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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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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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Percona Live 2023 Event Recap

Datavail

Percona Live 2023 was an exciting open-source database event that brought together industry experts, database administrators, data engineers, and IT leadership. Keynotes, breakout sessions, workshops, and panel discussions kept the database conversations going throughout the event. Here are a few highlights.

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6 strategic imperatives for your next data strategy

CIO

Not only should the data strategy be cognizant of what’s in the IT and business strategies, it should also be embedded within those strategies as well, helping them unlock even more business value for the organization.

Strategy 290
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Building a vision for real-time artificial intelligence

CIO

After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It isn’t easy.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. DataStax Let’s start with the real-time operational data store, as this is the central data engine for building ML models.