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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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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.
Open-source backend for Kepler.gl. Self-hosted alternative to CARTO and Foursquare Studio.
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.