Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
- batch-processing
- kafka
- pathway
- python
47,133 repositories indexed
1–16 of 16 · Most stars
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
Logstash - transport and process your logs, events, or other data
SeaTunnel is a multimodal, high-performance, distributed, massive data integration tool.
Fancy stream processing made operationally mundane
Privacy and Security focused Segment-alternative, in Golang and React
pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
Fast, Simple and a cost effective tool to replicate data from Postgres to Data Warehouses, Queues and Storage
The fastest unified streaming SQL Engine in a single C++ binary. ⚡ Millisecond latency. 100+ GB/s throughput. Continuously compute stateful context from streams, logs, metrics, events, and CDC.
A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.
MongoDB data stream pipeline tools by YouGov (adopted from MongoDB)
Python Streaming DataFrames for Kafka
Apache InLong - a one-stop, full-scenario integration framework for massive data
High performance Rust stream processing engine seamlessly integrates AI capabilities, providing powerful real-time data processing and intelligent analysis.
omniparser: a native Golang ETL streaming parser and transform library for CSV, JSON, XML, EDI, text, etc.
Conduit streams data between data stores. Kafka Connect replacement. No JVM required.
An extensible Java framework for building event-driven applications that break up XML and non-XML data into chunks for data integration