Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.
- analytics
- bigquery
- data-modeling
- data-pipelines
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Utility to compare data between homogeneous or heterogeneous environments to ensure source and target tables match
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.
- data-ops
- data-quality
- data-quality-checks
- data-quality-measurement
Data validator agains Splunk Common Information Model (CIM)