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Fundamentals of Data Engineering

Xebia

The following is a review of the book Fundamentals of Data Engineering by Joe Reis and Matt Housley, published by O’Reilly in June of 2022, and some takeaway lessons. This book is as good for a project manager or any other non-technical role as it is for a computer science student or a data engineer.

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1. Streamlining Membership Data Engineering at Netflix with Psyberg

Netflix Tech

By Abhinaya Shetty , Bharath Mummadisetty At Netflix, our Membership and Finance Data Engineering team harnesses diverse data related to plans, pricing, membership life cycle, and revenue to fuel analytics, power various dashboards, and make data-informed decisions. It also becomes inefficient as the data scale increases.

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Cloudera Data Engineering 2021 Year End Review

Cloudera

Since the release of Cloudera Data Engineering (CDE) more than a year ago , our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. Performance boost with Spark 3.1. Modernizing pipelines. With the release of Spark 3.1

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What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

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Porsche Carrera Cup Brasil gets real-time data boost

CIO

In the annual Porsche Carrera Cup Brasil, data is essential to keep drivers safe and sustain optimal performance of race cars. Until recently, getting at and analyzing that essential data was a laborious affair that could take hours, and only once the race was over. You can monitor and act on the data and you can set thresholds.”

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Snowflake Best Practices for Data Engineering

Perficient

Introduction: We often end up creating a problem while working on data. So, here are few best practices for data engineering using snowflake: 1.Transform Each data model has its own advantages and storing intermediate step results has significant architectural advantages.

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Data Scientist vs Data Engineer: Differences and Why You Need Both

Altexsoft

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering.