Learn actionable strategies for error management modeling in Apache NiFi data pipelines, and understand the benefits of planning for error handling.

Cryptography can go wrong in a number of ways. We highlight some of the challenges Java developers face when building cryptography into their existing code.

In this article, we will discuss a use case where data from one Kafka cluster has to be migrated to another Kafka Cluster. We will be using mirrormaker 2.

This article delivers an explanation of how Blockchain aids telemedicine services by providing secure data sharing, the privacy of PHIs, and verifiable data.

Batch processing is dealing with a large amount of data; it actually is a method of running high-volume, repetitive data jobs and each job does a specific task.

In this guide, we will showcase NUnit parameterized test cases along with the commonly used attributes like the TestFixture NUnit attribute.

Implementing microservices can bring great benefits, but also complex performance challenges. Performance test can help you confirm the software quality.

Take a look at this tutorial that show you how use R2DBC in a Spring project.

Overview Elastic Stack is a group of open-source tools that includes Elasticsearch for supporting data ingestion, storage, enrichment, visualization, and analysis for containerized applications. As a distributed search and analytics engine, Elasticsearch is an open-source tool that ingests application data, indexes it then stores it for analytics. Since it gathers large volumes of data while indexing different data types, Elasticsearch is often considered write-heavy. To manage such dynamic volumes of data, Kubernetes makes it easy to configure, manage, and scale Elasticsearch clusters. Kubernetes also simplifies the provisioning of resources for Elasticsearch using Infrastructure-as-Code configurations, abstracting cluster management. While Kubernetes alone cannot store data generated by a cluster, persistent volumes can be used to sustain it for future use. To help with this, OpenEBS provisions local persistent volumes or LocalPV and allows for data to be stored on physical disks. Many users have shared their experience of using OpenEBS for local storage management in Kubernetes for Elasticsearch, including the Cloud Native Computing Foundation, ByteDance (TikTok), and Zeta Associates (Lockheed Martin) on the Adopters list in the OpenEBS community available here. In this guide, we explore how OpenEBS LocalPV can provision data storage for Elasticsearch clusters. This guide will also cover - Primary functions of Elastic Stack operators in a Kubernetes cluster Integrating Elasticsearch operators with Fluentd and Kibana to form the EFK stack Monitoring Elasticsearch cluster metrics with Prometheus and Grafana Getting Started with Elasticsearch Analytics Elasticsearch extends the ability to store and search large amounts of textual, graphical or numerical data efficiently. Kubernetes makes it easy to manage the connections between Elasticsearch nodes, thereby simplifying deploying Elasticsearch on-premises or in hosted cloud environments. It must be noted that Elasticsearch nodes are different from Kubernetes nodes of a cluster. While an Elasticsearch node runs a single instance of Elasticsearch, a Kubernetes node is a physical or virtual machine that the orchestrator runs on. Elasticsearch Cluster Topology From Kubernetes’ point of view, an Elasticsearch node can be considered as a POD. Whenever an Elasticsearch cluster is deployed, three types of Elasticsearch PODs are created: Master - manage the Elasticsearch cluster Client - direct incoming traffic to appropriate PODs Data - responsible for storing and availing cluster data The diagram below shows the topology of a typical 7 POD Elasticsearch cluster with 3-master, 2-client and 2-data nodes: Deploying Elasticsearch involves creating manifest files for each of the cluster’s PODs. By connecting to the cluster, OpenEBS creates a visibility tier that enables cluster monitoring, logging and topology checks for LocalPV Storage. Additionally, to enable cluster-wide analytics, the following tools are deployed : Fluentd - An open-source data collection agent that integrates with Elasticsearch to collect log data, transform it then ship it to the Elastic Backend. Fluentd is set up on cluster nodes to collect and convert POD information and send it to the Elasticsearch data PODs for storage and indexing. It is typically set up as a DaemonSet to run on each Kubernetes worker node. Kibana - Once the cluster is deployed on Kubernetes, it needs to be monitored and managed. To help with this, Kibana is used as a visualization tool for cluster data by providing the Elasticsearch client service as an environment variable in PODs that Kibana should connect to. Solution Guide The following solution guide explains the steps and important considerations for deploying Elasticsearch clusters on Kubernetes using OpenEBS Persistent Volumes. By following the guide, you can create persistent storage for the EFK stack supported by Kubernetes, to which OpenEBS is deployed. The guide includes steps on performing metric checks and performance monitoring for the Elasticsearch cluster using Prometheus and Grafana. Let us know how you use Elasticsearch in production and if you have an interesting use case to share. Also, please check out other OpenEBS deployment guides on common Kubernetes stateful workloads on our website. Deploying Kafka on Kubernetes Deploying WordPress on DigitalOcean Kubernetes Deploying Magento on Kubernetes Deploying Percona on Kubernetes Deploying Cassandra on Kubernetes Deploying MinIO on Kubernetes Deploying Prometheus on Kubernetes This article has already been published on https://blog.mayadata.io/deploy-elasticsearch-on-kubernetes-using-openebs-localpv and has been authorized by MayaData for a republish.

Contrary to logs and observability, which show what happens on a service, tracing allows developers and operators to follow a specific request and how it calls different services and dependencies

Introduction to Spring Boot and JDBCTemplate: Refactoring to SpringData JPA.

A step-by-step guide for building an interactive JS Mosaic Chart, illustrated by visualizing data on quarterly PC shipments by brand in 2020.

Learning about performance differences between microservices written in Java and Go will help you plan the language you choose when building your own services.

When working with pages in several automating projects I noticed that the fields like login ID, search fields, etc., are getting saved on the page.

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One of the key challenges of microservices is the reduced visibility of requests that span multiple services. The answer to the perplexing problem is Distributed Tracing.

In the video within this article, we take a closer look at the SpringBoot Configure DataSource Using JNDI, alongside an example using a Tomcat 9 Server

This article contains a quick video with a great tutorial for batch processing large data sets with Spring Boot and Spring batch.

There is a new class of tools for dockerizing and deploying an application to Kubernetes which are aimed at developers. The latest in that category is JKube from RedHat.

BascomTask lightweight in-process task orchestration helps reign in the complexities involved with processing data from multiple sources.