https://github.com/data-drift/data-drift

Metrics Observability & Troubleshooting
https://github.com/data-drift/data-drift

analytics bigquery context data-diffing data-governance data-lineage data-monitoring data-observability data-quality data-reliability data-version-control dbt dbt-metrics dbt-packages drill-down metrics reconciliation redshift semantic-layer snowflake

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Metrics Observability & Troubleshooting

• Host: GitHub
• URL: https://github.com/data-drift/data-drift
• Owner: data-drift
• License: gpl-3.0
• Created: 2023-04-21T07:59:16.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-29T14:45:51.000Z (8 months ago)
• Last Synced: 2024-04-29T16:41:24.407Z (6 months ago)
• Topics: analytics, bigquery, context, data-diffing, data-governance, data-lineage, data-monitoring, data-observability, data-quality, data-reliability, data-version-control, dbt, dbt-metrics, dbt-packages, drill-down, metrics, reconciliation, redshift, semantic-layer, snowflake
• Language: HTML
• Homepage: https://www.data-drift.io/
• Size: 11.7 MB
• Stars: 299
• Watchers: 5
• Forks: 11
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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Metrics Observability & Troubleshooting


Datadrift is an open-source metric observability framework that helps data teams deliver trusted and reliable metrics.





  Website |

  Issues |

  Blog |

  Doc |

  Roadmap |

  Discord





  

    

  



Data monitoring tools fail by focusing on static tests (eg. null, unique, expected values) and metadata monitoring (eg. column-level).

Datadrift monitors your metrics, sends alerts when anomalies are detected and automates root cause analysis.

**Data teams detect and solve data issues faster with Datadrift's row-level monitoring & troubleshooting.**

# 🚀 Quickstart

## dbt integration

```

pip install driftdb

```

[Here is a quick demo](https://app.claap.io/sammyt/drift-db-demo-a18-c-ApwBh9kt4p-07oQMdsIzt_e).

For a step-by-step guide on the dbt installation, see the [docs](https://pypi.org/project/driftdb/).

## Python integration

Install the monitor in your pipeline.

```python

>>> from driftdb.connectors import LocalConnector

>>> LocalConnector().snapshot_table(table_dataframe=dataframe, table_name="revenue")

```

For a step-by-step guide on the python installation, see the [docs](https://pypi.org/project/driftdb/).

## Datadrift cloud

We are in development and we would love to do the installation with you. [Fill the form on our website](https://www.data-drift.io/) so we can do a 15min demo. If the tool solves your problem then the installation requires 30min.

# ⚡️ Key Features

## 🔮 Metrics monitoring & custom alerting

Get full visibility into metrics variation and pro-actively detect data quality issues. Become aware of unknown unknowns with metric drift custom alerting.

  

    

  

## 🧑‍🎤 Automated root cause analysis & troubleshooting

Operationalize your monitoring and solve your underlying data quality issue with lineage drill-down to understand the root cause of the problem.

  

    

  

## 💎 Shared understanding of metric variation

Give visibility to data analysts and data consumers with shared explanation of metric variation.

  

    

  

## 🧠 And much more

We are in the early days of Datadrift. Just open a new [issue](https://github.com/data-drift/data-drift/issues) to tell us more about it and see how we could help!

# 💚 Community

We 💚 contributions big and small. In priority order (although everything is appreciated) with the most helpful first:

- [Star this repo](https://github.com/data-drift/data-drift) to help us get visibility and build awesome open-source tools

- [Join our Discord server](https://discord.gg/X2RUXFAm) to be part of our thriving community

- [Open an issue](https://github.com/data-drift/data-drift/issues) to share your idea or a bug you might have spotted

- [Become a Design Partner](https://www.data-drift.io/design-partner) to co-built a product you & users love

# 🗓 Upcoming features

Track planning on [Github Projects](https://github.com/orgs/data-drift/projects/3) and help us prioritising by upvoting or creating [issues](https://github.com/data-drift/data-drift/issues).