This article explores the latest advancements in data architecture, focusing on frameworks and newer paradigms such as LakeDB and zero ETL architectures.
April 15, 2025
by Vidyasagar (Sarath Chandra) Machupalli FBCS
CORE
Apache Doris offers various index types, each designed for specific needs. Let’s delve into their features and discover what drives their exceptional performance.
Want to know how this "data giant ship" navigates the waves? Follow this article to uncover the amazing story of Doris and Hudi, the "dream team" of data.
Optimize your Amazon S3 data lake with strategic bucket configurations, data layers, encryption, and lifecycle policies for security, efficiency, and cost savings.
This tutorial covers the setup of a budget-friendly, secure, and scalable ELK logging platform using Infrastructure as Code (IaC) with Terraform and Ansible.
Learn how to integrate Apache Doris with Apache Hudi for efficient federated querying, real-time analytics, and seamless data migration in big data environments.
Explore how IIoT middleware and data streaming tools like Kafka and Flink bridge OT and IT, enabling real-time integration and smarter industrial operations.
Build a secure, scalable AWS data lake with S3, Glue, and Lake Formation, and learn key components, best practices, and analytics for optimized insights.
Apache Doris excels in analytics, SQL support, and cost efficiency, while Elasticsearch leads in text search but has higher storage costs and complexity.
I asked ChatGPT the question, ‘9.9 or 9.11, which is bigger?’ ChatGPT alone answered incorrectly, but with the help of Python, it provided the correct answer: 9.9.
Ensure data quality in pipelines with Great Expectations. Learn to integrate with Databricks, validate data, and automate checks for reliable datasets.
This is the second article in the “Lakehouse: What’s the Big Deal?” series, where I will periodically discuss Lakehouse. Your comments and discussions are welcome.
Integrate Ansible with Kafka for real-time automation: trigger playbooks via Kafka events, enhance incident response, optimize workflows, and scale seamlessly.
This is the first article in the “Lakehouse: What’s the Big Deal?” series, where I will periodically discuss Lakehouse. Your comments and discussions are welcome.
Simplify Kafka with KRaft—ditch ZooKeeper, streamline configs for Docker and Kubernetes, and integrate easily with Spring Boot for development and deployment.
Recommender systems predict preferences using feedback, tackling sparsity and cold starts with collaborative filtering, matrix factorization, and hybrid models.