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Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and…

The Netflix TechBlog

Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can

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Reimagining Experimentation Analysis at Netflix

The Netflix TechBlog

ABlaze: The standard view of analyses in the XP UI Suppose you’re running a new video encoding test and theorize that the two new encodes should reduce play delay, a metric describing how long it takes for a video to play after you press the start button. Our data scientists faced numerous challenges in our previous infrastructure.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

For example, a job would reprocess aggregates for the past 3 days because it assumes that there would be late arriving data, but data prior to 3 days isn’t worth the cost of reprocessing. Backfill: Backfilling datasets is a common operation in big data processing. append, overwrite, etc.).

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

All Things Distributed

The reality is that many traditional BI solutions are built on top of legacy desktop and on-premises architectures that are decades old. They require teams of data engineers to spend months building complex data models and synthesizing the data before they can generate their first report. Enter Amazon QuickSight.

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