Master Data Quality in dbt: Tests & Best Practices Explained! скачать в хорошем качестве

Master Data Quality in dbt: Tests & Best Practices Explained!

Ship trustworthy data with dbt. In this video, we turn data testing from an afterthought into a core part of your analytics workflow so broken data never reaches your end users. You’ll learn how dbt handle's testing, how to use schema (generic) tests like not_null, unique, accepted_values, and relationships, when to write singular (SQL) tests, how unit tests validate SQL logic, and how to set up source freshness + Cloud alerts so you catch issues before stakeholders do. WHAT YOU'LL LEARN Why data quality fails (and how dbt prevents “garbage in → garbage out”) How dbt tests work (pass/fail logic that halts pipelines on bad data) Built-in schema tests: not_null, unique, accepted_values, relationships Singular tests for one-off business rules (SQL in /tests) Unit tests vs data tests (dev/CI vs production) Source freshness checks (warn/error windows) dbt Cloud scheduling & Slack/email alerts Useful add-ons: dbt-expectations, Elementary for test coverage & observability WHO IS THIS FOR? Analytics engineers, data analysts, and data teams using dbt who want higher data trust, faster incident detection, and fewer “the dashboard looks wrong” messages. If this helped, hit 👍, subscribe, and drop your testing tips in the comments! #dbt #AnalyticsEngineering #DataQuality #DataTesting #dbtCloud #ModernDataStack Chapters (Timestamps) 00:00 – Why data quality matters (56% still struggle) 01:30 – How dbt tests work (pass/fail logic) 02:56 – Types of tests in dbt (overview) 03:11 – Source freshness tests (stale data alerts) 04:34 – Source freshness + Data tests intro 05:59 – Generic vs singular tests 06:17 – Built-in generic tests: not_null, unique, accepted_values, relationships 07:47 – Singular tests (one-off SQL checks) 08:28 – Unit tests vs data tests (dev/CI vs prod) 09:33 – When to use each: a simple testing plan 11:06 – dbt Cloud scheduling & notifications (Slack/email) 11:42 – Packages: dbt-expectations & Elementary 12:28 – dbt artifacts & observability dashboards 12:53 – Final takeaways: build data trust, one brick at a time 13:13 – Outro + like/subscribe/notifications