Programming model for Apache Beam | Cloud Dataflow Google Cloud Platform (GCP) offered a data processing model called "Dataflow," which is based on the Apache Beam programming model. Apache Beam is an open-source, unified model for both batch and stream data processing. Google Cloud Dataflow is Google's managed service for executing Apache Beam pipelines. - Google Cloud Platform Training in Hyderabad
Here are the key components and concepts related to the Beam programming model in GCP: 1. Pipeline: A data processing pipeline is created using the Apache Beam SDK. It represents the series of data transformations and computations you want to perform on your data. This can include both batch and stream processing. 2. PTransforms (Parallel Transforms): These are the building blocks of a Beam pipeline. They represent the data processing operations, like mapping, filtering, aggregating, and joining data. PTransforms take one or more PCollections as input and produce one or more PCollections as output. 3. PCollections (Parallel Collections): These represent the data in your pipeline. PCollections are created from data sources and are manipulated by PTransforms. Data can be in the form of bounded (batch) or unbounded (streaming) datasets. - Google Cloud Training Institute in Hyderabad 4. Sources and Sinks: Sources are connectors to input data, and sinks are connectors to output data. These can be various data storage or streaming systems, such as Google Cloud Storage, Google BigQuery, Pub/Sub, etc.