Data professionals from all disciplines will benefit from this comprehensive introduction to the components of the Databricks Lakehouse Platform that directly support putting ETL pipelines into production. You’ll leverage SQL and Python to define and schedule pipelines that incrementally process new data from a variety of data sources to power analytic applications and dashboards in the lakehouse. This course offers hands-on instruction in Databricks Data Science and Engineering Workspace, Databricks SQL, Delta Live Tables, Databricks Repos, Databricks Task Orchestration and Unity Catalog.
This course will prepare you to take the Databricks Certified Data Engineer Associate exam.
Prerequisites
Participants should have:
- Beginner familiarity with basic cloud concepts (virtual machines, object storage, identity management).
- Ability to perform basic code development tasks (e.g., creating compute instances, running code in notebooks, using basic notebook operations, and importing repositories from Git).
- Intermediate familiarity with SQL, including commands such as CREATE, SELECT, INSERT, UPDATE, DELETE, GROUP BY, JOIN.
- Intermediate experience with SQL concepts such as aggregate functions, filters, sorting, indexes, tables, and views.
- Basic knowledge of Python programming, Jupyter Notebook interface, and PySpark fundamentals.
If you do not have one or more of the pre-requisites QA recommends:
- QATSQL - Querying SQL Databases
- QADHPYTHON - Data Handling with Python
Target Audience
This course is designed for:
- Data Engineers who want to enhance their knowledge of Databricks and Delta Lake.
- Data Analysts looking to expand their expertise in data pipelines and transformation.
- Cloud Engineers and Developers working with big data frameworks.
- Professionals preparing for the Databricks Associate Data Engineering certification.
