Get Started with Databricks for Data Warehousing

This course explores Databricks' Lakeflow Spark Declarative Pipelines (SDP) for building production-grade streaming pipelines. You will learn advanced design patterns, robust data quality enforcement, and cross-platform integration essential for real-world lakehouse engineering.

Throughout the course, you will dive into modern data ingestion and processing techniques, mastering tools like Liquid Clustering for layout optimization and the Multiplex Streaming pattern for mixed-schema events. By the end of the modules, you will know how to confidently handle schema evolution, automate Change Data Capture (CDC), and ensure data integrity.

Through lectures and hands-on demos, you will:

• Build multi-flow pipelines to ingest multi-source data into a unified Bronze table.

• Apply Liquid Clustering and Data Quality Expectations across Silver and Gold layers.

• Implement the Multiplex pattern with Iceberg UniForm for cross-platform data access.

• Automate SCD Type 2 history tracking using AUTO CDC INTO.

• Design zero-data-loss quarantine pipelines to audit and manage invalid records.

Note: 

1. This course is the first in the 'Advanced Data Engineering with Databricks' series.

2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

This course offers essential knowledge of Apache Spark™, with a focus on its distributed architecture and practical applications for large-scale data processing. Participants will explore programming frameworks, learn the Spark DataFrame API, and develop skills for reading, writing, and transforming data using Python-based Spark workflows.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Languages Available: English | 한국어

In this course, you'll learn how to effectively use Databricks for data analytics, with a specific focus on Databricks SQL. As a Databricks Data Analyst, your responsibilities will include finding relevant data, analyzing it for potential applications, and transforming it into formats that provide valuable business insights. You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL. Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!

Languages Available: English | 日本語 | Português BR | 한국어