DBT REDSHIFT MATERIALIZED VIEW INSTALL
DBT REDSHIFT MATERIALIZED VIEW MANUAL

DBT REDSHIFT MATERIALIZED VIEW MANUAL

Seeds are a convenient way in dbt for you to manage manual files. However the process of running macro macro_manage_users_grants can be automated by hooks in dbt. This introduces the operational challenge of you having to remember to run macro macro_manage_users_grants. Regranting access to an existing view is required as dbt updates by dropping existing view and creating a new view. Lab 9: dbt hooksĪs models are added or updated, you will constantly need to grant access to new views and regrant access to existing views. For example, you might want to include a new macro to manage Role-based access control (RBAC) in Amazon Redshift. Similar to the Python script earlier, you can modify the macros created in this lab to better suit your needs beyond the scope of this workshop. In this lab, you will use macros to manage users and grants. Macros can be used to send queries to Amazon Redshift. Lab 8: Access Control Management with dbt macros In this lab, you will simulate a Technology department with one model that contains data masking logic. Macros are a great way in dbt to create reusable pieces of SQL codes like a function in Python. In this lab, you will use macros to create a piece of reusable data transformation logic and also to manage users and grants. Model 2 - Quarterly Top Events By Sales (references Model 1 to rank and filter for top 3 events by sales for each quarter).Model 1 - Quarterly Total Sales By Event.To reference means to reuse and not have to duplicate code. To explore dbt's ability for objects to be referenced by other objects, in this lab, you will simulate a Finance deparment that maintains two models where the second model references the first model. For instance, a data engineer's base layer likely relates to tables containing raw data while a data analyst's base layer likely relates to tables containing cleaned data. In addition, depending on the nature of your dbt project, a base layer can be made up of different objects. Base layer falls under the category of models and represents existing objects (tables and views) in your Amazon Redshift cluster. What is a base layer and what are models? Models are SQL Select statements that represents your data transformation logic including usage of case statements and joins. In this lab, you will setup a base layer for your models to reference.

DBT REDSHIFT MATERIALIZED VIEW INSTALL

In this lab, we will install dbt in our system. In this lab, we are going to ingest the TICKIT dataset into the Redshift warehouse Lab 3: Setup dbt Use AWS Secret Manager to store and retrieve our Warehouse credentials.Connect to the Cluster from our local computer using Python.

Create Amazon Redshift Serverless Cluster on AWS.

You should have basic understanding of the following concepts: You should have basic understanding of the following tools:

Use dbt experimental package explore materialization of materialize views.

Use dbt docs to generate documentation with visualization.

Use dbt hooks to automate continuous execution of grants.

Use dbt macros to maintain common logic as functions and to administer user creation and grants.

Use dbt models to maintain data transformations with referencing capabilities.

Use dbt dbt-labs/codegen package and Python script to automate creation of a base layer.

They need your help in establishing the dbt models and pipelines in their warehouse. Building an ELT Pipeline with dbt and Amazon Redshift Business ProblemĪ fictional company 'Recohut' is looking to leverage dbt on their existing data warehouse in Amazon Redshift.

Dbt redshift materialized view