data build tool in Cloud(dbt Cloud)

Explore how dbt enables data engineers to transform data already loaded in the data warehouse using select statements and SQL. Apply SQL proficiency and Git basics as prerequisites.

Learn how to create a dbt Cloud account, set up a new project and schema, configure a database connection, and create and clone a private Git repository with deploy keys.

Models function as transformation scripts by defining select statements to transform data in dbt Cloud. Executing a model runs its statements and creates a table or view in data warehouse.

Create your first model using the provided example, run two models, and verify results in the Learn Liberty schema with a table and a view.

Create and reference dbt models to transform stock index data, join it with stock info using a macro, and verify schema and data loading in dbt Cloud.

Explore how lineage visualizes data pipelines linking stock index data to stock index ref, compile models, validate data quality, and run dbt models with logs to monitor execution.

Explore materializations in dbt Cloud: table, view, incremental, and ephemeral, and learn when each creates objects, refresh behavior, and suitability for simple versus complex transformations.

Explore how models default to views in the database and how materialized settings switch them to tables, showing view refresh and automatic table creation.

Override default properties with macros to customize a view model. Create a stock index view with materialized set to view and alias stock_index_vw, then deploy and verify.

Learn to create an incremental model in dbt Cloud, using macros to load new data and update existing stock index records without duplicates.

explore ephemeral models in dbt cloud, using materialized as ephemeral to define logic that extracts data and references one model from another without creating physical tables or views.

Define and run tests for the stock index model in a dbt cloud project, covering unique, not null, accepted values, and relationships; validate with test files and logs.

Explore defining and using variables in dbt cloud to replace hardcoded values, set global or local scope, reference by name in models, and dynamically influence queries like region and exchanges.

define sources in dbt cloud by creating a stock_market source from the public schema with stock_index and stock_info tables, then reference it in a model and run source-level tests.

Learn how seeds, as csb files in dbt cloud, provide static data that remains unchanged, offering an alternative to models; execute seeds to create tables in your database.

Learn to create and use seeds in dbt cloud by loading stock data into seed files, compiling and running models, and validating seed integration with the model.

Learn how snapshots monitor changes in a data warehouse using two methods: time stamp strategy and check strategy, with an updated field and columns to compare current and historical values.

Explore how to create and manage snapshots in dbt cloud, using time-stamp and check strategies, configure a postgres target database and schema, and track updates with unique keys.

Explore hooks as pre and post database commands executed within data pipelines, defined in the model as seed or snapshot, with implementation details to follow.

Create and run a dbt cloud model for a stock index, configuring hooks for testing and inserting data, then verify completion with pre- and post-execution steps and new entries.

Explore Ginger templating in dbt Cloud, using curly braces for expressions, statements, and comments, and implement control structures to extend operations beyond simple select statements.

Learn to generate sql with jinja in dbt cloud, joining stock index with stock info, using for loops, case statements, and if conditions to create currency and volume columns.

The lecture introduces macros as functions for dbt cloud projects, stored in a macros directory and packaged for use; the next lecture shows how to create a macro.

Define and test a macro under macros, creating a get_currency function to output a list of currencies. Build a model and write a select statement to verify the query results.

Learn to deploy and run dbt cloud projects in production by creating deployment environments, configuring branches and credentials, and executing jobs with seeds, snapshots, and tracking data lineage.