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Deploy Workloads with Databricks Workflows

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.

In this half-day course, you’ll learn how to orchestrate data pipelines with Databricks Workflow Jobs and schedule dashboard updates to keep analytics up-to-date. We’ll cover topics like getting started with Databricks Workflows, how to use Databricks SQL for on-demand queries, and how to configure and schedule dashboards and alerts to reflect updates to production data pipelines.


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

Skill Level
Associate
Duration
4h
Prerequisites
  • Beginner-level familiarity with basic cloud concepts (virtual machines, object storage, identity management)

  • Ability to perform basic code development tasks (create compute, run code in notebooks, use basic notebook operations, import repos from git, etc)

  • Intermediate familiarity with basic SQL concepts (CREATE, SELECT, INSERT, UPDATE, DELETE, WHILE, GROUP BY, JOIN, etc.)

Outline

  • Introduction to Workflows
  • Jobs Compute

- Scheduling Tasks with the Jobs UI

- Workflows Lab

  • Jobs Features

- Explore Scheduling Options

- Conditional Tasks and Repairing Runs

- Modular Orchestration

  • Databricks Workflows Best Practices

Upcoming Public Classes

Date
Time
Language
Price
May 12
01 PM - 05 PM (Europe/London)
English
$750.00
May 14
01 PM - 05 PM (Asia/Kolkata)
English
$750.00
May 16
09 AM - 01 PM (America/New_York)
English
$750.00
Jun 16
09 AM - 01 PM (Asia/Kolkata)
English
$750.00
Jun 18
01 PM - 05 PM (America/New_York)
English
$750.00
Jun 20
09 AM - 01 PM (Europe/London)
English
$750.00
Jul 14
01 PM - 05 PM (Europe/London)
English
$750.00
Jul 15
01 PM - 05 PM (Asia/Kolkata)
English
$750.00
Jul 17
09 AM - 01 PM (America/New_York)
English
$750.00

Public Class Registration

If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.

Private Class Request

If your company is interested in private training, please submit a request.

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Upcoming Public Classes

Data Engineer

Automated Deployment with Databricks Asset Bundles

This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines.

The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI.

Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles.

By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices.

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

Paid
4h
Lab
instructor-led
Professional

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.