A Java framework for creating real-time time series aggregations from Amazon Kinesis streams.
Amazon Kinesis Aggregators is a Java framework that automatically creates real-time time series aggregations from data streaming through Amazon Kinesis. It solves the problem of manually building complex aggregation logic by allowing developers to simply configure the data format, time granularity, and grouping criteria, while the framework handles all calculations and persistence.
Java developers and data engineers working with Amazon Kinesis streams who need to generate real-time aggregated metrics and time series data without building custom aggregation pipelines.
Developers choose this framework because it significantly reduces the complexity of implementing real-time aggregations, provides built-in integration with AWS services like DynamoDB and CloudWatch, and offers extensibility for custom data formats and storage backends.
Amazon Kinesis Aggregators provides a simple way to create real time aggregations of data on Amazon Kinesis.
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Supports CSV, JSON, regex-extracted strings, and object-serialized data, allowing aggregation from diverse stream sources without custom parsing, as detailed in the configuration options.
Built-in persistence to DynamoDB and metrics emission to CloudWatch, streamlining setup for AWS-centric applications and enabling real-time dashboards.
Allows custom implementations for data extraction, data stores, and metrics emitters, as described in the 'Extending Aggregators' section, facilitating adaptation to specific needs.
Aggregates data by second, minute, hour, day, month, year, or custom grouped periods, enabling precise time series analysis with minimal code.
The framework is no longer actively developed, with only security updates, making it unsuitable for new systems that require long-term support and improvements.
Heavily reliant on AWS services like Kinesis, DynamoDB, and CloudWatch, limiting portability and increasing dependency on a single cloud provider.
Requires detailed JSON configuration files and understanding of AWS deployment options, such as Elastic Beanstalk setup, which can be cumbersome compared to simpler tools.