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.

There is a rise in industry-specific data analytics solutions because building up and maintaining a custom data warehouse is difficult.

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.

A discussion of AI concepts, such as comparing records in a database, and how these techniques can be used in conjunction with Salesforce.

A Channel Interceptor is used to capture a message before being sent or received in order to view or modify it. Learn how a channel interceptor works and how to use it.

Bringing Kafka to the cloud.

A full installation guide for building the event mesh with Apache Camel. We will be using microservice, function, and connector for the connector node in the mesh.

Web scraper development with node.js and vue.js in the front-end with socket.io to get real-time data.

A developer and Hadoop expert runs through the processes he and his team used to transfer their data over network with TLS encryption when switching to Azure.

Bare Metal for IoT runs one application at a time—in stark contrast to regular operating systems. Let's consider the options for developers and devices.

It has been observed when we configure and deploy a multi-node Hadoop cluster or add new DataNodes, there is an SSH permission issue in communication with Hadoop daemons.

When we design and build a Data Platform, we are working on providing the capacities and tools that others teams need to develop their projects. Let's discuss!

What does graphing have to do with machine learning and data science? A lot, actually — learn more in The Year of the Graph Newsletter's Spring 2021 edition.

See what's new with MQTT and IoT.

Kafka Connect assumes a significant part for streaming data between Apache Kafka and other data systems. Importing data from the Database set to Kafka topic.

In this article, we will explore Debezium to capture data changes. Debezium is a distributed open-source platform for change data capture. Point the Debezium...

A hands-on guide to teach you how to interact with Kafka using the Rust programming language.

An experience software architect runs through the concepts behind Apache Spark and gives a tutorial on how to use Spark to better analyze your data sets.

We will see what to have in component tests for an event-driven microservice and understand what component testing is and how it's different from unit testing.

This article discusses that by using appropriate data ingestion tools, companies can collect, import, process data for later use or storage in a database.