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Learn about Beam Get Started Documentation Toggle navigation Toggle navigation About Get Started Documentation General Languages Runners I/O Connectors Roadmap Community Contribute Blog Case Studies Apache ASF Homepage License Security Thanks Sponsorship Code of Conduct About Get Started Documentation General Languages Runners I/O Connectors Roadmap Community Contribute Blog Case Studies Apache ASF Homepage License Security Thanks Sponsorship Code of Conduct Documentation Using the Documentation Concepts Basics of the Beam model How Beam executes a pipeline Beam programming guide Overview Pipelines PCollections Creating a PCollection PCollection characteristics Transforms Applying transforms Core Beam transforms ParDo GroupByKey CoGroupByKey Combine Flatten Partition Requirements for user code Side inputs Additional outputs Composite transforms Pipeline I/O Using I/O transforms I/O connectors Managed I/O I/O connector guides Apache Iceberg Apache Parquet Hadoop Input/Output Format HCatalog IO Google BigQuery Snowflake CDAP I/O Spark Receiver I/O SingleStoreDB I/O Web APIs I/O Developing new I/O connectors Overview: Developing connectors Developing connectors (Java) Developing connectors (Python) I/O Standards Testing I/O transforms Schemas What is a schema Schemas for programming language types Schema definition Logical types Creating schemas Using schemas Data encoding and type safety Data encoding basics Specifying coders Default coders and the CoderRegistry Windowing Windowing basics Provided windowing functions Setting your PCollection’s windowing function Watermarks and late data Adding timestamps to a PCollection’s elements Triggers Trigger basics Event time triggers and the default trigger Processing time triggers Data-driven triggers Setting a trigger Composite triggers Metrics Metrics basics Types of metrics Querying metrics Using metrics in pipeline Export metrics State and Timers Types of state Deferred state reads Timers Garbage collecting state State and timers examples Splittable DoFns Basics Sizing and progress User-initiated checkpoint Runner initiated split Watermark estimation Truncating during drain Bundle finalization Multi-language Pipelines Creating cross-language transforms Using cross-language transforms Runner Support Batched DoFns Transform service Pipeline development lifecycle Design Your Pipeline Create Your Pipeline Test Your Pipeline Common pipeline patterns Overview File processing Side inputs Pipeline options Custom I/O Custom windows BigQueryIO AI Platform Schema BigQuery ML Grouping elements for efficient external service calls Rate limiting DoFns and Transforms Cache using a shared object AI/ML pipelines Get started with AI/ML About Beam ML Prediction and inference Overview Build a pipeline with multiple models Build a custom model handler with TensorRT Use LLM inference Build a multi-language inference pipeline Update your model in production Data processing Preprocess data Explore your data Workflow orchestration Use ML-OPS workflow orchestrators Model training Per-entity training Online clustering ML model evaluation ML Dependency Extras Use cases Build an anomaly detection pipeline Reference RunInference metrics Model validation Runtime systems Container environments Resource hints SDK Harness Configuration Transform catalog Python Overview Element-wise Enrichment Overview Bigtable example Milvus example CloudSQL example Vertex AI Feature Store examples Filter FlatMap Keys KvSwap Map MLTransform ParDo Partition Regex Reify RunInference Overview PyTorch examples Sklearn examples ToString Values WithTimestamps Aggregation ApproximateQuantiles ApproximateUnique BatchElements CoGroupByKey CombineGlobally CombinePerKey CombineValues Count Distinct GroupBy GroupByKey GroupIntoBatches Latest Max
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