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Data Workloads with Repos and Workflows

Moving a data pipeline to production means more than just confirming that code and data are working as expected. By scheduling tasks with Databricks Jobs, applications can be run automatically to keep tables in the Lakehouse fresh. Using Databricks SQL to schedule updates to queries and dashboards allows quick insights using the newest data. In this course, students will be introduced to task orchestration using the Databricks Workflow Jobs UI. Optionally, they will configure and schedule dashboards and alerts to reflect updates to production data pipelines. 

Skill Level



  • Introduction to Databricks architecture and services
    • Navigating the Databricks Workspace
    • Introduction to Databricks compute resources
    • Creating and managing interactive clusters
    • Working with Databricks Repos
    • Databricks notebook operations
    • Introduction to Databricks Workflows 
    • Building and monitoring Workflow jobs
    • Scheduling tasks with the Jobs UI

Upcoming Public Classes

Jun 17
01 PM - 05 PM (Europe/London)
Jun 20
09 AM - 01 PM (America/Los_Angeles)
Jun 25
08 AM - 12 PM (Australia/Sydney)
Jul 16
02 PM - 06 PM (Asia/Kolkata)
Jul 17
02 PM - 06 PM (America/New_York)
Jul 18
09 AM - 01 PM (Europe/London)

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Data Engineer

Data Pipelines with Delta Live Tables

In this course, you'll use Delta Live Tables with your choice of Spark SQL or Python to define and schedule pipelines that incrementally process new data from a variety of data sources into the Lakehouse. Learning objectives Describe how Delta Live Tables tracks data dependencies in data pipelines. Configure and run data pipelines using the Delta Live Tables UI. Use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables in the lakehouse using Auto Loader and Delta Live Tables. Use APPLY CHANGES INTO syntax to process Change Data Capture feeds. Review event logs and data artifacts created by pipelines and troubleshoot DLT syntaxPrerequisites Beginner familiarity with cloud computing concepts (virtual machines, object storage, etc.) Ability to perform basic code development tasks using the Databricks Data Engineering & Data Science workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc) Beginning programming experience with Delta Lake,Use Delta Lake DDL to create tables, compact files, restore previous table versions, and perform garbage collection of tables in the Lakehouse.Use CTAS to store data derived from a query in a Delta Lake table.Use SQL to perform complete and incremental updates to existing tables. Beginner programming experience with Python (syntax, conditions, loops, functions) Beginning programming experience with Spark SQL or 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. Production experience working with data warehouses and data lakes. Last course update April 2023
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