DevOps Essentials for Data Engineering

This course explores software engineering best practices and DevOps principles, specifically designed for data engineers working with Databricks. Participants will build a strong foundation in key topics such as code quality, version control, documentation, and testing. The course emphasizes DevOps, covering core components, benefits, and the role of continuous integration and delivery (CI/CD) in optimizing data engineering workflows.

You will learn how to apply modularity principles in PySpark to create reusable components and structure code efficiently. Hands-on experience includes designing and implementing unit tests for PySpark functions using the pytest framework, followed by integration testing for Databricks data pipelines with DLT and Workflows to ensure reliability.

The course also covers essential Git operations within Databricks, including using Databricks Git Folders to integrate continuous integration practices. Finally, you will take a high level look at various deployment methods for Databricks assets, such as REST API, CLI, SDK, and Databricks Asset Bundles (DABs), providing you with the knowledge of techniques to deploy and manage your pipelines.

By the end of the course, you will be proficient in software engineering and DevOps best practices, enabling you to build scalable, maintainable, and efficient data engineering solutions.

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

Skill Level

Associate

Duration

Prerequisites

- Proficient knowledge of the Databricks platform, including experience with Databricks Workspaces, Apache Spark, Delta Lake and the Medallion Architecture, Unity Catalog, Delta Live Tables, and Workflows. A basic understanding of Git version control is also required.
- Experience ingesting and transforming data, with proficiency in PySpark for data processing and DataFrame manipulations. Additionally, candidates should have experience writing intermediate level SQL queries for data analysis and transformation.
- Knowledge of Python programming, with proficiency in writing intermediate level Python code, including the ability to design and implement functions and classes. Users should also be skilled in creating, importing, and effectively utilizing Python packages.

Outline

Software Engineering Best Practices, DevOps, and CI/CD Fundamentals

Introduction to Software Engineering (SWE) Best Practices

Introduction to Modularizing PySpark Code

Modularizing PySpark Code

DevOps Fundamentals

The Role of CI/CD in DevOps

Knowledge Check/Discussion

Continuous Integration (CI)

Planning the Project

Project Setup Exploration

Introduction to Unit Tests for PySpark

Creating and Executing Unit Tests

Executing Integration Tests with DLT and Workflows

Performing Integration Tests with DLT and Workflows

Version Control with Git Overview

Introduction to Continuous Deployment (CD)

Deplyoying Databricks Assets Overview (slides)

Deploying the Databricks Project

Upcoming Public Classes

Date	Time	Language	Price
Date	Time	Language	Price	Aug 11	09 AM - 01 PM (Asia/Kolkata)	English	$750.00
Sep 08	09 AM - 01 PM (Asia/Kolkata)	English	$750.00
Sep 08	09 AM - 01 PM (America/New_York)	English	$750.00
Sep 09	09 AM - 01 PM (America/New_York)	English	$750.00
Oct 06	01 PM - 05 PM (Asia/Kolkata)	English	$750.00
Oct 07	01 PM - 05 PM (Europe/London)	English	$750.00

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Upcoming Public Classes

Data Engineer

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In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more.

Note: This course is part of the 'Advanced Data Engineering with Databricks' course series.

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

Data Analysis with Databricks

This course provides a comprehensive introduction to Databricks SQL. Learners will ingest data, write queries, produce visualizations and dashboards, and configure alerts. This course will prepare you to take the Databricks Certified Data Analyst Associate exam.

This course consists of two four-hour modules.

SQL Analytics on Databricks

In this course, you'll learn how to effectively use Databricks for data analytics, with a specific focus on Databricks SQL. As a Databricks Data Analyst, your responsibilities will include finding relevant data, analyzing it for potential applications, and transforming it into formats that provide valuable business insights.

You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL.

Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

AI/BI for Data Analysts

In this course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Engine.

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

Data Warehousing Practitioner

Data Warehousing with Databricks

This course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently using the industry-standard TCP-DI dataset as a reference. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks.

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