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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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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 226
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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 203
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Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

Since memory management is not something one usually associates with classification problems, this blog focuses on formulating the problem as an ML problem and the data engineering that goes along with it. We now explore each of these components individually, while highlighting the nuances of the data pipeline and pre-processing.

Big Data 179
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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

In this way, no human intervention is required in the remediation process. We used feature hashing to process the non-numeric values because they come from a high cardinality and dynamic set of values. Upon further profiling, we found that most of the latency came from the candidate generated step (i.e., user name).

Tuning 210
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Netflix at AWS re:Invent 2019

The Netflix TechBlog

This entertaining romp through the tech stack serves as an introduction to how we think about and design systems, the Netflix approach to operational challenges, and how other organizations can apply our thought processes and technologies. We explore all the systems necessary to make and stream content from Netflix.

AWS 100