Serverless distributed processing with BigFrames

Exciting news from Google Cloud with the launch of BigFrames (in preview). 🚀🚀🚀

This new library has significant potential to streamline processes that were traditionally managed by more intricate technologies like Apache Beam (Dataflow) or Spark. It also fills the gap between local Pandas operations running on Jupyter and deploying large-scale workloads in production, and enables faster interactive development at scale.

By harnessing the power of BigQuery's serverless compute and utilising Remote Functions on Cloud Run / Cloud Functions, it offers a more straightforward and modular approach to handling tasks before diving into machine learning workloads.

#GoogleCloud #BigFrames #RemoteFunctions #DataProcessing #MachineLearning #DistributedCompute #Orchestration

00:50 - What problem are we trying to solve
03:27 - What is BigFrames
04:58 - How does it work
09:10 - Why should we care
11:23 - Demo in Action
23:17 - Pros & cons plus ideas
To slide: https://docs.google.com/presentation/d/1IOacAdMqgWh45tIEdiQDvFI4DcKBqHh3c07awV33eFY/edit#slide=id.g62fc528f49_1_72
To repo: https://github.com/rocketechgroup/bigframes/tree/main

Видео Serverless distributed processing with BigFrames канала PracticalGCP