Integrated Azure Synapse Workspace helps handle the security of data in one place for all data lakes, data analytics, and warehousing needs, but also requires learning some new concepts.

The serverless development model is now a requirement for enterprises that want to spin up their business applications on-demand rather than running them all the time.

How to create gorgeous documentation using GitBook — and why you should use the platform for your next project.

Introduction to Apache Kafka with Spring.

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

Improve your team's pull request management system to ship faster more often. Here, we'll discuss challenges and their solutions in managing pull requests.

The purpose of this article is to show how you can attach Continuous Delivery (CD) to build a CI/CD pipeline so you can deploy your applications to Kubernetes.

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.

In this article, we will discuss and explore options on how we can customize the configuration for Artemis Broker on Kubernetes or OpenShift.

Learn best practices and considerations for your multi-tenant apps!

Learn how to integrate mxGraph in TypeScript project.

Are you using IaC, like Terraform? Learn about IaC OSS testing tools, like Terrascan, Checkov, TFLint, Tf-sec, Sentinel, and others – and how they compare.

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

Java serverless functions with Quarkus

This article will be helpful for you to understand how Kubernetes requests and limits work, and why they can work in an expected way.

IaC tackles problems that were present before its use, such as manual environment build and approval processes, high costs, hardware issues, and human error.

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.

We run through a tutorial on on how to create Azure DevOps Artifacts, connect our Artifacts to a Maven feed, and build and push the changes to our Artifacts.

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

A discussion of testing pain points with the DevOps pipeline, the issues they can cause to development teams doing CI/CD, and how to resolve these issues.