Skip to main content

Data observability for Databricks — freshness, volume, and schema-change monitoring

Project description

DashObserve — Databricks Library

CI PyPI License

Part of the Dashlibs suite — Databricks libraries built for business users.

Monte Carlo-style data observability that runs natively in a Databricks notebook — no external service, no agent to deploy. v1 covers:

  • Freshness monitoring — alert when a table's most recent timestamp value is older than expected
  • Volume monitoring — alert on row-count bounds, or on deviation from a rolling historical baseline
  • Schema-change detection — alert when columns are added, removed, or change type since the last run

All monitor results are appended to a Delta history table, which also feeds the volume baseline and schema-diff comparisons for future runs.

Installation

%pip install dash-observe

Quick Start

import dashobserve
dashobserve.launch()   # Opens interactive UI in your Databricks notebook

What it looks like

DashObserve UI

Python API

from dashobserve import MonitorConfig, run_monitors

cfg = MonitorConfig(
    table="catalog.schema.orders",
    freshness_column="updated_at", max_staleness_minutes=60,
    min_rows=1000, volume_tolerance_pct=20,
    track_schema=True,
)
report = run_monitors(cfg, history_table="catalog.schema.observe_history")
report.display()
print(report.summary())

Part of Dashlibs

Library Purpose
dash-dq Data Quality
dash-synthetic Synthetic Data Generation
dash-observe Data Observability (freshness, volume, schema)
dash-ml ML Model Monitoring
dash-ingest Data Ingestion
dash-gov Data Governance
dash-relate Ontology & Lineage for AI
dash-ui Shared UI components (PyPI: dash-uis)

Quality & Contributing

  • 25 unit tests, zero Spark dependency to run them — pytest tests/ -v (freshness/volume/schema-diff checks are pure Python and fully covered; only the Spark/Delta glue in runner.py needs a live cluster)
  • Lint-clean (ruff check dashobserve/), PEP 561 typed (py.typed)
  • Every change ships through a reviewed pull request; CI (lint → test on Python 3.9–3.12 → build) gates every PR and every release
  • See CONTRIBUTING.md for dev setup, CHANGELOG.md for release history, SECURITY.md to report a vulnerability, and CODE_OF_CONDUCT.md

License

Apache 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dash_observe-0.1.2.tar.gz (110.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dash_observe-0.1.2-py3-none-any.whl (12.4 kB view details)

Uploaded Python 3

File details

Details for the file dash_observe-0.1.2.tar.gz.

File metadata

  • Download URL: dash_observe-0.1.2.tar.gz
  • Upload date:
  • Size: 110.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for dash_observe-0.1.2.tar.gz
Algorithm Hash digest
SHA256 d3ce2faf5d5795a1ff74a551ebe7f1bf28a91d65c3b7cba634c676757d64e2a8
MD5 1c3d138a713aec3ca02942efef601791
BLAKE2b-256 23d024f4c15a045af7e2b43b6226ff8a78492405fe454d9eedb79d25e1b3ab59

See more details on using hashes here.

Provenance

The following attestation bundles were made for dash_observe-0.1.2.tar.gz:

Publisher: release.yml on dash-libs/dash-observe

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file dash_observe-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: dash_observe-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 12.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for dash_observe-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e8e89b3ef17c3d97a9761d6446078ed187027147d162faadc231d39eeac219a0
MD5 bc1bcf5b474fc5faefe84fac617123e2
BLAKE2b-256 a2301b43d0b7c28f925f34f11e60a5959f2ca9c62e2315d43038e88c97697d44

See more details on using hashes here.

Provenance

The following attestation bundles were made for dash_observe-0.1.2-py3-none-any.whl:

Publisher: release.yml on dash-libs/dash-observe

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page