Data Engineering using Databricks

Skills Gained DataBricks and AWS Integration: Introduces the Databricks on AWS architecture, covers workspace setup and configuration, and explores best practices for integrating with core AWS data...

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Duration 2 days
Course Code WA3926
Available Formats Classroom

Overview

Skills Gained

  • DataBricks and AWS Integration: Introduces the Databricks on AWS architecture, covers workspace setup and configuration, and explores best practices for integrating with core AWS data services including S3, Redshift, and RDS.
  • Data Ingestion with Lakeflow Connect: Covers the Lakeflow Connect platform and its role in the Medallion Architecture, including how to configure ingestion pipelines, connect to external source systems, implement incremental ingestion patterns, and govern data using Unity Catalog.
  • Deploy Workflows with Lakeflow Jobs: Teaches students how to build, schedule, and manage automated workflows using Lakeflow Jobs, including task types, conditional logic, parameterization, and notifications.
  • Lakeflow Spark Declarative Pipelines: Explores the declarative pipeline model in Databricks, covering how to build and manage Lakeflow Spark Declarative Pipelines in Python and SQL across Bronze, Silver, and Gold Medallion Architecture layers.
  • DevOps Practices in Data Engineering:Applies software engineering and DevOps principles to data engineering workflows, covering Git version control, CI/CD pipeline design, PySpark code modularization, unit and integration testing, asset deployment, and Infrastructure as Code.
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Course Details

Course Details

WA3926 Introduction

  • Agenda
  • What is covered in this course?

DataBricks and AWS Integration

  • Introduction
  • Databricks on AWS Architecture
  • Benefits of Using Databricks on AWS
  • Setting Up a Databricks Workspace on AWS
  • Setting up Databricks to Access AWS
  • Workspace Admins and Users
  • Accessing Data Stored in AWS with Databricks
  • AWS S3 Integration Best Practices
  • Amazon Redshift Integration Best Practices
  • AWS RDS Integration Best Practices
  • Reflection Questions
  • Resources
  • Review

Data Ingestion with Lakeflow Connect

  • Introduction to Lakeflow Connect
  • Databricks Data Intelligence Platform
  • Lakeflow Connect and Medallion Architecture
  • Example of a medallion architecture pipeline
  • Understanding Data Lake
  • Data Lakehouse
  • Unity Catalog in Databricks
  • Architecture of Lakeflow Connect
  • Why Use Lakeflow Connect?
  • Core Components of Lakeflow Connect
  • Connector Types Supported by Lakeflow Connect
  • Configuring Lakeflow Connect Ingestion Pipelines
  • Establishing Connections to External Source Systems
  • Implementing Incremental Ingestion Patterns
  • Analyzing Schema Changes
  • Loading Ingested Data into Unity Catalog Governed Tables and Demonstrating Governance Capabilities
  • Best Practices for Data Ingestion with Lakeflow Connect
  • Reflection Questions
  • Resources
  • Review

Deploy Workflows with Lakeflow Jobs

  • What is a Lakeflow Job?
  • Why Use Lakeflow Jobs?
  • Lakeflow Jobs Use Cases
  • Why Workflow Automation?
  • Benefits of Using Lakeflow Jobs for Workflow Automation
  • Lakeflow Jobs Core Concepts
  • Architecture of Lakeflow Jobs
  • Lakeflow Jobs Terminology
  • Creating and Scheduling Jobs
  • Lakeflow Jobs Tasks
  • Task Types
  • Orchestration and code modularization methods
  • Creating a Job using a Controller Notebook
  • Creating a Job using Spark SQL and Delta Lake
  • Creating a Pipeline using the UI
  • Running a Job
  • Schedules & Triggers
  • Task Dependencies and Conditional Logic
  • If/Else Task Example
  • Lakeflow Jobs Configuration and Management
  • Parameters
  • Notifications
  • Lakeflow Jobs Examples
  • Lakeflow Jobs API
  • Advance Lakeflow Jobs Features
  • Lakeflow Jobs Limitations
  • Best Practices
  • Reflection Questions
  • Resources
  • Review

Lakeflow Spark Declarative Pipelines

  • What is a Lakeflow Spark Declarative Pipeline (SDP)?
  • Lakeflow Spark Declarative Pipeline (SDP) Fundamentals
  • How does Lakeflow Pipelines extend Apache Spark Declarative Pipelines
  • Core Concepts
  • Advantages of Declarative Approach
  • Key Components
  • Illustrated Architecture
  • Datasets and Data Sources
  • Building Lakeflow Spark Declarative Pipelines
  • Managing Lakeflow Spark Declarative Pipelines
  • Lakeflow SDP Python and SQL Coding
  • Medallion Architecture in Pipelines
  • Pipeline Limitations and Challenges
  • Best Practices for Lakeflow Spark Declarative Pipelines
  • Reflection Questions
  • Resources
  • Review

DevOps Essentials for Data Engineering

  • Introduction to Software Engineering (SWE) Best Practices
  • Introduction to DevOps for Data Engineering
  • DevOps Fundamentals

DevOps Practices in Data Engineering

  • DevOps Tools and Technologies
  • DevOps Illustrated
  • Planning the Project
  • Introduction to Modularizing PySpark Code
  • Version Control with Git Overview
  • The Role of CI/CD in DevOps
  • CI/CD Pipelines for Data Engineering
  • Introduction to Unit Tests for PySpark
  • Executing Integration Tests with DLT and Workflows
  • Deploying Databricks Assets Overview
  • Benefits of CI/CD pipelines
  • Introduction to Infrastructure as Code (IaC)
  • Best practices for implementing CI/CD pipelines
  • Reflection Questions
  • Resources
  • Review

WA3926 Conclusion

  • Course Summary
  • Thank You

Schedule

Upcoming Course Dates

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Reviews

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