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Our First Netflix Data Engineering Summit

The Netflix TechBlog

Engineers from across the company came together to share best practices on everything from Data Processing Patterns to Building Reliable Data Pipelines. The result was a series of talks which we are now sharing with the rest of the Data Engineering community! In this video, Sr.

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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. What is late-arriving data?

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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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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems. ETL workflows), as well as downstream (e.g.

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Chaos Data Engineering Manifesto: 5 Laws for Successful Failures

DZone

During every major election, the wave would crest and crash against our overwhelmed systems before receding, allowing us to assess the damage. It's midnight in the dim and cluttered office of The New York Times, currently serving as the "situation room." A powerful surge of traffic is inevitable.

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Choosing an OLAP Engine for Financial Risk Management: What To Consider?

DZone

From a data engineer's point of view, financial risk management is a series of data analysis activities on financial data. The financial sector imposes its unique requirements on data engineering. Before they adopted an OLAP engine, they were using Kettle to collect data.

FinTech 130
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SIEM Volume Spike Alerts Using ML

DZone

SIEM systems enable early detection of security threats and suspicious activities by analyzing vast amounts of log data in real time. Correlation Engine: SIEM systems analyze and correlate the collected data to identify patterns, anomalies, and potential security incidents.

Storage 136