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How to learn data engineering

Christophe Blefari

Learn data engineering, all the references ( credits ) This is a special edition of the Data News. But right now I'm in holidays finishing a hiking week in Corsica 🥾 So I wrote this special edition about: how to learn data engineering in 2024. What is Hadoop? Who are the data engineers?

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Brief History of Data Engineering

Jesse Anderson

Doug Cutting took those papers and created Apache Hadoop in 2005. They were the first companies to commercialize open source big data technologies and pushed the marketing and commercialization of Hadoop. Hadoop was hard to program, and Apache Hive came along in 2010 to add SQL. They eventually merged in 2012.

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Reflecting On The Past 6 Years Of Data Engineering

Data Engineering Podcast

In that time there have been a number of generational shifts in how data engineering is done. Materialize’s PostgreSQL-compatible interface lets users leverage the tools they already use, with unsurpassed simplicity enabled by full ANSI SQL support.

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Data Engineering Weekly #173

Data Engineering Weekly

[link] Tweeq: Tweeq Data Platform: Journey and Lessons Learned: Clickhouse, dbt, Dagster, and Superset Tweeq writes about its journey of building a data platform with cloud-agnostic open-source solutions and some integration challenges. It is refreshing to see an open stack after the Hadoop era.

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Top 8 Interview Questions on Apache Sqoop

Analytics Vidhya

Introduction In this constantly growing technical era, big data is at its peak, with the need for a tool to import and export the data between RDBMS and Hadoop. Apache Sqoop stands for “SQL to Hadoop,” and is one such tool that transfers data between Hadoop(HIVE, HBASE, HDFS, etc.)

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Most Essential 2023 Interview Questions on Data Engineering

Analytics Vidhya

Introduction Data engineering is the field of study that deals with the design, construction, deployment, and maintenance of data processing systems. The goal of this domain is to collect, store, and process data efficiently and efficiently so that it can be used to support business decisions and power data-driven applications.

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Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. What are its limitations and how do the Hadoop ecosystem address them? What is Hadoop.