Data Engineering

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.

A guide on how to set up an environment to work with Airflow and Spark

How to setup a Hadoop cluster on Docker in simplest way

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

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)

To start: What is Hadoop?

deploy the latest version of Apache Hadoop (Stable release: 3.2.0) on the multi-node cluster to store unstructured data in a distributed manner.

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

Create a streaming pipeline using Docker, Kafka, and Kafka Connect

Learn the key steps of deploying databases and stateful workloads in Kubernetes and meet cloud-native technologies that can streamline Apache Cassandra for K8s.

Everything you ever wanted for your Python project continuous integration pipeline — up-and-running in matter of minutes

Explore top 15 real-world data engineering projects ideas for beginners with source code to gain hands-on experience on diverse data engineering skills.

If the unbundling of Airflow means all the heavy lifting is done by separate tools, what is left behind?

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

What skills do you need to become a data engineer? Learn how to grow your data engineer skillset with this introductory guide.

With an extensive background in data science, analytics, and cloud computing, I am consistently asked...

Resources and tips to help you nail down the DP-203 exam to earn the Azure Data Engineer Associate Certificate

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.

When you watch the blueprint I also use in my cookbook you see the different phases: Connect, Processing Framework, Store and Buffer. At…

Spark Data processing in an easy, cost-effective and serverless way

A step-by-step guide on how to start with Airflow: from your local set-up to creating simple tasks.

Are you disappointed with online SQL tutorials that aren't deep enough? Are you frustrated knowing that you are missing SQL skills, but can't quite put your finger on it? This post is for you. In this post, we go over a few topics that can take your SQL skills to the next level and help you be a better data engineer.