How to Build a Spark Cluster with Docker, JupyterLab, and Apache Livy—a REST API for Apache Spark
Read our step-by-step guide to building an Apache Spark cluster based on the Docker virtual environment with JupyterLab and the Apache Livy REST interface.
How to install Apache Spark on Ubuntu using Apache Bigtop
Want to install Apache Spark using Apache Bigtop? Step by step tutorial. Bigtop is a package manager for Spark, HBase, Hadoop and other Apache projects related to big data. This tutorial is for Machine Learning engineers and Data Scientists looking for a convenient way to manage big data components of their ecosystem.
Description Construct Hadoop-ecosystem cluster composed of 1 master, 1 DB, and n of slaves, using docker-compose. Get experience of hadoop map-reduce routine and hive, sqoop, and hbase system, among the hadoop ecosystem.
This post explains how to setup Yarn master on hadoop 3.1 cluster and run a map reduce program.Before you proceed this document, please make sure you have Hadoop3.1 cluster up and running. if you do not have a setup, please follow below link to setup your cluster and come back to this page.
This post explains how to setup Apache Spark and run Spark applications on the Hadoop with the Yarn cluster manager that is used to run spark examples as deployment mode client and master as yarn. You can also try running the Spark application in cluster mode. Prerequisites : If you don't have Hadoop & Yarn installed, please Install and Setup Hadoop cluster and setup Yarn on Cluster before proceeding with this article.. Spark Install and Setup In order to install and setup Apache Spark on Hadoop cluster, access Apache Spark Download site and go to the Download Apache Spark section
Apache Hadoop Installation on Ubuntu (multi-node cluster).
Below are the steps of Apache Hadoop Installation on a Linux Ubuntu server, if you have a windows laptop with enough memory, you can create 4 virtual machines by using Oracle Virtual Box and install Ubuntu on these VM's. This article assumes you have Ubuntu OS running and doesn't explain how to create VM's and install Ubuntu. Apache Hadoop is an open-source distributed storing and processing framework that is used to execute large data sets on commodity hardware; Hadoop natively runs on Linux operating system, in this article I will explain step by step Apache Hadoop installation version (Hadoop 3.1.1)
Install and Configuration of Apache Hive on multi-node Hadoop cluster
The apache Hive is a data warehouse system. to install and configure the latest version of Apache Hive on top of the existing multi-node Hadoop cluster.
Multi Node Cluster in Hadoop 2.x Here, we are taking two machines – master and slave. On both the machines, a datanode will be running. Let us start with the setup of Multi Node Cluster in Hadoop. PREREQUISITES: Cent OS 6.5 Hadoop-2.7.3 JAVA 8 SSH We have two machines (master and slave) with IP: Master […]
Hive - Installation, All Hadoop sub-projects such as Hive, Pig, and HBase support Linux operating system. Therefore, you need to install any Linux flavored OS. The following simple
Learn the key steps of deploying databases and stateful workloads in Kubernetes and meet cloud-native technologies that can streamline Apache Cassandra for K8s.
15+ Data Engineering Projects for Beginners with Source Code
Explore top 15 real-world data engineering projects ideas for beginners with source code to gain hands-on experience on diverse data engineering skills.
Starting your journey with Microsoft Azure Data Factory
In this article, we will go through the Microsoft Azure Data Factory service, that can be used to ingest, copy and transform data generated from various data sources
Preparing for a data engineering interview and are overwhelmed by all the tools and concepts?. Then this post is for you, in this post we go over the most common tools and concepts you need to know to ace your data engineering interviews.
This post goes over what the ETL and ELT data pipeline paradigms are. It tries to address the inconsistency in naming conventions and how to understand what they really mean. Finally ends with a comparison of the 2 paradigms and how to use these concepts to build efficient and scalable data pipelines.