Databricks Streaming and Lakeflow Declarative Pipelines

In this course, you’ll learn how to Incrementally process data to power analytic insights with Structured Streaming and Auto Loader, and how to apply design patterns for designing workloads to perform ETL on the Data Intelligence Platform with Lakeflow Declarative Pipelines. First, we’ll cover topics including ingesting raw streaming data, enforcing data quality, implementing CDC, and exploring and tuning state information. Then, we’ll cover options to perform a streaming read on a source, requirements for end-to-end fault tolerance, options to perform a streaming write to a sink, and creating an aggregation and watermark on a streaming dataset.

Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc.), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.). Beginner experience with streaming workloads and familiarity with Lakeflow Declarative Pipelines.

Labs: No

Certification Path: Databricks Certified Data Engineer Professional