Apache beam and google cloud dataflow
summary
Overview of Apache Beam and Google Cloud Dataflow, explaining how to build and run data-processing pipelines using Beam’s unified programming model and execute them on Google Cloud Dataflow as a runner for scalable, managed processing.
AI-written summary · about
chapters (6)
- 0:00Start
- 3:22What is apache beam Unified APs that encapsulates the entire capabilities of data processing regardless the runner that preform the computation
- 4:27Why use apache beam? Apache beam is very simple and intuitive It allows you to replace the data processing from one to another It allows you to move from on premise to the cloud and vice
- 12:58Apache beam programming model Apache beam programming model includes: pipeline Pcollection, transformers and source & Sync
- 13:28Pipeline When you write a program in apache beam you write pipeline.
- 15:31Transformers Transformers are the business logic of the processing we have root transformers, core transformers and composite transformers
related talks
39mApache Beam and Google Cloud DataFlow
26mBeam and dataflow: a perfect combination of parallel processing
52mApache Beam, a unified model for Big Data processing
10mDemocratizing the pipeline with Apache Beam and Dataflow
47mApache Beam - what do I gain?
41mApache Beam: Unified Data Processing
53mStreaming data processing pipelines with Apache Beam [in Python, naturally!]
46mBuilding an Apache Beam streaming runtime in GCP
