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#

DataOps

DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.

Here are 235 public repositories matching this topic...

Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..

  • Updated Mar 14, 2026
  • Rust

An open-source data logging library for machine learning models and data pipelines. Provides visibility into data quality & model performance over time. Supports privacy-preserving data collection, ensuring safety & robustness.

  • Updated Jan 10, 2025
  • Jupyter Notebook

The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium features.

  • Updated Mar 15, 2026
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Website
github.com/topics/dataops
Wikipedia
Wikipedia

Related topics

open-data