Open-source alternatives to WaveStitch

Hadoop is a big data infrastructure suite and distributed data processing framework designed to store and process massive datasets across clusters of computers. It consists of a distributed storage system for managing large files across multiple nodes and a parallel computing engine for processing data across a distributed cluster. The framework implements a distributed file system to ensure fault tolerance and high throughput, paired with a programming model that processes large datasets in parallel. It manages the underlying hardware and software environment required for distributed big dat

Apache IoTDB is a time-series database designed for the Internet of Things, purpose-built to ingest high-volume data from millions of low-power devices and store timestamp-value pairs with configurable data types and encoding schemes. It organizes time series data and device metadata in a tree-like hierarchy, enabling efficient management of complex industrial sensor networks. The database supports rich querying capabilities, including time-aligned data retrieval across multiple devices, time-based aggregation like downsampling, and frequency-domain signal analysis. It provides high-throughpu

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

CMSIS-DSP is an optimized compute library for embedded systems (DSP is in the name for legacy reasons).

data compression library for embedded/real-time systems

Amazon Kinesis Aggregators provides a simple way to create real time aggregations of data on Amazon Kinesis.

Client library for Amazon Kinesis

This package provides an interface to the Amazon Kinesis Client Library (KCL) MultiLangDaemon for the .NET Framework.

Amazon Kinesis Client Library for Node.js

Amazon Kinesis Client Library for Python

A Ruby interface for the Amazon Kinesis Client Library. Allows developers to easily create robust application to process Amazon Kinesis streams in Ruby.

The Amazon Kinesis Connector Library helps Java developers integrate Amazon Kinesisaws-kinesis with other AWS and non-AWS services. The current version of the library provides connectors for Amazon DynamoDBaws-dynamodb, Amazon Redshiftaws-redshift, Amazon S3aws-s3,…

Amazon Kinesis Data Visualization Sample Application

Learning Amazon Kinesis Development

Amazon Kinesis Producer Library

The Kinesis Scaling Utility is designed to give you the ability to scale Amazon Kinesis Streams in the same way that you scale EC2 Auto Scaling groups – up or down by a count or as a percentage of the total fleet. You can also simply scale to an exact number of Shards. There is no requirement for you to manage the allocation of the keyspace to Shards when using this API, as it is done automatically.

Amazon Redshift Utils contains utilities, scripts and view which are useful in a Redshift environment

Amazon Kinesis output plugin for Fluentd

Amazon Redshift Database Loader implemented in AWS Lambda

The Amazon DynamoDB Streams Adapter implements the Amazon Kinesis interface so that your application can use KCL to consume and process data from a DynamoDB stream.

ARCHIVED: Log4J Appender for writing data into a Kinesis Stream

Simple multi-threaded Kinesis Poster and Worker Python examples

Kinesis spout for Storm