Raccogliamo qui tutti i link alle risorse menzionate durante i nostri QShare
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Updated
Apr 29, 2020
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.
Raccogliamo qui tutti i link alle risorse menzionate durante i nostri QShare
funsies is a lightweight workflow engine 🔧
ysv: clean and transform CSV data along your rules encoded in YAML format, lightning fast
Open source data infrastructure platform. Designed for developers, built for speed.
Data analytics library for Python and suite of open source, command line based data ops tools.
simple data platform to study which country indicators are relevant for olympics sports outcome
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Repositorch is a VCS repository analysis engine written in C#.
Polyglot workflows without leaving the comfort of your technology stack.
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A data lineage tool detects table dependencies from rendered SQL statements.
Manage Redshift/Postgres privileges in GitOps style written in Rust
Open Source Data Quality Monitoring.
Github Action enabling easy use of Snowflake CLI in your CI/CD workflows
Collect, aggregate, and visualize a data ecosystem's metadata