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  • Datafold
  • Introduction
    • Data Diff
      • Continuous Integration
      • Manual Data Diff
      • Diff Results
    • Column-level lineage
      • Usage, popularity, & impact per table or column
    • Alerting
  • ⏱️Quickstart Guide
  • Getting Started
    • Data Warehouses
      • Snowflake
      • BigQuery
      • Redshift
      • Postgres
      • Databricks
    • Configuration
      • Indexing
      • Filtering
      • Profiling
      • Lineage
    • On-prem Deployment
      • AWS
      • GCP
    • SSO
      • Okta
      • Google OAuth
      • SAML
  • Integrations
    • Continuous Integration
      • Source Control with Git
        • GitHub
          • On-prem Github
        • GitLab
      • dbt Cloud
      • dbt Core / datafold-sdk
        • GitHub example
        • GitLab example
      • dbt Configurations
      • datafold-sdk
    • Alert Integrations
      • Slack integration
        • Slack Alerts
        • On-prem Slack Integration
      • Alerting webhooks
    • Data Apps
      • Mode
      • Hightouch
  • Developer
    • Datafold API
      • Alerting
      • GraphQL Metadata API
      • Data Diff
      • Error handling
    • Security
      • GDPR
      • Network Security
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Introduction

Setting up Pro-Active Data Quality using Datafold

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Last updated 2 years ago

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Getting started with Datafold is accommodated in three parts:

  • makes sure that you're in control of the changes to the data pipeline. Don't get fooled by an erroneous transformation, or an unexpected change downstream.

  • gives detailed insight into how the data streams throughout the pipeline, and where reports source their data from.

  • makes sure that you get notified when something unexpected happens with the data that you base your business-critical decisions on.

The getting started guides are based on repository.

Data Diff
Data catalog with column-level lineage
Alerting
Datafolds' public dbt-beers