Data transformation process: 6 steps in an ELT workflow
The data transformation process can be broken down into six steps: extraction and loading, exploration, transformation, testing, documentation, and deployment.
Read articleTechnical Writer @ dbt Labs
The data transformation process can be broken down into six steps: extraction and loading, exploration, transformation, testing, documentation, and deployment.
Read articleFoundational data quality checks you should be running on your data cover the concepts of uniqueness, acceptance, referential integrity, and freshness.
Read articleData quality testing is the practice of using code-based and automated data quality tests to know whether these assertions you make about your data are valid.
Read articleData quality dimensions create a general profile of your data's quality. How you define what "good" data quality is depends on how that data is used.
Read articleThe main differences between ETL and ELT lie in when and where data transformation occurs—changes that might seem small but come with big implications!
Read articleA technical writing mentorship program for the dbt Community, sponsored by dbt Labs.
Read articleAsk anyone at dbt Labs, the most special week of the year for dbt is Coalesce. There’s so much work, anticipation, and anxiety that goes into Coalesce: Will talks resonate with the Community? How can we ensure speakers feel confident? Will folks be excited about new product launches? How can we make the online and...
Read articleOctober 17-21, 2022: Coalesce is back and in-person! With 100+ speakers and sessions, 3 global locations and a virtual option, it’s a week you’re not going to want to miss. Find out what’s new and what to expect from the third year of the Analytics Engineering Conference.
Read articleThis piece is about how direct collaboration with engineers for event tracking is the best axe to wield in product analytics, and how our data team went about benchmarking IDE performance.
Read articleThis post is part of our “Orchestrating my day” series where data folks walk us through their daily experiences.
Read articleThe STAR dbt macro generates a a comma-separated list of all fields that exist in the `from` relation and excludes any fields listed in an `except` argument
Read articleThe DATE_TRUNC function will truncate a date or time to the first instance for a given date part maintaining a date format.
Read articleThe DATEDIFF function will return the difference in specified units (ex. days, weeks, years) between a start date/time and an end date/time. It’s a simple and widely used function that you’ll find yourself using more often than you expect.
Read articledbt Labs recent underwent a massive overhauling of our OKR framework. Erica Louie, Head of Data, shares how we think about OKRs and a solution to measuring them that works for us.
Read articleThe COALESCE SQL function is an incredibly useful function that allows you to fill in unhelpful blank values that may show up in your data.
Read articleDeep dive into the EXTRACT function, how it works, and why we use it. The EXTRACT function allows you to extract a specified date part from a date/time.
Read articleThe LOWER SQL Function allows you to return a string value as an all lowercase string. It’s an effective way to create consistent capitalization for string values across your data.
Read articleErica Louie, Head of Data at dbt Labs, walks us through her typical daily routine, challenges, and workflows.
Read articleThis playbook dives into how dbt Labs thinks and implements reverse ETL workflows for our internal analytics work.
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