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Overview

Deploy Workloads with Lakeflow Jobs course teaches how to orchestrate and automate data, analytics, and AI workflows using Lakeflow Jobs as a unified orchestration platform within the Databricks ecosystem. 

  • You will learn to design and implement data workloads using Directed Acyclic Graphs (DAGs), configure various scheduling options, and implement advanced workflow features such as conditional task execution, run-if dependencies, and for each loops. 
  • The course covers best practices for creating robust, production-ready pipelines with proper compute selection, modular orchestration, error handling techniques, and fault-tolerant design-all natively integrated within the Databricks Data Intelligence Platform.
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What You'll Learn

  • Introduction to Data Engineering in Databricks
  • Lakeflow Jobs Core Concepts
  • Creating and Scheduling Jobs
  • Advance Lakeflow Jobs Features

Who Should Attend

N/A
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Prerequisites

  • Completion of the course "Get Started with Databricks for Data Engineering", or a solid understanding of the Databricks Data Intelligence Platform
  • Basic Understanding of topics like navigating a Databricks Workspace, Apache Spark, Delta Lake, Medallion Architecture, and Unity Catalog.
  • Familiarity with python/pyspark
  • Experience in writing intermediate-level SQL queries.

Learning Journey

Coming Soon...

Introduction to Data Engineering in Databricks

  • Data Engineering in Databricks
  • What is Lakeflow Jobs?

Lakeflow Jobs Core Concepts

  • Building Blocks of Lakeflow Jobs
  • Task Orchestration
  • Creating a Job Using Lakeflow Jobs UI

Creating and Scheduling Jobs

  • Common Task Configuration Options
  • Jobs Scheduling and Triggers
  • Demo - Exploring Scheduling Options

Advance Lakeflow Jobs Features

  • Conditional and Iterative Tasks
  • Handling Task Failures
  • Lakeflow Jobs in Production
  • Best Practices

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Frequently Asked Questions (FAQs)

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