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Incremental Processing using Netflix Maestro and Apache Iceberg

Netflix Tech

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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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

When Glue receives a trigger, it collects the data, transforms it using code that Glue generates automatically, and then loads it into Amazon S3 or Amazon Redshift. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog. For analyzing huge datasets, they want to employ familiar Python primitive types.

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Introducing Vector Search on Rockset: How to run semantic search with OpenAI and Rockset

Rockset

Under the hood, Rockset utilizes its Converged Index technology, which is optimized for metadata filtering, vector search and keyword search, supporting sub-second search, aggregations and joins at scale. Fast Search: Combine vector search and selective metadata filtering to deliver fast, efficient results.

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Evolution of ML Fact Store

Netflix Tech

An example of data about members is the video they had watched or added to their My List. An example of video data is video metadata, like the length of a video. These facts are managed and made available by services like viewing history or video metadata services outside of Axion. How do we monitor the quality of data?

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Keeping Small Queries Fast – Short query optimizations in Apache Impala

Cloudera

Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? It turns out that Apache Impala scales down with data just as well as it scales up. So clearly Impala is used extensively with datasets both small and large. Metadata Caching. Execution Engine.

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Data Preprocessing - Techniques, Concepts and Steps to Master

ProjectPro

Data preprocessing is a step that involves transforming raw data so that issues owing to the incompleteness, inconsistency, and/or lack of appropriate representation of trends are resolved so as to arrive at a dataset that is in an understandable format.

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How Airbnb Achieved Metric Consistency at Scale

Airbnb Tech

While we have previously shared how we ingest data into our data warehouse and how to enable users to conduct their own analyses with contextual data , we have not yet discussed the middle layer: how to properly model and transform data into accurate, analysis-ready datasets. Our work hardly stopped there, however.