents or media files.Apply automatic watermarking for files containing sensitive data. 4. CCPA and GDPR Compliance Procedures Consumer Request Validation Before responding to consumer requests under CCPA or GDPR, validate the quality of the data to ensure it is accurate and complete. Implement error-handling procedures to address any discrepancies in consumer data. Data Update Procedures Establish workflows for correcting inaccurate data identified during regular reviews or consumer requests. Deletion and Retention Quality Validation Before data is deleted or retained for compliance, quality checks are performed to confirm its integrity and relevance. Right to Access/Deletion Requests Establish a ticketing system for processing data subject requests and verifying user identity before fulfilling the request. Breach Notification Procedures Define steps to notify regulators and affected individuals within the time frame mandated by GDPR (72 hours) and CCPA. Data Anonymization Apply masking or tokenization techniques to de-identify personal data used in analytics. Roles and Responsibilities in Defining Policies and Procedures The following are the various generalized roles and their responsibilities in defining policies and procedures, which may vary depending on the size and policies of the organization. Data Governance Policy Makers 1. Data Governance Council (DGC) Role A strategic decision-making body comprising senior executives and stakeholders from across the organization. Responsibilities Establish the overall data governance framework.Approve and prioritize data governance policies and procedures.Align policies with business objectives and regulatory compliance requirements (e.g., CCPA, GDPR).Monitor compliance and resolve escalated issues. 2. Chief Data Officer (CDO) Role Oversees the entire data governance initiative and ensures policies align with the organization’s strategic goals. Responsibilities Lead the development of data governance policies and ensure buy-in from leadership.Define data governance metrics and success criteria.Ensure the integration of policies across structured, semi-structured, and unstructured data systems.Advocate for resource allocation to support governance initiatives. 3. Data Governance Lead/Manager Role Operationally manages the implementation of data governance policies and procedures. Responsibilities Collaborate with data stewards and owners to draft policies.Ensure policies address data extraction, transformation, storage, and movement.Develop and document procedures based on approved policies.Facilitate training and communication to ensure stakeholders understand and adhere to policies. 4. Data Stewards Role Serve as subject matter experts for specific datasets, ensuring data quality, compliance, and governance. Responsibilities Enforce policies for data accuracy, consistency, and protection.Monitor the quality of structured, semi-structured, and unstructured data.Implement specific procedures such as data masking, encryption, and validation during ETL processes.Ensure compliance with policies related to CCPA and GDPR (e.g., data classification and access controls). 5. Data Owners Role Typically, business leaders or domain experts are responsible for specific datasets within their area of expertise. Responsibilities Define access levels and assign user permissions.Approve policies and procedures related to their datasets.Ensure data handling aligns with regulatory and internal standards.Resolve data-related disputes or issues escalated by stewards. 6. Legal and Compliance Teams Role Ensure policies meet regulatory and contractual obligations. Responsibilities Advise on compliance requirements, such as GDPR, CCPA, and industry-specific mandates.Review and approve policies related to data privacy, retention, and breach response.Support the organization in audits and regulatory inspections. 7. IT and Security Teams Role Provide technical expertise to secure and implement policies at a systems level. Responsibilities Implement encryption, data masking, and access control mechanisms.Define secure protocols for data in transit and at rest.Monitor and log activities to enforce data policies (e.g., audit trails).Respond to and mitigate data breaches, ensuring adherence to policies and procedures. 8. Business Units and Data Consumers Role Act as end users of the data governance framework. Responsibilities Adhere to the defined policies and procedures in their day-to-day operations.Provide feedback to improve policies based on practical challenges.Participate in training sessions to understand data governance expectations. Workflow for Defining Policies and Procedures Steps in the Policy and Procedure Workflow 1. Policy Development Initiation. The CDO and Data Governance Lead identify the need for specific policies based on organizational goals and regulatory requirements.Drafting. Data stewards, legal teams, and IT collaborate to draft comprehensive policies addressing technical, legal, and operational concerns.Approval. The Data Governance Council reviews and approves the policies. 2. Procedure Design Operational input. IT and data stewards define step-by-step procedures to enforce the approved policies.Documentation. Procedures are formalized and stored in a central repository for easy access.Testing. Procedures are tested to ensure feasibility and effectiveness. 3. Implementation and Enforcement Training programs are conducted for employees across roles.Monitoring tools are deployed to track adherence and flag deviations. 4. Continuous Improvement Policies and procedures are periodically reviewed to accommodate evolving regulations, technologies, and business needs. By involving the right stakeholders and clearly defining roles and responsibilities, organizations can ensure their data governance policies and procedures are robust, enforceable, and adaptable to changing requirements. Popular Tools The following table lists the top 10 most popular companies that support data governance, data quality, policies, and procedures: Tool Best For Key Features Use Cases 1 Ataccama Data Quality, MDM, Governance - Automated data profiling, cleansing, and enrichment - AI-driven data discovery and anomaly detection - Ensuring data accuracy during ETL processes - Automating compliance checks (e.g., GDPR, CCPA) 2 Collibra Enterprise Data Governance, Cataloging - Data catalog for structured, semi-structured, and unstructured data - Workflow management - Data lineage tracking - Cross-functional collaboration on governance - Automating compliance documentation and audits 3 Oracle EDM Comprehensive Data Management - Data security and lifecycle management - Real-time quality checks - Integration with Oracle Analytics - Managing policies in complex ecosystems - Monitoring real-time data quality 4 IBM InfoSphere Enterprise-Grade Governance, Quality - Automated data profiling - Metadata management - AI-powered recommendations for data quality - Governing structured and semi-structured data - Monitoring and enforcing real-time quality rules 5 OvalEdge Unified Governance and Collaboration - Data catalog and glossary - Automated lineage mapping - Data masking capabilities - Developing and communicating governance policies - Tracking and mitigating policy violations 6 Manta Data Lineage and Impact Analysis - Visual data lineage - Integration with quality and governance platforms - Enhancing policy enforcement for data in motion - Strengthening data flow visibility 7 Talend Data Fabric End-to-End Data Integration, Governance - Data cleansing and validation - Real-time quality monitoring - Compliance tools - Maintaining data quality in ETL processes - Automating privacy policy enforcement 8 Informatica Axon Enterprise Governance Frameworks - Integrated quality and governance - Automated workflows - Collaboration tools - Coordinating governance across global teams - Establishing scalable data policies and procedures 9 Microsoft Purview Cloud-First Governance and Compliance - Automated discovery for hybrid environments - Policy-driven access controls - Compliance reporting - Governing hybrid cloud data - Monitoring data access and quality policies 10 DataRobot AI-Driven Quality and Governance - Automated profiling and anomaly detection - Governance for AI models - Real-time quality monitoring - Governing data in AI workflows - Ensuring compliance of AI-generated insights Conclusion Together, data quality, policies, and procedures form a robust foundation for an effective data governance framework. They not only help organizations manage their data efficiently but also ensure that data remains a strategic asset driving growth and innovation. By implementing these policies and procedures, organizations can ensure compliance with legal mandates, protect data integrity and privacy, and enable secure and effective data governance practices. This layered approach safeguards data assets while supporting the organization’s operational and strategic objectives. References AtaccamaCollibraWhat Is a Data Catalog?, OracleWhat is a data catalog?, IBM5 Core Benefits of Data Lineage, OvalEdge

Amazon Elastic MapReduce (EMR) is a platform to process and analyze big data. Traditional EMR runs on a cluster of Amazon EC2 instances managed by AWS. This includes provisioning the infrastructure and handling tasks like scaling and monitoring. EMR on EKS integrates Amazon EMR with Amazon Elastic Kubernetes Service (EKS). It allows users the flexibility to run Spark workloads on a Kubernetes cluster. This brings a unified approach to manage and orchestrate both compute and storage resources. Key Differences Between Traditional EMR and EMR on EKS Traditional EMR and EMR on EKS differ in several key aspects: Cluster management. Traditional EMR utilizes a dedicated EC2 cluster, where AWS handles the infrastructure. EMR on EKS, on the other hand, runs on an EKS cluster, leveraging Kubernetes for resource management and orchestration.Scalability. While both services offer scalability, Kubernetes in EMR on EKS provides more fine-grained control and auto-scaling capabilities, efficiently utilizing compute resources.Deployment flexibility. EMR on EKS allows multiple applications to run on the same cluster with isolated namespaces, providing flexibility and more efficient resource sharing. Benefits of Transitioning to EMR on EKS Moving to EMR on EKS brings several key benefits: Improved resource utilization. Enhanced scheduling and management of resources by Kubernetes ensure better utilization of compute resources, thereby reducing costs.Unified management. Big data analytics can be deployed and managed, along with other applications, from the same Kubernetes cluster to reduce infrastructure and operational complexity.Scalable and flexible. The granular scaling offered by Kubernetes, alongside the ability to run multiple workloads in isolated environments, aligns closely with modern cloud-native practices.Seamless integration. EMR on EKS integrates smoothly with many AWS services like S3, IAM, and CloudWatch, providing a consistent and secure data processing environment. Transitioning to EMR on EKS can modernize the way organizations manage their big data workloads. Up next, we'll delve into understanding the architectural differences and the role Kubernetes plays in EMR on EKS. Understanding the Architecture Traditional EMR architecture is based on a cluster of EC2 instances that are responsible for running big data processing frameworks like Apache Hadoop, Spark, and HBase. These clusters are typically provisioned and managed by AWS, offering a simple way to handle the underlying infrastructure. The master node oversees all operations, and the worker nodes execute the actual tasks. This setup is robust but somewhat rigid, as the cluster sizing is fixed at the time of creation. On the other hand, EMR on EKS (Elastic Kubernetes Service) leverages Kubernetes as the orchestration layer. Instead of using EC2 instances directly, EKS enables users to run containerized applications on a managed Kubernetes service. In EMR on EKS, each Spark job runs inside a pod within the Kubernetes cluster, allowing for more flexible resource allocation. This architecture also separates the control plane (Amazon EKS) from the data plane (EMR pods), promoting more modular and scalable deployments. The ability to dynamically provision and de-provision pods helps achieve better resource utilization and cost-efficiency. Role of Kubernetes Kubernetes plays an important role in the EMR on EKS architecture because of its strong orchestration capabilities for containerized applications. Following are some of the significant roles. Pod management. Kubernetes maintains the pod as the smallest manageable unit inside of a Kubernetes Cluster. Therefore, every Spark Job in an EMR on EKS operates on a Pod of its own with a high degree of isolation and flexibility.Resource scheduling. Kubernetes intelligently schedules pods based on resource requests and constraints, ensuring optimal utilization of available resources. This results in enhanced performance and reduced wastage.Scalability. Kubernetes supports both horizontal and vertical scaling. It could dynamically adjust the number of pods depending on the workload at that moment in time, scaling up in high demand and scaling down in low usage periods of time.Self-healing. In case some PODs fail, Kubernetes will independently detect them and replace those to ensure the high resiliency of applications running in the cluster. Planning the Transition Assessing Current EMR Workloads and Requirements Before diving into the transition from traditional EMR to EMR on EKS, it is essential to thoroughly assess your current EMR workloads. Start by cataloging all running and scheduled jobs within your existing EMR environment. Identify the various applications, libraries, and configurations currently utilized. This comprehensive inventory will be the foundation for a smooth transition. Next, analyze the performance metrics of your current workloads, including runtime, memory usage, CPU usage, and I/O operations. Understanding these metrics helps to establish a baseline that ensures the new environment performs at least as well, if not better,r than the old one. Additionally, consider the scalability requirements of your workloads. Some workloads might require significant resources during peak periods, while others run constantly but with lower resource consumption. Identifying Potential Challenges and Solutions Transitioning to EMR on EKS brings different technical and operational challenges. Recognizing these challenges early helps in crafting effective strategies to address them. Compatibility issues. EMR on EKS might be different in terms of specific configurations and applications. Test applications for compatibility and be prepared to make adjustments where needed.Resource management. Unlike traditional EMR, EMR on EKS leverages Kubernetes for resource allocation. Learn Kubernetes concepts such as nodes, pods, and namespaces to efficiently manage resources.Security concerns. System transitions can reveal security weaknesses. Evaluate current security measures and ensure they can be replicated or improved upon in the new setup. This includes network policies, IAM roles, and data encryption practices.Operational overheads. Moving to Kubernetes necessitates learning new operational tools and processes. Plan for adequate training and the adoption of tools that facilitate Kubernetes management and monitoring. Creating a Transition Roadmap The subsequent step is to create a detailed transition roadmap. This roadmap should outline each phase of the transition process clearly and include milestones to keep the project on track. Step 1. Preparation Phase Set up a pilot project to test the migration with a subset of workloads. This phase includes configuring the Amazon EKS cluster and installing the necessary EMR on EKS components. Step 2. Pilot Migration Migrate a small, representative sample of your EMR jobs to EMR on EKS. Validate compatibility and performance, and make adjustments based on the outcomes. Step 3. Full Migration Roll out the migration to encompass all workloads gradually. It’s crucial to monitor and compare performance metrics actively to ensure the transition is seamless. Step 4. Post-Migration Optimization Following the migration, continuously optimize the new environment. Implement auto-scaling and right-sizing strategies to guarantee effective resource usage. Step 5. Training and Documentation Provide comprehensive training for your teams on the new tools and processes. Document the entire migration process, including best practices and lessons learned. Best Practices and Considerations Security Best Practices for EMR on EKS Security will be given the highest priority while moving to EMR on EKS. Data security and compliance laws will ensure the smooth and secure running of the processes. IAM roles and policies. Use AWS IAM roles for least-privilege access. Create policies to grant permissions to users and applications based on their needs.Network security. Leverage VPC endpoints to their maximum capacity in establishing a secure connection between your EKS cluster and any other AWS service. Inbound and outbound traffic at the instance and subnet levels can be secured through security groups and network ACLs.Data encryption. Implement data encryption in transit and at rest. To that end, it is possible to utilize AWS KMS, which makes key management easy. Turn on encryption for any data held on S3 buckets and in transit.Monitoring and auditing. Implement ongoing monitoring with AWS CloudTrail and Amazon CloudWatch for activity tracking, detection of any suspicious activity, and security standards compliance. Performance Tuning and Optimization Techniques Performance tuning on EMR on EKS is crucial to keep the resources utilized effectively and the workloads executed suitably. Resource allocation. The resources need to be allocated based on the workload. Kubernetes node selectors and namespaces allow effective resource allocation.Spark configurations tuning. Spark configuration parameters like spark.executor.memory, spark.executor.cores, and spark.sql.shuffle.partitions are required to be tuned. Tuning needs to be job-dependent based on utilization and capacity in the cluster.Job distribution. Distribute jobs evenly across nodes using Kubernetes scheduling policies. This aids in preventing bottlenecks and guarantees balanced resource usage.Profiling and monitoring. Use tools like CloudWatch and Spark UI to monitor job performance. Identify and address performance bottlenecks by tuning configurations based on insights. Scalability and High Availability Considerations Auto-scaling. Leverage auto-scaling of your cluster and workloads using Kubernetes Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler. This automatically provisions resources on demand to keep up with the needs of jobs.Fault tolerance. Set up your cluster for high availability by spreading the nodes across numerous Availability Zones (AZs). This reduces the likelihood of downtime due to AZ-specific failures.Backup and recovery. Regularly back up critical data and cluster configurations. Use AWS Backup and snapshots to ensure you can quickly recover from failures.Load balancing. Distribute workloads using load balancing mechanisms like Kubernetes Services and AWS Load Balancer Controller. This ensures that incoming requests are evenly spread across the available nodes. Conclusion For teams that are thinking about the shift to EMR on EKS, the first step should be a thorough assessment of their current EMR workloads and infrastructure. Evaluate the potential benefits specific to your operational needs and create a comprehensive transition roadmap that includes pilot projects and phased migration plans. Training your team on Kubernetes and the nuances of EMR on EKS will be vital to ensure a smooth transition and long-term success. Begin with smaller workloads to test the waters and gradually scale up as confidence in the new environment grows. Prioritize setting up robust security and governance frameworks to safeguard data throughout the transition. Implement monitoring tools and cost management solutions to keep track of resource usage and expenditures. I would also recommend adopting a proactive approach to learning and adaptation to leverage the full potential of EMR on EKS, driving innovation and operational excellence.

By Satrajit Basu CORE

The term "big data" often conjures images of massive unstructured datasets, real-time streams, and machine learning algorithms. Amid this buzz, some may question whether SQL, the language of traditional relational databases, still holds its ground. Spoiler alert: SQL is not only relevant but is a cornerstone of modern data warehousing, big data platforms, and AI-driven insights. This article explores how SQL, far from being a relic, remains the backbone of big data and AI ecosystems, thriving in the context of data warehousing and cloud-native technologies like Google BigQuery. The Enduring Role of SQL in Data Warehousing Data warehousing is foundational to analytics and decision-making. At its core, SQL plays a pivotal role in querying, transforming, and aggregating data efficiently. Traditional relational databases like Teradata, Oracle, and SQL Server pioneered the concept of storing structured data for analytical processing, with SQL as their interface. Fast forward to today, modern cloud data warehouses like Google BigQuery, Snowflake, and Amazon Redshift have revolutionized scalability, enabling querying petabytes of data. Yet, SQL remains the common denominator, allowing analysts and engineers to seamlessly interact with these systems. Why SQL Excels in Data Warehousing Declarative querying. SQL allows users to express complex queries without worrying about execution mechanics. This simplicity scales beautifully in modern architectures.Integration with big data. SQL-based tools can process structured and semi-structured data (e.g., JSON, Parquet) stored in cloud data lakes. For example, BigQuery allows SQL queries on data in Google Cloud Storage without moving the data.Interoperability. SQL integrates well with modern BI tools like Tableau and Looker, offering direct querying capabilities for visualization. SQL Meets Big Data In big data, where datasets are distributed across clusters, SQL has adapted to handle scale and complexity. Distributed query engines and cloud-based platforms enable SQL to power advanced analytics on massive datasets. Distributed SQL Query Engines Google BigQuery – a fully managed, serverless data warehouse that lets you run SQL queries over terabytes or petabytes of data with near real-time resultsApache Hive and Presto/Trino – designed for querying distributed file systems like Hadoop HDFS or cloud object storageSnowflake – combines data warehousing and big data with SQL as the querying interface SQL on Data Lakes Modern architectures blur the lines between data lakes and warehouses. SQL tools like BigQuery and AWS Athena allow querying directly on semi-structured data stored in object storage, effectively bridging the gap. Example: SQL in Big Data Analytics SQL SELECT user_id, COUNT(*) AS total_transactions, SUM(amount) AS total_spent FROM `project.dataset.transactions` WHERE transaction_date BETWEEN '2023-01-01' AND '2023-12-31' GROUP BY user_id ORDER BY total_spent DESC LIMIT 10; This query could run on millions of rows in BigQuery, with results returned in seconds. SQL in the Age of AI AI thrives on data, and SQL remains indispensable in the AI lifecycle. From data preparation to serving real-time predictions, SQL bridges the gap between raw data and actionable insights. 1. Data Preparation Before training machine learning models, data must be aggregated, cleaned, and structured. SQL excels in: Joins, aggregations, and filteringFeature engineering with window functions or conditional logic 2. SQL for Machine Learning Modern platforms like BigQuery ML and Snowflake Snowpark allow SQL users to build, train, and deploy ML models directly within the data warehouse. For instance: SQL CREATE MODEL my_model OPTIONS(model_type='linear_reg') AS SELECT feature1, feature2, label FROM `project.dataset.training_data`; This democratizes AI by enabling analysts who may lack coding expertise in Python to participate in ML workflows. 3. Real-Time AI Insights Streaming platforms like Apache Kafka integrate with SQL engines like ksqlDB, allowing real-time analytics and predictions on streaming data. Why SQL Remains Irreplaceable SQL has adapted and thrived because of its unique strengths: Universal language. SQL is universally understood across tools and platforms, enabling seamless communication between different systems.Standardization and extensions. While core SQL syntax is standardized, platforms like BigQuery have introduced extensions (e.g., ARRAY functions) to enhance functionality.Cloud-native scalability. SQL’s integration with cloud platforms ensures it can handle modern workloads, from querying terabytes of data in data lakes to orchestrating machine learning models.Evolving ecosystem. SQL-based tools like dbt have transformed how data transformations are managed in the data pipeline, keeping SQL relevant even in modern data engineering workflows. Challenges and How SQL Overcomes Them While SQL has limitations, such as handling unstructured data or certain scalability concerns, these are addressed by modern innovations: Handling semi-structured data. JSON and ARRAY functions in platforms like BigQuery enable querying nested data directly.Distributed processing. SQL-based engines now scale across clusters to handle petabytes of data efficiently. Conclusion: SQL as the Timeless Backbone of Data and AI From the structured queries of yesterday’s relational databases to today’s cutting-edge big data and AI platforms, SQL has proven its adaptability and indispensability. It continues to evolve, bridging traditional data warehousing with modern big data and AI needs. With tools like Google BigQuery bringing SQL into the forefront of scalable, cloud-native analytics, SQL is far from outdated. It is, in fact, the backbone of modern data ecosystems, ensuring that businesses can make sense of their data in an increasingly complex world. So, is SQL outdated? Not at all. It’s thriving and continuously powering big data and AI powerhouses.

Cardinality is the number of distinct items in a dataset. Whether it's counting the number of unique users on a website or estimating the number of distinct search queries, estimating cardinality becomes challenging when dealing with massive datasets. That's where the HyperLogLog algorithm comes into the picture. In this article, we will explore the key concepts behind HyperLogLog and its applications. HyperLogLog HyperLogLog is a probabilistic algorithm designed to estimate the cardinality of a dataset with both high accuracy and low memory usage. Traditional methods for counting distinct items require storing all the items seen so far, e.g., storing all the user information in the user dataset, which can quickly consume a significant amount of memory. HyperLogLog, on the other hand, uses a fixed amount of memory, a few kilobytes, and still provides accurate estimates of cardinality, making it ideal for large-scale data analysis. Use Cases HyperLogLog is particularly useful in the following scenarios: Limited Memory If working with massive datasets, such as logs from millions of users or network traffic data, storing every unique item might not be feasible due to memory constraints. Approximate Count In many cases, an exact count isn't necessary, and a good estimate is sufficient. HyperLogLog gives an estimate that is close enough to the true value without the overhead of precise computation. Streaming Data When working with continuous streams of data, such as live website traffic or social media feeds, HyperLogLog can update its estimate without needing to revisit past data. Some notable application use cases include the following: Web analytics: Estimating the number of unique users visiting a website. Social media analysis: Counting unique hashtags, mentions, or other distinct items in social media streams.Database systems: Efficiently counting distinct keys or values in large databases.Big data systems: Frameworks like Apache Hadoop and Apache Spark use HyperLogLog to count distinct items in big data pipelines.Network monitoring: Estimating the number of distinct IP addresses or packets in network traffic analysis. Existing Implementations HyperLogLog has been implemented in various languages and data processing frameworks. Some popular tools that implement HyperLogLog are the following: Redis provides a native implementation of HyperLogLog for approximate cardinality estimation via the PFADD, PFCOUNT, and PFMERGE commands. Redis allows users to efficiently track unique items in a dataset while consuming minimal memory.Google BigQuery provides a built in function called APPROX_COUNT_DISTINCT that uses HyperLogLog to estimate the count of distinct items in a large dataset. BigQuery optimizes the query processing by using HyperLogLog to offer highly efficient cardinality estimation without requiring the full storage of data.Apache DataSketches is a collection of algorithms for approximate computations, including HyperLogLog. It is implemented in Java and is often used in distributed computing environments for large-scale data processing.Python package hyperloglog is an implementation of HyperLogLog that allows you to compute the approximate cardinality of a dataset with a small memory footprint.The function approx_count_distinct is available in PySpark's DataFrame API and is used to calculate an approximate count of distinct values in a column of a DataFrame. It is based on the HyperLogLog algorithm, providing a highly memory efficient way of estimating distinct counts. Example Usage Python from pyspark.sql import SparkSession from pyspark.sql import functions spark=SparkSession.builder.appName('Test').getOrCreate() df = spark.createDataFrame([("user1", 1), ("user2", 2), ("user3", 3)]) distinct_count_estimate = df.agg(functions.approx_count_distinct("_1").alias("distinct_count")).collect() print(distinct_count_estimate) Logic The basic idea behind HyperLogLog is to use hash functions to map each item in the dataset to a position in a range of values. By analyzing the position of these items, the algorithm can estimate how many distinct items exist without storing them explicitly. Here's a step-by-step breakdown of how it works: Each item in the set is hashed using a hash function. The output of the hash function is a binary string.HyperLogLog focuses on the leading zeros in the binary representation of the hash value. The more leading zeros, the rarer the value. Specifically, the position of the first 1 bit in the hash is tracked, which gives an idea of how large the number of distinct items could be.HyperLogLog divides the range of possible hash values into multiple buckets or registers. Each register tracks the largest number of leading zeros observed for any item hashed to that register.After processing all items, HyperLogLog combines the information from all registers to compute an estimate of the cardinality. The more registers and the higher the number of leading zeros observed, the more accurate the estimate. HyperLogLog provides an estimate with an error margin. The error rate depends on the number of registers used in the algorithm. The more registers in use, the smaller the error margin, but also the higher the memory usage. The accuracy can be fine-tuned based on the needs of the application. Advantages Here are some of the key advantages of using HyperLogLog. Space Complexity Unlike traditional methods, which require storing each unique item, HyperLogLog uses a fixed amount of memory that scales logarithmically with the number of distinct items. This makes it ideal for large-scale datasets. Time Complexity HyperLogLog is highly efficient in terms of processing speed. It requires constant time for each item processed, making it suitable for real-time or streaming applications. Scalability HyperLogLog scales well with large datasets and is often used in distributed systems or data processing frameworks where handling massive volumes of data is a requirement. Simplicity The algorithm is relatively simple to implement and does not require complex data structures or operations. Other Approaches There are several other approaches for cardinality estimation, such as Count-Min Sketch and Bloom Filters. While each of these methods has its strengths, HyperLogLog stands out in terms of its balance between accuracy and space complexity. Bloom Filters Bloom filters are great for checking if an item exists, but they do not provide an estimate of cardinality. HyperLogLog, on the other hand, can estimate cardinality without needing to store all items. Count-Min Sketch This is a probabilistic data structure used for frequency estimation, but it requires more memory than HyperLogLog for the same level of accuracy in cardinality estimation. Conclusion HyperLogLog is an incredibly efficient and accurate algorithm for estimating cardinality in large datasets. Utilizing probabilistic techniques and hash functions will allow the handling of big data with minimal memory usage, making it an essential tool for applications in data analytics, distributed systems, and streaming data. References https://cloud.google.com/bigquery/docs/reference/standard-sql/hll_functionshttps://redis.io/docs/latest/develop/data-types/probabilistic/hyperloglogs/https://datasketches.apache.org/docs/HLL/HllMap.htmlhttps://pypi.org/project/hyperloglog/https://docs.databricks.com/en/sql/language-manual/functions/approx_count_distinct.html

By Bhala Ranganathan CORE

Event-driven architecture facilitates systems to reply to real-life events, such as when the user's profile is updated. This post illustrates building reactive event-driven applications that handle data loss by combining Spring WebFlux, Apache Kafka, and Dead Letter Queue. When used together, these provide the framework for creating fault-tolerant, resilient, and high-performance systems that are important for large applications that need to handle massive volumes of data efficiently. Features Used in this Article Spring Webflux: It provides a Reactive paradigm that depends on non-blocking back pressure for the simultaneous processing of events.Apache Kafka: Reactive Kafka producers and consumers help in building competent and adaptable processing pipelines.Reactive Streams: They do not block the execution of Kafka producers and consumers' streams.Dead Letter Queue (DLQ): A DLQ stores messages temporarily that could not have been processed due to various reasons. DLQ messages can be later used to reprocess messages to prevent data loss and make event processing resilient. Reactive Kafka Producer A Reactive Kafka Producer pushes messages in parallel and does not block other threads while publishing. It is beneficial where large data to be processed. It blends well with Spring WebFlux and handles backpressure within microservices architectures. This integration helps in not only processing large messages but also managing cloud resources well. The reactive Kafka producer shown above can be found on GitHub. Reactive Kafka Consumer Reactive Kafka Consumer pulls Kafka messages without blocking and maintains high throughput. It also supports backpressure handling and integrates perfectly with WebFlux for real-time data processing. The reactive consumer pipeline manages resources well and is highly suited for applications deployed in the cloud. The reactive Kafka consumer shown above can be found on GitHub. Dead Letter Queue (DLQ) A DLQ is a simple Kafka topic that stores messages sent by producers and fails to be processed. In real time, we need systems to be functional without blockages and failure, and this can be achieved by redirecting such messages to the Dead Letter Queue in the event-driven architecture. Benefits of Dead Letter Queue Integration It provides a fallback mechanism to prevent interruption in the flow of messages.It allows the retention of unprocessed data and helps to prevent data loss.It stores metadata for failure, which eventually aids in analyzing the root cause.It provides as many retries to process unprocessed messages.It decouples error handling and makes the system resilient. Failed messages can be pushed to DLQ from the producer code as shown below: A DLQ Handler needs to be created in the reactive consumer as shown below: Conclusion The incorporation of DLQ with a reactive producer and consumer helps build resilient, fault-tolerant, and efficient event-driven applications. Reactive producers ensure nonblocking message publication; on the other hand, Reactive consumers process messages with backpressure, improving responsiveness. The DLQ provides a fallback mechanism preventing disruptions and prevents data loss. The above architecture ensures isolation of system failure and helps in debugging which can further be addressed to improve applications. The above reference code can be found in the GitHub producer and GitHub consumer. More details regarding the reactive producer and consumer can be found at ReactiveEventDriven. Spring Apache Kafka documents more information regarding DLQ.

Through achieving graduation status from the Cloud Native Computing Foundation, CubeFS reaches an important breakthrough as a distributed file system created by community input. CubeFS's graduation status demonstrates its achieved technical sophistication while establishing its reliable history of managing production workloads on a large scale. CubeFS provides low-latency file lookups and high throughput storage with strong protection through separate handling of metadata and data storage while remaining suited for numerous types of computing workloads. The inherent compatibility between CubeFS's cloud-native design and Kubernetes achieves full automation of deployments together with rolling upgrades as well as scalable node adaptation to meet increasing data needs. CubeFS establishes itself as a trustworthy high-performance solution for container-based organizations wanting to upgrade their storage systems because of its dedicated open-source community support and adherence to the CNCF quality standards. Introduction to CubeFS CubeFS functions as a distributed file system that developers worldwide can access under an open-source license. The distribution of file operations occurs between MetaNodes, which handle metadata management tasks, and DataNodes manage data storage tasks overseen by the Master Node, which coordinates cluster activities. Authored structure achieves quick file searches and maintains high data processing speed. When data nodes fail, replication mechanisms safeguard them, resulting in highly reliable support for essential large-scale applications. Why Deploy on Kubernetes Kubernetes offers automated container orchestration, scaling, and a consistent way to deploy microservices. By running CubeFS on Kubernetes: You can quickly add or remove MetaNodes and DataNodes to match storage needs.You benefit from Kubernetes features like rolling updates, health checks, and autoscaling.You can integrate with the Container Storage Interface (CSI) for dynamic provisioning of volumes. End-to-End Deployment Examples Below are YAML manifests that illustrate a straightforward deployment of CubeFS on Kubernetes. They define PersistentVolumeClaims (PVCs) for each component, plus Deployments or StatefulSets for the Master, MetaNodes, and DataNodes. Finally, they show how to mount and use the file system from a sample pod. Master Setup Master PVC YAML apiVersion: v1 kind: PersistentVolumeClaim metadata: name: cubefs-master-pvc labels: app: cubefs-master spec: accessModes: - ReadWriteOnce resources: requests: storage: 5Gi storageClassName: <YOUR_STORAGECLASS_NAME> Master Service YAML apiVersion: v1 kind: Service metadata: name: cubefs-master-svc labels: app: cubefs-master spec: selector: app: cubefs-master ports: - name: master-port port: 17010 targetPort: 17010 type: ClusterIP Master Deployment YAML apiVersion: apps/v1 kind: Deployment metadata: name: cubefs-master-deploy labels: app: cubefs-master spec: replicas: 1 selector: matchLabels: app: cubefs-master template: metadata: labels: app: cubefs-master spec: containers: - name: cubefs-master image: cubefs/cubefs:latest ports: - containerPort: 17010 volumeMounts: - name: master-data mountPath: /var/lib/cubefs/master env: - name: MASTER_ADDR value: "0.0.0.0:17010" - name: LOG_LEVEL value: "info" volumes: - name: master-data persistentVolumeClaim: claimName: cubefs-master-pvc MetaNode Setup MetaNode PVC YAML apiVersion: v1 kind: PersistentVolumeClaim metadata: name: cubefs-meta-pvc labels: app: cubefs-meta spec: accessModes: - ReadWriteOnce resources: requests: storage: 10Gi storageClassName: <YOUR_STORAGECLASS_NAME> MetaNode StatefulSet YAML apiVersion: apps/v1 kind: StatefulSet metadata: name: cubefs-meta-sts labels: app: cubefs-meta spec: serviceName: "cubefs-meta-sts" replicas: 2 selector: matchLabels: app: cubefs-meta template: metadata: labels: app: cubefs-meta spec: containers: - name: cubefs-meta image: cubefs/cubefs:latest ports: - containerPort: 17011 volumeMounts: - name: meta-data mountPath: /var/lib/cubefs/metanode env: - name: MASTER_ENDPOINT value: "cubefs-master-svc:17010" - name: METANODE_PORT value: "17011" - name: LOG_LEVEL value: "info" volumes: - name: meta-data persistentVolumeClaim: claimName: cubefs-meta-pvc DataNode Setup DataNode PVC YAML apiVersion: v1 kind: PersistentVolumeClaim metadata: name: cubefs-data-pvc labels: app: cubefs-data spec: accessModes: - ReadWriteOnce resources: requests: storage: 100Gi storageClassName: <YOUR_STORAGECLASS_NAME> DataNode StatefulSet YAML apiVersion: apps/v1 kind: StatefulSet metadata: name: cubefs-data-sts labels: app: cubefs-data spec: serviceName: "cubefs-data-sts" replicas: 3 selector: matchLabels: app: cubefs-data template: metadata: labels: app: cubefs-data spec: containers: - name: cubefs-data image: cubefs/cubefs:latest ports: - containerPort: 17012 volumeMounts: - name: data-chunk mountPath: /var/lib/cubefs/datanode env: - name: MASTER_ENDPOINT value: "cubefs-master-svc:17010" - name: DATANODE_PORT value: "17012" - name: LOG_LEVEL value: "info" volumes: - name: data-chunk persistentVolumeClaim: claimName: cubefs-data-pvc Consuming CubeFS With the Master, MetaNodes, and DataNodes running, you can mount CubeFS in your workloads. Below is a simple pod spec that uses a hostPath for demonstration. In practice, you may prefer the CubeFS CSI driver for dynamic volume provisioning. YAML apiVersion: v1 kind: Pod metadata: name: cubefs-client-pod spec: containers: - name: cubefs-client image: cubefs/cubefs:latest command: ["/bin/sh"] args: ["-c", "while true; do sleep 3600; done"] securityContext: privileged: true volumeMounts: - name: cubefs-vol mountPath: /mnt/cubefs volumes: - name: cubefs-vol hostPath: path: /mnt/cubefs-host type: DirectoryOrCreate Inside this pod, you would run: mount.cubefs -o master=cubefs-master-svc:17010 /mnt/cubefs Check logs to ensure successful mounting, and test file I/O operations. Post-Deployment Checks Master Logs: kubectl logs cubefs-master-deploy-<POD_ID>MetaNode Logs: kubectl logs cubefs-meta-sts-0 and kubectl logs cubefs-meta-sts-1DataNode Logs: kubectl logs cubefs-data-sts-0, etc.I/O Test: Write and read files on /mnt/cubefs to confirm everything is functioning. Conclusion Through its CNCF graduation, CubeFS achieves confirmed enterprise-grade status as a cloud-native storage system that withstands demanding data workloads. Organizations gain simple operational storage solutions that improve performance while optimizing resource usage through CubeFS’s scalable architecture and efficient Kubernetes integration, which also provides fault tolerance. CubeFS stands as a dependable choice thanks to features that consistently evolve due to active community backing empowered by CNCF graduation while providing key users support for modern storage solutions that handle any data volume.

By Sai Sandeep Ogety CORE

Understanding Teradata Data Distribution and Performance Optimization Teradata performance optimization and database tuning are crucial for modern enterprise data warehouses. Effective data distribution strategies and data placement mechanisms are key to maintaining fast query responses and system performance, especially when handling petabyte-scale data and real-time analytics. Understanding data distribution mechanisms, workload management, and data warehouse management directly affects query optimization, system throughput, and database performance optimization. These database management techniques enable organizations to enhance their data processing capabilities and maintain competitive advantages in enterprise data analytics. Data Distribution in Teradata: Key Concepts Teradata's MPP (Massively Parallel Processing) database architecture is built on Access Module Processors (AMPs) that enable distributed data processing. The system's parallel processing framework utilizes AMPs as worker nodes for efficient data partitioning and retrieval. The Teradata Primary Index (PI) is crucial for data distribution, determining optimal data placement across AMPs to enhance query performance. This architecture supports database scalability, workload management, and performance optimization through strategic data distribution patterns and resource utilization. Understanding workload analysis, data access patterns, and Primary Index design is essential for minimizing data skew and optimizing query response times in large-scale data warehousing operations. What Is Data Distribution? Think of Teradata's AMPs (Access Module Processors) as workers in a warehouse. Each AMP is responsible for storing and processing a portion of your data. The Primary Index determines how data is distributed across these workers. Simple Analogy Imagine you're managing a massive warehouse operation with 1 million medical claim forms and 10 workers. Each worker has their own storage section and processing station. Your task is to distribute these forms among the workers in the most efficient way possible. Scenario 1: Distribution by State (Poor Choice) Let's say you decide to distribute claims based on the state they came from: Plain Text Worker 1 (California): 200,000 claims Worker 2 (Texas): 150,000 claims Worker 3 (New York): 120,000 claims Worker 4 (Florida): 100,000 claims Worker 5 (Illinois): 80,000 claims Worker 6 (Ohio): 70,000 claims Worker 7 (Georgia): 60,000 claims Worker 8 (Virginia): 40,000 claims Worker 9 (Oregon): 30,000 claims Worker 10 (Montana): 10,000 claims The Problem Worker 1 is overwhelmed with 200,000 formsWorker 10 is mostly idle, with just 10,000 formsWhen you need California data, one worker must process 200,000 forms aloneSome workers are overworked, while others have little to do Scenario 2: Distribution by Claim ID (Good Choice) Now, imagine distributing claims based on their unique claim ID: Plain Text Worker 1: 100,000 claims Worker 2: 100,000 claims Worker 3: 100,000 claims Worker 4: 100,000 claims Worker 5: 100,000 claims Worker 6: 100,000 claims Worker 7: 100,000 claims Worker 8: 100,000 claims Worker 9: 100,000 claims Worker 10: 100,000 claims The Benefits Each worker handles exactly 100,000 formsWork is perfectly balancedAll workers can process their forms simultaneouslyMaximum parallel processing achieved This is exactly how Teradata's AMPs (workers) function. The Primary Index (distribution method) determines which AMP gets which data. Using a unique identifier like claim_id ensures even distribution, while using state_id creates unbalanced workloads. Remember: In Teradata, like in our warehouse, the goal is to keep all workers (AMPs) equally busy for maximum efficiency. The Real Problem of Data Skew in Teradata Example 1: Poor Distribution (Using State Code) SQLite CREATE TABLE claims_by_state ( state_code CHAR(2), -- Only 50 possible values claim_id INTEGER, -- Millions of unique values amount DECIMAL(12,2) -- Claim amount ) PRIMARY INDEX (state_code); -- Creates daily hotspots which will cause skew! Let's say you have 1 million claims distributed across 50 states in a system with 10 AMPs: SQLite -- Query to demonstrate skewed distribution SELECT state_code, COUNT(*) as claim_count, COUNT(*) * 100.0 / SUM(COUNT(*)) OVER () as percentage FROM claims_by_state GROUP BY state_code ORDER BY claim_count DESC; -- Sample Result: -- STATE_CODE CLAIM_COUNT PERCENTAGE -- CA 200,000 20% -- TX 150,000 15% -- NY 120,000 12% -- FL 100,000 10% -- ... other states with smaller percentages Problems With This Distribution 1. Uneven workload California (CA) data might be on one AMPThat AMP becomes overloaded while others are idleQueries involving CA take longer 2. Resource bottlenecks SQLite -- This query will be slow SELECT COUNT(*), SUM(amount) FROM claims_by_state WHERE state_code = 'CA'; -- One AMP does all the work Example 2: Better Distribution (Using Claim ID) SQLite CREATE TABLE claims_by_state ( state_code CHAR(2), claim_id INTEGER, amount DECIMAL(12,2) ) PRIMARY INDEX (claim_id); -- Better distribution Why This Works Better 1. Even distribution Plain Text -- Each AMP gets approximately the same number of rows -- With 1 million claims and 10 AMPs: -- Each AMP ≈ 100,000 rows regardless of state 2. Parallel processing SQLite -- This query now runs in parallel SELECT state_code, COUNT(*), SUM(amount) FROM claims_by_state GROUP BY state_code; -- All AMPs work simultaneously Visual Representation of Data Distribution Poor Distribution (State-Based) SQLite -- Example demonstrating poor Teradata data distribution CREATE TABLE claims_by_state ( state_code CHAR(2), -- Limited distinct values claim_id INTEGER, -- High cardinality amount DECIMAL(12,2) ) PRIMARY INDEX (state_code); -- Causes data skew Plain Text AMP1: [CA: 200,000 rows] ⚠️ OVERLOADED AMP2: [TX: 150,000 rows] ⚠️ HEAVY AMP3: [NY: 120,000 rows] ⚠️ HEAVY AMP4: [FL: 100,000 rows] AMP5: [IL: 80,000 rows] AMP6: [PA: 70,000 rows] AMP7: [OH: 60,000 rows] AMP8: [GA: 50,000 rows] AMP9: [Other states: 100,000 rows] AMP10: [Other states: 70,000 rows] Impact of Poor Distribution Poor Teradata data distribution can lead to: Unbalanced workload across AMPsPerformance bottlenecksInefficient resource utilizationSlower query response times Good Distribution (Claim ID-Based) SQLite -- Implementing optimal Teradata data distribution CREATE TABLE claims_by_state ( state_code CHAR(2), claim_id INTEGER, amount DECIMAL(12,2) ) PRIMARY INDEX (claim_id); -- Ensures even distribution Plain Text AMP1: [100,000 rows] ✓ BALANCED AMP2: [100,000 rows] ✓ BALANCED AMP3: [100,000 rows] ✓ BALANCED AMP4: [100,000 rows] ✓ BALANCED AMP5: [100,000 rows] ✓ BALANCED AMP6: [100,000 rows] ✓ BALANCED AMP7: [100,000 rows] ✓ BALANCED AMP8: [100,000 rows] ✓ BALANCED AMP9: [100,000 rows] ✓ BALANCED AMP10: [100,000 rows] ✓ BALANCED Performance Metrics from Real Implementation In our healthcare system, changing from state-based to claim-based distribution resulted in: 70% reduction in query response time85% improvement in concurrent query performance60% better CPU utilization across AMPsElimination of processing hotspots Best Practices for Data Distribution 1. Choose High-Cardinality Columns Unique identifiers (claim_id, member_id)Natural keys with many distinct values 2. Avoid Low-Cardinality Columns State codesStatus flagsDate-only values 3. Consider Composite Keys (Advanced Teradata Optimization Techniques) Use when you need: Better data distribution than a single column providesEfficient queries on combinations of columnsBalance between distribution and data locality Plain Text Scenario | Single PI | Composite PI ---------------------------|--------------|------------- High-cardinality column | ✓ | Low-cardinality + unique | | ✓ Frequent joint conditions | | ✓ Simple equality searches | ✓ | SQLite CREATE TABLE claims ( state_code CHAR(2), claim_id INTEGER, amount DECIMAL(12,2) ) PRIMARY INDEX (state_code, claim_id); -- Uses both values for better distribution 4. Monitor Distribution Quality SQLite -- Check row distribution across AMPs SELECT HASHAMP(claim_id) as amp_number, COUNT(*) as row_count FROM claims_by_state GROUP BY 1 ORDER BY 1; /* Example Output: amp_number row_count 0 98,547 1 101,232 2 99,876 3 100,453 4 97,989 5 101,876 ...and so on */ What This Query Tells Us This query is like taking an X-ray of your data warehouse's health. It shows you how evenly your data is spread across your Teradata AMPs. Here's what it does: HASHAMP(claim_id) – this function shows which AMP owns each row. It calculates the AMP number based on your Primary Index (claim_id in this case)COUNT(*) – counts how many rows each AMP is handlingGROUP BY 1 – groups the results by AMP numberORDER BY 1 – displays results in AMP number order Interpreting the Results Good Distribution You want to see similar row counts across all AMPs (within 10-15% variance). Plain Text AMP 0: 100,000 rows ✓ Balanced AMP 1: 98,000 rows ✓ Balanced AMP 2: 102,000 rows ✓ Balanced Poor Distribution Warning signs include large variations. Plain Text AMP 0: 200,000 rows ⚠️ Overloaded AMP 1: 50,000 rows ⚠️ Underutilized AMP 2: 25,000 rows ⚠️ Underutilized This query is essential for: Validating Primary Index choicesIdentifying data skew issuesMonitoring system healthPlanning optimization strategies Conclusion Effective Teradata data distribution is fundamental to achieving optimal database performance. Organizations can significantly improve their data warehouse performance and efficiency by implementing these Teradata optimization techniques.

Elasticsearch and OpenSearch are powerful tools for handling search and analytics workloads, offering scalability, real-time capabilities, and a rich ecosystem of plugins and integrations. Elasticsearch is widely used for full-text search, log monitoring, and data visualization across industries due to its mature ecosystem. OpenSearch, a community-driven fork of Elasticsearch, provides a fully open-source alternative with many of the same capabilities, making it an excellent choice for organizations prioritizing open-source principles and cost efficiency. Migration to OpenSearch should be considered if you are using Elasticsearch versions up to 7.10 and want to avoid licensing restrictions introduced with Elasticsearch's SSPL license. It is also ideal for those seeking continued access to an open-source ecosystem while maintaining compatibility with existing Elasticsearch APIs and tools. Organizations with a focus on community-driven innovation, transparent governance, or cost control will find OpenSearch a compelling option. History Elasticsearch, initially developed by Shay Banon in 2010, emerged as a powerful open-source search and analytics engine built on Apache Lucene. It quickly gained popularity for its scalability, distributed nature, and robust capabilities in full-text search, log analysis, and real-time data processing. Over the years, Elasticsearch became part of the Elastic Stack (formerly ELK Stack), integrating with Kibana, Logstash, and Beats to provide end-to-end data management solutions. However, a significant shift occurred in 2021 when Elastic transitioned Elasticsearch and Kibana to a more restrictive SSPL license. In response, AWS and the open-source community forked Elasticsearch 7.10 and Kibana to create OpenSearch, adhering to the Apache 2.0 license. OpenSearch has since evolved as a community-driven project, ensuring a truly open-source alternative with comparable features and ongoing development tailored for search, observability, and analytics use cases. Why Migrate to OpenSearch? 1. Open Source Commitment OpenSearch adheres to the Apache 2.0 license, ensuring true open-source accessibility. In contrast, Elasticsearch's transition to a more restrictive SSPL license has raised concerns about vendor lock-in and diminished community-driven contributions. 2. Cost Efficiency OpenSearch eliminates potential licensing fees associated with Elasticsearch's newer versions, making it an attractive choice for organizations seeking cost-effective solutions without compromising on capabilities. 3. Compatibility OpenSearch maintains compatibility with Elasticsearch versions up to 7.10, including many of the same APIs and tools. This ensures a smooth migration with minimal disruption to existing applications and workflows. 4. Active Development and Support Backed by AWS and an active community, OpenSearch receives consistent updates, feature enhancements, and security patches. Its open governance model fosters innovation and collaboration, ensuring the platform evolves to meet user needs. 5. Customizable and Flexible OpenSearch allows for greater customization and flexibility compared to proprietary systems, enabling organizations to tailor their deployments to specific use cases without constraints imposed by licensing terms. 6. Evolving Ecosystem OpenSearch offers OpenSearch Dashboards (a Kibana alternative) and plugins tailored for observability, log analytics, and full-text search. These tools expand its usability across domains while ensuring continued alignment with user needs. When to Migrate Licensing concerns: If you wish to avoid SSPL licensing restrictions introduced by Elastic after version 7.10.Budgetary constraints: To minimize costs associated with commercial licensing while retaining a powerful search and analytics engine.Future-proofing: To adopt a platform with a transparent development roadmap and strong community backing.Feature parity: When using features supported in Elasticsearch 7.10 or earlier, as these are fully compatible with OpenSearch.Customization needs: When greater flexibility, open governance, or community-led innovations are critical to your organization’s goals. Migrating to OpenSearch ensures you maintain a robust, open-source-driven platform while avoiding potential restrictions and costs associated with Elasticsearch’s licensing model. Pre-Migration Checklist Before migrating from Elasticsearch to OpenSearch, follow this checklist to ensure a smooth and successful transition: 1. Assess Version Compatibility Verify that your Elasticsearch version is compatible with OpenSearch. OpenSearch supports Elasticsearch versions up to 7.10.Review any API or plugin dependencies to ensure they are supported in OpenSearch. 2. Evaluate Use of Proprietary Features Identify any proprietary features or plugins (e.g., Elastic's machine learning features) that may not have equivalents in OpenSearch.Assess whether third-party tools or extensions used in your Elasticsearch cluster will be impacted. 3. Backup Your Data Create a full backup of your Elasticsearch indices using the snapshot API to avoid any potential data loss: Shell PUT /_snapshot/backup_repo/snapshot_1?wait_for_completion=true Ensure backups are stored in a secure and accessible location for restoration. 4. Review Cluster Configurations Document your current Elasticsearch cluster settings, including node configurations, shard allocations, and index templates.Compare these settings with OpenSearch to identify any required adjustments. 5. Test in a Staging Environment Set up a staging environment to simulate the migration process.Restore data snapshots in the OpenSearch staging cluster to validate compatibility and functionality.Test your applications, queries, and workflows in the staging environment to detect issues early. 6. Check API and Query Compatibility Review the Elasticsearch APIs and query syntax used in your application. OpenSearch maintains most API compatibility, but slight differences may exist.Use OpenSearch’s API compatibility mode for smoother transitions. 7. Update Applications and Clients Replace Elasticsearch client libraries with OpenSearch-compatible libraries (e.g., opensearch-py for Python or OpenSearch Java Client).Test client integration to ensure applications interact correctly with the OpenSearch cluster. 8. Verify Plugin Support Ensure that any plugins used in Elasticsearch (e.g., analysis, security, or monitoring plugins) are available or have alternatives in OpenSearch.Identify OpenSearch-specific plugins that may enhance your cluster's functionality. 9. Inform Stakeholders Communicate the migration plan, timeline, and expected downtime (if any) to all relevant stakeholders.Ensure teams responsible for applications, infrastructure, and data are prepared for the migration. 10. Plan for Rollback Develop a rollback plan in case issues arise during the migration. This plan should include steps to restore the original Elasticsearch cluster and data from backups. 11. Monitor Resources Ensure your infrastructure can support the migration process, including disk space for snapshots and sufficient cluster capacity for restoration. By completing this checklist, you can minimize risks, identify potential challenges, and ensure a successful migration from Elasticsearch to OpenSearch. Step-by-Step Migration Guide 1. Install OpenSearch Download the appropriate version of OpenSearch from opensearch.org.Set up OpenSearch nodes using the official documentation, ensuring similar cluster configurations to your existing Elasticsearch setup. 2. Export Data from Elasticsearch Use the snapshot APIto create a backup of your Elasticsearch indices: Shell PUT /_snapshot/backup_repo/snapshot_1?wait_for_completion=true Ensure that the snapshot is stored in a repository accessible to OpenSearch. 3. Import Data into OpenSearch Register the snapshot repository in OpenSearch: Shell PUT /_snapshot/backup_repo { "type": "fs", "settings": { "location": "path_to_backup", "compress": true } } Restore the snapshot to OpenSearch: Shell POST /_snapshot/backup_repo/snapshot_1/_restore 4. Update Applications and Clients Update your application’s Elasticsearch client libraries to compatible OpenSearch clients, such as the OpenSearch Python Client (opensearch-py) or Java Client.Replace Elasticsearch endpoints in your application configuration with OpenSearch endpoints. 5. Validate Data and Queries Verify that all data has been restored successfully.Test queries, index operations, and application workflows to ensure everything behaves as expected. 6. Monitor and Optimize Use OpenSearch Dashboards (formerly Kibana) to monitor cluster health and performance.Enable security features like encryption, authentication, and role-based access controls if required. Post-Migration Considerations 1. Plugins and Features If you rely on Elasticsearch plugins, verify their availability or find OpenSearch alternatives. 2. Performance Tuning Optimize OpenSearch cluster settings to match your workload requirements.Leverage OpenSearch-specific features, such as ultra-warm storage, for cost-efficient data retention. 3. Community Engagement Join the OpenSearch community for support and updates.Monitor release notes to stay informed about new features and improvements. Challenges and Tips for Migrating from Elasticsearch to OpenSearch 1. Plugin Compatibility Challenge Some Elasticsearch plugins, especially proprietary ones, may not have direct equivalents in OpenSearch. Tips Audit your current Elasticsearch plugins and identify dependencies.Research OpenSearch’s plugin ecosystem or alternative open-source tools to replace missing features.Consider whether OpenSearch’s built-in capabilities, such as OpenSearch Dashboards, meet your needs. 2. API Differences Challenge While OpenSearch maintains compatibility with Elasticsearch APIs up to version 7.10, minor differences or deprecated endpoints may impact functionality. Tips Use OpenSearch’s API compatibility mode to test and adapt APIs gradually.Review API documentation and replace deprecated endpoints with recommended alternatives. 3. Data Migration Challenge Migrating large datasets can be time-consuming and prone to errors, especially if there are format or schema differences. Tips Use the snapshot and restore approach for efficient data transfer.Test the restoration process in a staging environment to ensure data integrity.Validate data post-migration by running key queries to confirm consistency. 4. Performance Tuning Challenge OpenSearch and Elasticsearch may have differences in cluster configurations and performance tuning, potentially leading to suboptimal performance post-migration. Tips Monitor cluster performance using OpenSearch Dashboards or other monitoring tools. Adjust shard sizes, indexing strategies, and resource allocation to optimize cluster performance. 5. Client and Application Integration Challenge Applications using Elasticsearch client libraries may require updates to work with OpenSearch. Tips Replace Elasticsearch clients with OpenSearch-compatible versions, such as opensearch-py (Python) or the OpenSearch Java Client.Test application workflows and query execution to ensure smooth integration. 6. Limited Features in OpenSearch Challenge Certain proprietary Elasticsearch features (e.g., machine learning jobs, Elastic Security) are not available in OpenSearch. Tips Identify critical features missing in OpenSearch and determine their importance to your use case.Explore third-party or open-source alternatives to replace unavailable features. 7. Training and Familiarity Challenge Teams familiar with Elasticsearch may face a learning curve when transitioning to OpenSearch, especially for cluster management and new features. Tips Provide training and documentation to familiarize your team with OpenSearch’s tools and workflows.Leverage OpenSearch’s active community and forums for additional support. 8. Real-Time Data and Downtime Challenge For real-time systems, ensuring minimal downtime during migration can be difficult. Tips Plan the migration during low-traffic periods.Use a blue-green deployment strategy to switch seamlessly between clusters.Sync new data into OpenSearch using tools like Logstash or Beats during the migration window. 9. Scalability and Future Growth Challenge Ensuring the new OpenSearch cluster can handle future growth and scalability requirements. Tips Plan for scalability by designing a cluster architecture that supports horizontal scaling.Use OpenSearch’s distributed architecture to optimize resource usage. 10. Community Support Challenge While OpenSearch has a growing community, some advanced issues may lack extensive documentation or third-party solutions. Tips Engage with the OpenSearch community via forums and GitHub for troubleshooting.Regularly monitor OpenSearch updates and contribute to the community for better insights. By anticipating these challenges and following these tips, organizations can navigate the migration process effectively, ensuring a seamless transition while maintaining search and analytics performance. Conclusion Migrating from Elasticsearch to OpenSearch is a strategic decision for organizations seeking to align with open-source principles, reduce costs, and maintain compatibility with established search and analytics workflows. While the migration process presents challenges, such as plugin compatibility, API differences, and data migration complexities, these can be effectively managed through careful planning, thorough testing, and leveraging the vibrant OpenSearch community.

Dark data may contain secret information that is valuable for corporate operations. Companies can lead the competition by gaining insights from dark data using the relevant tools and practices. Let's check what dark data is all about and how to use it to make smarter decisions. What Is Dark Data? Dark data is the data collected and stored by an organization but is not analyzed or used for any essential purpose. It is frequently referred to as "data that lies in the shadows" because it is not actively used or essential in decision-making processes. Below are some examples of dark data: Customer feedback: Many organizations collect customer feedback via questionnaires. However, this data may not be analyzed or used in any helpful way.Social media platforms: Social media platforms generate voluminous data, including posts, comments, and user interactions. While some firms may use this information for marketing and consumer interaction, much remains unanalyzed.Email attachments and inboxes: Many firms keep large volumes of data in email attachments and inboxes. While some of this material may be studied or used, much remains unreadable. This data may contain helpful information such as client feedback, sales leads, and internal discussions. Organizations may store dark data for compliance or recordkeeping purposes, or they may believe the data may be helpful in the future when they have better technology and analytical capabilities to process it. However, keeping and safeguarding data can be costly, and sensitive information may be exposed if the data is not handled correctly. As a result, businesses must carefully examine the value of their dark data and devise methods for collecting, keeping, and analyzing it that balance potential benefits against costs and hazards. How Is Dark Data Useful for Organizations? Dark data can be highly beneficial to businesses as it offers insights and business intelligence that wouldn't be available otherwise. Companies that analyze dark data can better understand their customers, operations, and market trends. This enables them to make the best decisions and improve overall performance. Dark data can help organizations recoup lost opportunities by uncovering previously unknown patterns and trends. For example, dark data analysis can disclose client preferences, purchasing behaviors, and pain points, which can be leveraged to improve customer satisfaction. It can also assist businesses in identifying and addressing operational inefficiencies, such as bottlenecks in manufacturing or supply chain operations, which can lead to cost savings and increased productivity. How to Find the Dark Data? Finding dark data can be difficult since it is sometimes concealed inside enormous data sets and may not be readily available. There are different methods to identify and locate dark data. Some of them include the following: Data Profiling Data profiling means examining the structure and content of data sets to determine their characteristics and potential worth. This can assist in finding potentially useful data sets that have not yet been evaluated. Data Discovery Tools Organizations can identify and locate dark data using various data discovery technologies. These technologies scan data sets for patterns and relationships that can help identify useful data. Keyword Search Searching for specific keywords or phrases might help them find data sets relevant to their needs. Data Classification Data classification is based on relevance, value, and retention terms, allowing companies to identify no longer-needed data that can be removed or archived. Auditing This entails checking data access logs, system logs, and backups to find data that hasn't been viewed or used in a long time. It's vital to remember that finding dark data is an ongoing process that necessitates constant research and observation to detect new data sets and changes to current data. How Is Dark Data Created? Dark data occurs when data is captured but not used or examined. This can occur due to a variety of factors, including: 1. Unstructured Data When data is acquired in unstructured formats such as emails, papers, or social media posts, it isn't easy to search, analyze, and use the information effectively. 2. Lack of Data Governance This occurs when an organization lacks data management policies and procedures, resulting in data collection and storage without a clear goal or use. 3. Data Silos Data silos relate to data isolation within a company, in which various departments or teams collect, store, and use data independently. As a result, data may become difficult to access or exchange within the firm. 4. Using Legacy Systems If an organization continues to employ outdated technologies incompatible with current systems, accessing and using data saved on modern devices will be difficult. These conditions might make data harder to locate and retrieve, resulting in black data. How Is Dark Data Related to Big Data? Dark data is a subset of big data that is not currently being used, whereas big data might contain dark and beneficial data. Big Data Big data refers to all sorts of data within a company, both organized and unstructured, that is used for analytics and reporting. This data can come from various sources, including client transactions, social media, sensor data, and log files. The volume, pace, and variety of big data can make it difficult to process and evaluate using conventional approaches. Dark Data Dark data, on the other hand, refers to any type of data (structured or unstructured) not available for reporting or analytics. Organizations may be unaware of the presence of dark data or lack the necessary resources or technology to evaluate it. Use Dark Data for Decision-Making Using these techniques, organizations can effectively tap into the hidden potential of dark data to get important insights and improve decision-making. 1. Identify the Dark Data The initial stage is to discover and gather relevant data. This can be accomplished by creating an inventory of data currently being gathered and kept but not used. 2. Clean and Organize the Data Once the dark data has been collected, it must be cleansed before further analysis. This may include deleting duplicate data, correcting errors, and formatting information to make it easier to work with. 3. Analyze the Data After the data has been cleansed and categorized, it can be examined to reveal patterns and insights that will aid decision-making. This can be accomplished through various techniques, including data mining, machine learning, and statistical analysis. 4. Communicate the Results The insights and findings from the dark data analysis must be communicated to the relevant stakeholders to support decision-making. This can be accomplished via data visualization or report generation. Monitoring the consequences and outcomes of decisions is critical for determining their efficacy and making required adjustments. Dark data can benefit sentiment analysis, predictive maintenance, client retention, and acquisition. A clear framework and establishing particular business use cases for dark data will aid in efficient and effective exploitation. Optimize the Value of Dark Data There are several ways to optimize the value of dark data: Determine the Business Objectives Identifying precise business objectives is the first step in maximizing the value of dark data. Deciding whether data is valuable and how to analyze it might not be easy without specific goals. For example, if the goal is to increase customer satisfaction, prioritize dark data derived from client feedback. Select the Appropriate Tools The unique business objectives and data type will determine the methods and procedures utilized to evaluate dark data. Natural Language Processing (NLP) can analyze unstructured data from consumer comments, while data mining can detect trends in massive datasets. Collaborate With Cross-Functional Teams Collaborating with cross-functional teams, such as IT, data science, and business divisions, can assist in guaranteeing that dark data is studied in light of the organization's broader goals and objectives. Establish a Governance Framework A governance framework is required to ensure that data is used ethically and lawfully and to preserve individual privacy. It also helps to guarantee that the data is correct, thorough, and consistent. Resources to Learn About Dark Data Several resources, including books, articles, online courses, and tutorials, are available for learning about dark data. It is critical to experiment with many resources to see which one best suits your learning style and skills. Furthermore, it's a good idea to keep up with the latest advances and trends in the sector by following relevant blogs, forums, and industry experts. 1. Dark Data: Why What You Don't Know Matters This book is a practical guide to understanding the principles of dark data in depth. It includes several real-world examples and case studies to help readers understand the topic. The author provides various examples from other businesses to demonstrate the topics presented in the book. These examples help readers from all backgrounds relate to and comprehend the book better. 2. Dark Data: Control, Alt, Delete This book is an engaging and instructive handbook that provides a thorough overview of the issues and opportunities that dark data presents in today's digital world. The author has presented a step-by-step approach for identifying, collecting, and analyzing dark data and using it to achieve a competitive advantage in business. 3. Dark Data and Dark Social This is a must-read book for anyone looking to stay ahead of the curve in the data-driven era. In addition, the author has covered various issues, such as data governance, privacy, and security, making the book an invaluable resource for anyone in data science or business management. Conclusion Although dark data can be a valuable resource for businesses, its sheer volume and complexity make it challenging to manage and evaluate. Organizations must have a strategy to effectively use dark data to identify, gather, and assess it. This entails investing in data management and analysis technologies and hiring technical personnel with the required skills and expertise.

By Vijay Singh Khatri CORE