Remove Data Engineering Remove Latency Remove Processing
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Introducing Impressions at Netflix

The Netflix TechBlog

It requires a state-of-the-art system that can track and process these impressions while maintaining a detailed history of each profiles exposure. This nuanced integration of data and technology empowers us to offer bespoke content recommendations. This queue ensures we are consistently capturing raw events from our global userbase.

Tuning 165
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

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

The Netflix TechBlog

The voice service then constructs a message for the device and places it on the message queue, which is then processed and sent to Pushy to deliver to the device. The previous version of the message processor was a Mantis stream-processing job that processed messages from the message queue.

Latency 230
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Optimizing data warehouse storage

The Netflix TechBlog

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Some of the optimizations are prerequisites for a high-performance data warehouse.

Storage 208
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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. Leverage tiered storage systems that dynamically offload data based on priority.

IoT 52
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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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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Data: Fast Data Our main data lake is hosted on S3, organized as Apache Iceberg tables. For ETL and other heavy lifting of data, we mainly rely on Apache Spark. In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training.

Systems 231