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Awesome List
A curated list of data engineering tools for software developers
GitHub stars and default-branch commits for igorbarinov/awesome-data-engineering.
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Apache Superset is a Data Visualization and Data Exploration Platform
The Prometheus monitoring system and time series database.
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
The easy-to-use open source Business Intelligence and Embedded Analytics tool that lets everyone work with data :bar_chart:
Event Driven Orchestration & Scheduling Platform for Mission Critical Applications
Data Apps & Dashboards for Python. No JavaScript Required.
An orchestration platform for the development, production, and observation of data assets.
Apache Druid: a high performance real-time analytics database.
Incremental engine for long horizon agents 🌟 Star if you like it!
lakeFS - Data version control for your data lake | Git for data
Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
Python helpers for building dashboards using Flask and React
🔥🔥🔥 Open source Reverse ETL - alternative to hightouch and census.
Open-source ETL/ELT you deploy on your own servers or cloud. Built on DuckDB: no-code/low-code visual pipelines or SQL, 385 components, dbt, CDC, data quality, reverse ETL, lineage, MCP for AI agents. No vendor cloud, no per-row billing.
Open-source backend for Kepler.gl. Self-hosted alternative to CARTO and Foursquare Studio.
Accumulo backed time series database
No description.
One database for user-user (follows), user-item (likes), and item-item (related items) interactions — precomputed at write time, served as simple lookups
Data Quality and Observability platform for the whole data lifecycle, from profiling new data sources to full automation with Data Observability. Configure data quality checks from the UI or in YAML files, let DQOps run the data quality checks daily to detect data quality issues.
Turn CSV, Excel, and read-only databases into verified, editable business reports.
Trust metadata for AI agents — a stamp costs ~15 tokens, re-verifying costs 15,000. Agents stamp what they verify; the next agent runs 'akf check' and builds on it. pip install akf
One interface for your whole data stack, with built-in safeguards and a simpler workflow for your team. Written in Rust. Docker not needed.