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Introducing Impressions at Netflix

The Netflix TechBlog

Every image you hover over isnt just a visual placeholder; its a critical data point that fuels our sophisticated personalization engine. Collecting raw impression events Filtering & Enriching Raw Impressions Once the raw impression events are queued, a stateless Apache Flink job takes charge, meticulously processing this data.

Tuning 166
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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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Pushy to the Limit: Evolving Netflix’s WebSocket proxy for the future

The Netflix TechBlog

Dynomite is a Netflix open source wrapper around Redis that provides a few additional features like auto-sharding and cross-region replication, and it provided Pushy with low latency and easy record expiry, both of which are critical for Pushy’s workload. As Pushy’s portfolio grew, we experienced some pain points with Dynomite.

Latency 234
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Optimizing data warehouse storage

The Netflix TechBlog

Some of the optimizations are prerequisites for a high-performance data warehouse. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster. Both automatic (event-driven) as well as manual (ad-hoc) optimization.

Storage 212
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

by Shefali Vyas Dalal AWS re:Invent is a couple weeks away and our engineers & leaders are thrilled to be in attendance yet again this year! Technology advancements in content creation and consumption have also increased its data footprint. Please stop by our “Living Room” for an opportunity to connect or reconnect with Netflixers.

AWS 38
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These 7 Edge Data Challenges Will Test Companies the Most in 2025

VoltDB

Edge computing has transformed how businesses and industries process and manage data. By bringing computation closer to the data source, edge-based deployments reduce latency, enhance real-time capabilities, and optimize network bandwidth. As data streams grow in complexity, processing efficiency can decline.

IoT 52
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

It also improves the engineering productivity by simplifying the existing pipelines and unlocking the new patterns. Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational.