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Performance Improvements for Stateful Pipelines in Apache Spark Structured Streaming

databricks

Introduction Apache SparkStructured Streaming is a popular open-source stream processing platform that provides scalability and fault tolerance, built on top of the S.

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A Deep Dive into the Latest Performance Improvements of Stateful Pipelines in Apache Spark Structured Streaming

databricks

This post is the second part of our two-part series on the latest performance improvements of stateful pipelines. The first part of this.

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Mitigating Redundant UDF Computations in Spark Plans

Towards AI

Photo by Samuel Sianipar on Unsplash Originally published on my blog. It’s not uncommon to be caught up in long debugging cycles when working with Spark. I was recently caught in such a debugging train when one of my pipelines was taking longer than expected. When processing big data, efficiency is key.

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How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

To generate value from your model, it should make many predictions, and these predictions should improve a product or lead to better decisions. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?