
Set your own pace, pause to note questions, and Google concepts as you build problem-solving skills while following along with hands-on exercises and paradigm documentation.
This zip folder contains csv files that you need to setup to follow hands-on exercises.
Explore dbt, the data build tool, for ELT transformations in the data warehouse with SQL and Jinja templating, and compare dbt core and dbt cloud for collaboration and automation.
Explore the editor features in Paradime, including docs, the data explorer, and the lineage graph, to visualize upstream dependencies, annotate changes, and compare branches for safe dbt development.
Explore bolt, Paradigm's scheduler, to manage hourly and daily schedules, inspect failures, view logs, annotate issues, and notify colleagues, then explore the SQL area for data model insights.
Explore the SQL workbench in Paradigm to preview data from the fact orders table, run queries, and inspect decompiled and compiled SQL with the jaffle model.
Log in to paradigm, set up the workspace and region, and create a fresh private git repository. Connect deploy keys to link paradigm with BigQuery and start dbt core work.
Build a dimensional model with dbt and Paradigm, connecting data sources to a cloud data lake and staging layer to a dimensional data warehouse.
Build dbt models within a dimensional data warehouse using Paradigm, initialize a repository, configure BigQuery with OAuth, and push changes via git to the project dataset.
Commit dbt_project.yml changes by creating a feature branch, pushing, and merging to main via a pull request, validating a YAML update that includes staging and warehouse models.
Build warehouse layer by creating dim customer, dim date (as a view), and fact purchase order tables, using a source CTE and selecting a unique record from the customer source.
Commit code changes to reflect data lineage, view the fact purchase order lineage with source and staging tables, and annotate for team review and Slack collaboration.
Requirements
Basic understanding of SQL is required
Basic understanding dbt is required
Basic understanding of what Analytics Engineering is required (We suggest taking our Analytics Engineering Bootcamp course on Udemy)
Note
You'll need Paradime trial account to practice hands-on exercises
Description
Are you looking to take your data transformation skills to the next level? If so, you’re in the right place! Our course “Learn dbt-core using Paradime” is designed to help you build strong knowledge in dbt (data build tool) and Paradime, the modern operation system for analytics! Paradime is the new operating system for Analytics and this is the first ever course on Udemy.
In this course, you’ll learn how to leverage dbt-core & Paradime to transform raw data into valuable insights that drive informed business decisions. With hands-on exercises and real-world examples, you’ll gain practical experience in designing, building, and deploying data transformations using dbt and Paradime.
By the end of this course, you’ll have the skills and knowledge you need to:
Set up Paradime from scratch and configure git & data warehouse
Confidently use Paradime & dbt-core to build data pipelines that are scalable, maintainable, and easy to understand
Create data models that are intuitive, flexible, and optimized for performance using Paradime
Collaborate effectively with team members using version control and annotations
Analyze and interpret data to extract valuable insights and drive informed decision-making
Whether you’re a data engineer, data analyst, or data scientist, this course will give you the tools you need to take your data transformation skills to the next level. Start your journey to becoming a dbt and Paradime expert!
By enrolling in this course, you'll get:
Lifetime access to the course and all future updates
Free access to Paradime Platform for 6 months
1.5 hours of high quality, up to date video lectures
Practical dbt+Paradime course with step by step instructions
Who this course is for:
Users with some experience with SQL and Analytics Engineering
BI Analysts
Data Analysts
Analytics Engineers
Data Engineers
Data Scientists