You now know what dbt (data build tool) is all about. You are being productive, but you forgot what `dbt build` does or you forgot what the @ dbt graph operator does. This handy dbt cheat sheet has it all in one place.
dbt cheat sheet - Updated for dbt 1.8
With the advent of dbt 1.6, we updated the awesome dbt cheat sheet created originally by Bruno de Lima
We have also moved the dbt jinja sheet sheet to a dedicated post.
This reference summarizes all the dbt commands you may need as you run your dbt jobs or study for your dbt certification.
If you ever wanted to know what the difference between +model and @model is in your dbt run, you will find the answer. Whether you are trying to understand dbt graph operators or what the dbt retry command does, but this cheat sheet has you covered. Check it out below.
Primary dbt commands
These are the principal commands you will use most frequently with dbt. Not all of these will be available on dbt Cloud
dbt Command arguments
The dbt commands above have options that allow you to select and exclude models as well as deferring to another environment like production instead of building dependent models for a given run. This table shows which options are available for each dbt command
dbt selectors
By combining the arguments above like "-s" with the options below, you can tell dbt which items you want to select or exclude. This can be a specific dbt model, everything in a specific folder, or now with the latest versions of dbt, the specific version of a model you are interested in.
dbt graph operators
dbt Graph Operator provide a powerful syntax that allow you to hone in on the specific items you want dbt to process.
Project level dbt commands
The following commands are used less frequently and perform actions like initializing a dbt project, installing dependencies, or validating that you can connect to your database.
dbt command line (CLI) flags
The flags below immediately follow the dbt command and go before the subcommand e.g. dbt <FLAG> run
Read the official dbt documentation
As a managed dbt Core solution, the Datacoves platform simplifies the dbt Core experience and retains its inherent flexibility. It effectively bridges the gap, capturing many benefits of dbt Cloud while mitigating the challenges tied to a pure dbt Core setup. See if Datacoves dbt pricing is right for your organization or visit our product page.
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FAQ
How do I run only a specific dbt model without running the whole project?
Use the --select or -s flag with the model name: dbt run --select my_model.
You can also target models
* by folder path (dbt run --s models/staging)
* by tag (dbt run --s tag:daily)
* by file path (dbt run --s path/to/some_model.sql)
To preview which models will be selected without running them, use dbt ls --select my_model first.
How do dbt seeds work and when should I use them?
dbt seed loads CSV files stored in your seeds/ folder into your data warehouse as tables. Seeds are best for small, static reference datasets. For example, country codes, territory mappings, or status lookup tables.Seeds are not suitable for large datasets. Run dbt seed to load them, and reference them in models using ref() just like any other model.Note that the CSV files for seeds are part of your dbt project and are committed to git, so you would not use a seed to load data that gets refreshed on a regular basis.
What does dbt retry do and when should I use it?
dbt retry re-executes the last dbt command starting from the point of failure. It reads run_results.json to determine which node failed and picks up from there. This is useful in CI/CD pipelines or long production runs where a transient error caused a mid-run failure and you do not want to rebuild models that already succeeded. Note, the run_results.json of the prior run must be availeble for dbt to know where to resume execution.
What does the + graph operator do in dbt?
The + operator selects upstream or downstream models relative to a target model. Placing + before a model name (e.g., +my_model) selects the model and all its upstream dependencies. Placing it after (e.g., my_model+) selects the model and all downstream dependents. You can use both (e.g., +my_model+) to select everything connected to a model in both directions.
What does the --full-refresh flag do and when is it needed?
The --full-refresh flag forces dbt to drop and recreate incremental models from scratch instead of appending or updating records. Use it when the logic of an incremental model has changed, when the schema has been altered, or when you need to reprocess historical data. Without it, incremental models only process new or updated records since the last run.
What is the @ graph operator and when should I use it?
The @ operator is like + but more thorough. It selects a model, all its downstream dependents, and all the upstream dependencies of those downstream models. Use it when you need to ensure the complete, self-contained subgraph is rebuilt. For example, when testing a change that affects multiple downstream pipelines and you want every dependency refreshed.
What is the difference between dbt Core and dbt Cloud, and where does Datacoves fit?
dbt Core is the open-source engine. It gives you full control but requires you to build and manage your own environment ‚ CI/CD, orchestration, secrets management, and infrastructure. dbt Cloud is a hosted SaaS platform from dbt Labs with a managed IDE and scheduler, but it limits flexibility, restricts private cloud deployment, and can be expensive at scale. Datacoves is a managed dbt Core alternative that delivers the usability of dbt Cloud without the trade-offs: it runs in your private cloud, integrates with Airflow, VS Code, and your existing tools, and includes pre-built CI/CD and best-practice guardrails out of the box.
What is the difference between dbt run and dbt build?
dbt run executes only your SQL models. dbt build does everything: it loads seeds, runs snapshots, executes models, and runs tests, as well as user defined functions(UDFs) in dbt 1.11+. For production pipelines and CI/CD, dbt build is the right choice. Use dbt run during active development when you want to iterate quickly on a single model without triggering the full test suite.
What is the difference between dbt test and dbt build when it comes to testing?
dbt test runs all tests defined in your project but does not execute models. dbt build runs models and immediately tests them after each node in DAG order. If a test fails on an upstream model, downstream models are skipped entirely. For development, dbt test gives you faster feedback on a specific model. For production, dbt build ensures tests gate downstream execution.
What is the purpose of dbt snapshot and when does it apply?
dbt snapshot captures the state of a source table at a point in time and tracks how it changes over time. It is similar to a Type 2 Slowly Changing Dimension (SCD2). It records when a record was added, modified, or removed. Use snapshots when you need historical tracking of records that get updated in place in the source system, such as customer status or order state changes.
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