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Graph-Native Infrastructure for Context and Accountable AI Systems
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Explore 10 GitHub repositories focused on data-engineering. Discover top-starred projects and those trending this week.
Graph-Native Infrastructure for Context and Accountable AI Systems
Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. Join the course here 👇🏼
Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
Python Stream Processing
ktx is an executable context layer for data and analytics agents 🐙 Allow Claude Code, Codex, and any AI agent to query data accurately through MCP with skills, memory and a semantic layer
The typed graph between your code and whichever warehouse, table format, or query engine you've chosen — typed compiler, branches, replay, column-level lineage, compile-time contracts, per-model cost. Adapters: Databricks, Snowflake, BigQuery, DuckDB. Single static Rust binary. Apache 2.0.
SDK for robotics teams to verify the quality of their data used for AI model training.
Stop re-explaining your data to your AI every session. The individual-analyst context layer, delivered over MCP (Claude Code / Cursor / Codex).
A Pub/Sub for Tables based data integration platform, to discover, publish, modify and consume data effortlessly.
Metadata-driven framework for Databricks Spark Declarative Pipelines. Config-driven, pattern based approach to batch & streaming across the medallion architecture. Deploys via Declarative Automation Bundles. Built for simplicity, extensibility, and alignment with the Databricks product roadmap.
Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. Join the course here 👇🏼
Graph-Native Infrastructure for Context and Accountable AI Systems
Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
Python Stream Processing
ktx is an executable context layer for data and analytics agents 🐙 Allow Claude Code, Codex, and any AI agent to query data accurately through MCP with skills, memory and a semantic layer
The typed graph between your code and whichever warehouse, table format, or query engine you've chosen — typed compiler, branches, replay, column-level lineage, compile-time contracts, per-model cost. Adapters: Databricks, Snowflake, BigQuery, DuckDB. Single static Rust binary. Apache 2.0.
SDK for robotics teams to verify the quality of their data used for AI model training.
Stop re-explaining your data to your AI every session. The individual-analyst context layer, delivered over MCP (Claude Code / Cursor / Codex).
A Pub/Sub for Tables based data integration platform, to discover, publish, modify and consume data effortlessly.
Metadata-driven framework for Databricks Spark Declarative Pipelines. Config-driven, pattern based approach to batch & streaming across the medallion architecture. Deploys via Declarative Automation Bundles. Built for simplicity, extensibility, and alignment with the Databricks product roadmap.