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Modern Data Engineering with MAGE: Empowering Efficient Data Processing

Analytics Vidhya

Introduction In today’s data-driven world, organizations across industries are dealing with massive volumes of data, complex pipelines, and the need for efficient data processing.

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Arbitrary stateful processing in PySpark with applyInPandasWithState


you can even write arbitrary stateful processing jobs! It's always a huge pleasure to see the PySpark API covering more and more Scala API features. Starting from Apache Spark 3.4.0 But since the API is a little bit different than the one available on the Scala side, I wanted to take a deeper look.

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Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

Data Engineering Podcast

Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. Decodable was built with a mission of eliminating all of the painful aspects of developing and deploying stream processing systems for engineering teams. Check out the agenda and register today at

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7 Steps to Mastering Natural Language Processing


Want to learn all about Natural Language Processing (NLP)? Here is a 7 step guide to help you go from the fundamentals of machine learning and Python to Transformers, recent advances in NLP, and beyond.

Process 119
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LLMs in Production: Tooling, Process, and Team Structure

Speaker: Dr. Greg Loughnane and Chris Alexiuk

Greg Loughnane and Chris Alexiuk in this exciting webinar to learn all about: How to design and implement production-ready systems with guardrails, active monitoring of key evaluation metrics beyond latency and token count, managing prompts, and understanding the process for continuous improvement Best practices for setting up the proper mix of open- (..)

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Revolutionizing Real-Time Streaming Processing: 4 Trillion Events Daily at LinkedIn

LinkedIn Engineering

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

Process 122
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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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