Build Data Pipelines with DLT
This course introduces users to the essential concepts and skills needed to build data pipelines using DLT in Databricks for incremental ingesting and processing through multiple streaming tables and materialized views. Designed for data engineers new to DLT, the course provides a comprehensive overview of core components such as incremental data processing, streaming tables, materialized views, and temporary views, reviewing their specific purposes and differences.
Topics covered include:
- Developing and debuging ETL pipelines with the multi-file editor in DLT using SQL (with Python code examples provided)
- How DLT tracks data dependencies in pipelines within the pipeline graph
- Configuring pipeline compute resources, data assets, trigger modes, and other advanced options
Next, the course introduces data quality expectations in DLT, guiding users through the process of integrating expectations into pipelines to validate and enforce data integrity. Then learners will then explore how to put a pipeline into production, including scheduling options, and gain basic knowledge in analyzing pipeline event logs to monitor a pipeline.
Finally, the course covers the implementation of Change Data Capture (CDC) using the APPLY CHANGES INTO syntax to manage slowly changing dimensions (SCD Type 1 and Type 2), preparing users to integrate CDC into their own pipelines.
⇾ Basic understanding of the Databricks Data Intelligence platform, including Databricks Workspaces, Apache Spark, Delta Lake, the Medallion Architecture and Unity Catalog.
⇾ Experience ingesting raw data into Delta tables, including using the read_files SQL function to load formats like CSV, JSON, TXT, and Parquet.
⇾ Proficiency in transforming data using SQL, including writing intermediate-level queries and a basic understanding of SQL joins.
Outline
Introduction to Data Engineering in Databricks
⇾ Data Engineering in Databricks
⇾ What is DLT?
⇾ Course Setup and Creating a Pipeline
⇾ Course Project Overview
Pipeline Fundamentals
⇾ Dataset Types Overview
⇾ Simplified Pipeline Development
⇾ Common Pipeline Settings
⇾ Developing a Simple Pipeline
⇾ Ensure Data Quality with Expectations
Building Pipelines
⇾ Streaming Joins Overview
⇾ Deploying a Pipeline to Production
⇾ Change Data Capture (CDC) Overview
⇾ Change Data Capture with Apply CHANGE INTO
⇾ Additional DLT Features and Next Steps
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