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Secrets Detection: Optimizing Filter Processes

DZone

While increasing both the precision and the recall of our secrets detection engine, we felt the need to keep a close eye on speed. In a gearbox, if you want to increase torque, you need to decrease speed. So it wasn’t a surprise to find that our engine had the same problem: more power, less speed.

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Reimagining Experimentation Analysis at Netflix

The Netflix TechBlog

You can look at ABlaze (our centralized A/B testing platform) and take a quick look at how it’s performing. Note that the new encodes perform well in the lower quantiles but worse in the higher ones You notice that the first new encode (Cell 2?—?Encode This enables us to optimize their experience at speed.

Metrics 215
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

These challenges are currently addressed in suboptimal and less cost efficient ways by individual local teams to fulfill the needs, such as Lookback: This is a generic and simple approach that data engineers use to solve the data accuracy problem. Users configure the workflow to read the data in a window (e.g.

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How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

The Netflix TechBlog

As a micro-service owner, a Netflix engineer is responsible for its innovation as well as its operation, which includes making sure the service is reliable, secure, efficient and performant. In the Performance space, our data teams currently focus on the quality of experience on Netflix-enabled devices.

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Hyper Scale VPC Flow Logs enrichment to provide Network Insight

The Netflix TechBlog

Spark could look up and retrieve the data in the s3 files that the Mouthful represented. This intermediate step of persisting Mouthfuls allowed us to easily “eat” through S3 event SQS messages at great speed, converting them to far fewer Mouthful SQS Messages which would each be consumed by a single Spark app instance.

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

All Things Distributed

The data infrastructure to collect, store, and process data is geared primarily towards developers and IT professionals whereas insights need to be derived by not just technical professionals but also non-technical business users. Auto-discovery : One of the challenges with BI is discovering and accessing the data.

Analytics 152
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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

Unified data management architecture. A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. In contrast, Alluxio a middleware for data access - think Alluxio storage layer as fast cache.