amazon-kinesis-connectors
A Java library for building data pipelines that connect Amazon Kinesis streams to AWS and non-AWS services like DynamoDB, Redshift, S3, and Elasticsearch.
What is amazon-kinesis-connectors?
Amazon Kinesis Connector Library is a Java framework that facilitates building data pipelines to process and route records from Amazon Kinesis streams to other AWS services (like DynamoDB, Redshift, S3) and non-AWS services (like Elasticsearch). It solves the problem of integrating real-time streaming data with various storage and analytics backends by providing a modular, configurable pipeline architecture.
Java developers and data engineers working with Amazon Kinesis who need to implement reliable, scalable stream processing applications that export data to multiple destinations.
Developers choose this library because it offers pre-built, production-ready connectors for common AWS services, reduces boilerplate code for stream processing, and provides a flexible pipeline model that supports custom transformations and batching logic.
Overview
The Amazon Kinesis Connector Library is a Java framework that simplifies the integration of Amazon Kinesis data streams with various storage and analytics services. It provides a structured pipeline for processing, transforming, and emitting streaming data to destinations such as DynamoDB, Redshift, S3, and Elasticsearch, enabling real-time data workflows.
Key Features
- Modular Pipeline — Implements interfaces for transformation, filtering, buffering, and emission to define custom data flows.
- Pre-built Connectors — Includes ready-to-use connectors for AWS DynamoDB, Redshift, S3, and Elasticsearch.
- Batch Processing — Buffers records based on configurable thresholds (count, size, time) for efficient batch writes.
- Custom Transformations — Supports user-defined data models and serializers via the ITransformer interface.
- Sample Implementations — Provides complete sample applications with Ant/Maven build files for each connector type.
Philosophy
The library emphasizes a decoupled, extensible architecture where developers can plug in custom logic for each stage of the data pipeline, promoting flexibility and reuse in stream processing applications.
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