Implement a Data Analytics Solution with Azure Databricks

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Implement a Data Analytics Solution with Azure Databricks

Implement a Data Analytics Solution with Azure Databricks

Course ID: DP-3011

Duration: 1 Day

Overview

This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production.

Learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark
Build confidence in managing distributed data processing
Get hands-on with the Databricks workspace—navigating clusters and creating and optimizing Delta tables
Dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality.
Automate and manage workloads with Lakeflow Jobs and pipelines
Explore governance and security capabilities such as Unity Catalog and Purview integration

Private in-house training

Apart from public, instructor-led classes, we also offer private in-house trainings for organizations based on their needs. Call us at +852 2116 3328 or email us at [email protected] for more details.

What are the skills covered

Explore Azure Databricks
Use Apache spark in Azure Databricks
Use Delta Lake in Azure Databricks
Use SQL Warehouses in Azure Databricks
Run Azure Databricks Notebooks with Azure Data Factory

Who should attend this course

IT Professionals

Course Modules

Module 1: Explore Azure Databricks

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.

Learning objectives

In this module, you’ll learn how to:

Provision an Azure Databricks workspace.
Identify core workloads and personas for Azure Databricks.
Describe key concepts of an Azure Databricks solution.

Prerequisites

Before starting this module, you should have a fundamental knowledge of data analytics concepts. Consider completing Azure Data Fundamentals certification before starting this module.

Module 2: Use Apache Spark in Azure Databricks

Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.

Learning objectives

In this module, you’ll learn how to:

Describe key elements of the Apache Spark architecture.
Create and configure a Spark cluster.
Describe use cases for Spark.
Use Spark to process and analyze data stored in files.
Use Spark to visualize data.

Prerequisites

Before starting this module, you should have a basic knowledge of Azure Databricks. Consider completing the Explore Azure Databricks module before this one.

Module 3: Use Delta Lake in Azure Databricks

Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Databricks.

Learning objectives

In this module, you’ll learn how to:

Describe core features and capabilities of Delta Lake.
Create and use Delta Lake tables in Azure Databricks.
Create Spark catalog tables for Delta Lake data.
Use Delta Lake tables for streaming data.

Prerequisites

Before starting this module, you should know how to use Apache Spark in Azure Databricks. Consider completing the Use Apache Spark in Azure Databricks module before this one.

Module 4: Use SQL Warehouses in Azure Databricks

Azure Databricks provides SQL Warehouses that enable data analysts to work with data using familiar relational SQL queries.

Learning objectives

In this module, you’ll learn how to:

Create and configure SQL Warehouses in Azure Databricks.
Create databases and tables.
Create queries and dashboards.

Prerequisites

Before starting this module, you should have a basic knowledge of Azure Databricks. Consider completing the Explore Azure Databricks module before this one.

Module 5: Run Azure Databricks Notebooks with Azure Data Factory

Using pipelines in Azure Data Factory to run notebooks in Azure Databricks enables you to automate data engineering processes at cloud scale.

Learning objectives

In this module, you’ll learn how to:

Describe how Azure Databricks notebooks can be run in a pipeline.
Create an Azure Data Factory linked service for Azure Databricks.
Use a Notebook activity in a pipeline.
Pass parameters to a notebook.

Prerequisites

Before starting this module, you should have a basic knowledge of Azure Databricks. Consider completing the Explore Azure Databricks module before this one.

Prerequisites

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Implement a Data Analytics Solution with Azure Databricks