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1. Streamlining Membership Data Engineering at Netflix with Psyberg

The Netflix TechBlog

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.

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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix.

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Automated Testing in Data Engineering: An Imperative for Quality and Efficiency

DZone

This holds true for the critical field of data engineering as well. As organizations gather and process astronomical volumes of data, manual testing is no longer feasible or reliable. This comprehensive guide takes an in-depth look at automated testing in the data engineering domain.

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Introducing Impressions at Netflix

The Netflix TechBlog

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability. Thus, all data in one region is processed by the Flink job deployed within thatregion.

Tuning 166
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Spice up your Analytics: Amazon QuickSight Now Generally Available in N. Virginia, Oregon, and Ireland.

All Things Distributed

Today, I am very happy to announce that QuickSight is now generally available in the N. When we announced QuickSight last year, we set out to help all customers—regardless of their technical skills—make sense out of their ever-growing data. Put simply, data is not always readily available and accessible to organizational end users.

Analytics 126
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Ready-to-go sample data pipelines with Dataflow

The Netflix TechBlog

Optionally, this step can use the Write-Audit-Publish pattern to ensure that data is correct before it is made available to the rest of the company. some_db.source_table.yaml ??? sparksql_write.sql ??? test_sparksql_write.py filter($"title_id" isNotNull).filter($"view_hours" filter($"view_hours" > 0).groupBy($"title_id",

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It's 2025: How Do You Choose Between Doris and ClickHouse?

DZone

Database selection is a challenge every data engineer faces. Among the many databases available, Apache Doris and ClickHouse, as two mainstream analytical databases, are often compared. Each has its strengths and is suited to different scenarios, making the choice difficult.