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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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A Recap of the Data Engineering Open Forum at Netflix

Netflix Tech

A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.

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DTN’s CTO on combining IT systems after a merger

CIO

Over the years, DTN has bought up several niche data service providers, each with its own IT systems — an environment that challenged DTN IT’s ability to innovate. “We Very little innovation was happening because most of the energy was going towards having those five systems run in parallel.”. The merger playbook.

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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. What is late-arriving data?

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Analytics operating system Redbird makes data more accessible to non-technical users

TechCrunch

Data engineers have a big problem. Almost every team in their business needs access to analytics and other information that can be gleaned from their data warehouses, but only a few have technical backgrounds. ” Tracking venture capital data to pinpoint the next US startup hot spots.

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Why a data scientist is not a data engineer

O'Reilly Media - Ideas

Or, why science and engineering are still different disciplines. "A He would have to ask an engineer to do it for him.". A few months ago, I wrote about the differences between data engineers and data scientists. That was interesting because the data engineers didn’t push back saying they’re data scientists.

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The key to operational AI: Modern data architecture

CIO

The team should be structured similarly to traditional IT or data engineering teams. Technology: The workloads a system supports when training models differ from those in the implementation phase. However, the biggest challenge for most organizations in adopting Operational AI is outdated or inadequate data infrastructure.