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Brief History of Data Engineering

Jesse Anderson

Google looked over the expanse of the growing internet and realized they’d need scalable systems. 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.

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Apache Hadoop turns 10: The Rise and Glory of Hadoop

ProjectPro

It is difficult to believe that the first Hadoop cluster was put into production at Yahoo, 10 years ago, on January 28 th , 2006. Ten years ago nobody was aware that an open source technology, like Apache Hadoop will fire a revolution in the world of big data. Happy Birthday Hadoop With more than 1.7

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Evolution of the Cloud Data Platform: From Google to Ascend

Ascend.io

Back in 2004, I got to work with MapReduce at Google years before Apache Hadoop was even released, using it on a nearly daily basis to analyze user activity on web search and analyze the efficacy of user experiments. Becoming subconsciously data-first In 2007, my two colleagues and I left Google and started Ooyala.

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Evolution of the Cloud Data Platform: From Google to Ascend

Ascend.io

Back in 2004, I got to work with MapReduce at Google years before Apache Hadoop was even released, using it on a nearly daily basis to analyze user activity on web search and analyze the efficacy of user experiments. Becoming subconsciously data-first In 2007, my two colleagues and I left Google and started Ooyala.

Cloud 52
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Analytics-on-the-fly: from batch to real-time user engagement

Rockset

It was the winter of 2007 when I logged into my newly created Facebook account for the very first time and I was amazed to see Facebook immediately show me three of my friends with whom I had lost touch since elementary school. You need a system that auto-scales so you do not have to pre-provision it for peak capacity.

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Telecom Network Analytics: Transformation, Innovation, Automation

Cloudera

The Dawn of Telco Big Data: 2007-2012. At the same time, centralised big data functions increasingly invested in Hadoop based architectures, in part to move away from proprietary and expensive software, but also in part to engage with what was emerging as a horizontal industry standard technology. Let’s examine how we got here.

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Hands-On Introduction to Delta Lake with (py)Spark

Towards Data Science

The Data Lake architecture was proposed in a period of great growth in the data volume, especially in non-structured and semi-structured data, when traditional Data Warehouse systems start to become incapable of dealing with this demand. Fundamentals of Data Engineering: Plan and Build Robust Data Systems (1st ed.). 5] Databricks.