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Data Engineering on Microsoft Azure

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Data Engineering on Microsoft Azure

About this Course

  • In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layer and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.

Audience Profile

  • The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

Audience Prerequisites

  • Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions. Specifically completing:  AZ-900 - Azure Fundamentals  DP-900 - Microsoft Azure Data Fundamentals

Module 1: Azure + Azure Data Fundamentals

  • Introduction to Azure
  • Navigating Azure Portal
  • Overview of Different types Azure Storage.
  • Blob Storage
  • Storage Redundancy
  • Storage Access Tiers
  • Storage Explorer
  • ADLS Gen2
  • Securing and Managing Storage
  • Storing and Accessing Data.
  • How to choose proper storage types.
  • Monitoring Storage.
  • Azure SQL Database
  • CosmosDB

Module 2: Azure Data Factory using Synapse Analytics

  • What Is Azure Data Factory?
  • Why We Need It?
  • How does Data Factory work?
  • Key Components of Azure Data Factory
  • Copy Data Flow activity in ADF
  • Azure Data Factory Instance
  • How to Connect to Azure SQL Database from On-Premise
  • Linked Services
  • Input & Output DataSet in Azure Data Factory
  • Control Flow Activity
  • Get Metadata Activity
  • Filter Activity
  • If Activity
  • Append Activity
  • Wait Activity
  • Dataflow Transformation
  • Source Transformation
  • Sink Transformation
  • Lookup Transformation
  • Conditional Split Transformation
  • Derived Column Transformation
  • Select Transformation
  • Filter Transformation
  • Join Transformation
  • Exists Transformation
  • Lookup Activity Dataflow Transformation
  • Pipeline Parameters
  • DataFlow Parameters
  • Use Case Implementation

Module 3: Explore compute and storage options for data engineering workloads

  • This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.

LESSONS

  • Introduction to Azure Synapse Analytics
  • Describe Azure Databricks
  • Introduction to Azure Data Lake storage
  • Describe Delta Lake architecture
  • Work with data streams by using Azure Stream Analytics

Lab: Explore compute and storage options for data engineering workloads

  • Organize the data lake into levels of file transformation
  • Index data lake storage for query and workload acceleration

After completing this module, students will be able to:

  • Describe Azure Synapse Analytics
  • Describe Azure Databricks
  • Describe Azure Data Lake storage
  • Describe Delta Lake architecture
  • Describe Azure Stream Analytics

Module 4: Design and implement the serving layer

  • This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory.

LESSONS

  • Design a multidimensional schema to optimize analytical workloads
  • Code-free transformation at scale with Azure Data Factory
  • Populate slowly changing dimensions in Azure Synapse Analytics pipelines

Lab: Designing and Implementing the Serving Layer

  • Design a star schema for analytical workloads
  • Populate slowly changing dimensions with Azure Data Factory and mapping data flows

After completing this module, students will be able to:

  • Design a star schema for analytical workloads
  • Populate a slowly changing dimensions with Azure Data Factory and mapping data flows

Module 5: Data engineering considerations for source files

  • This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake.

LESSONS

  • Design a Modern Data Warehouse using Azure Synapse Analytics
  • Secure a data warehouse in Azure Synapse Analytics

Lab: Data engineering considerations

  • Managing files in an Azure data lake
  • Securing files stored in an Azure data lake

After completing this module, students will be able to:

  • Design a Modern Data Warehouse using Azure Synapse Analytics
  • Secure a data warehouse in Azure Synapse Analytics

Module 6: Run interactive queries using Azure Synapse Analytics serverless SQL pools

  • In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).

Lessons

  • Explore Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools

Lab: Run interactive queries using serverless SQL pools

  • Query Parquet data with serverless SQL pools
  • Create external tables for Parquet and CSV files
  • Create views with serverless SQL pools
  • Secure access to data in a data lake when using serverless SQL pools
  • Configure data lake security using Role-Based Access Control (RBAC) and Access Control List

After completing this module, students will be able to:

  • Understand Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools

Module 7: Explore, transform, and load data into the Data Warehouse using Apache Spark

  • This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.

Lessons

  • Understand big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Lab: Explore, transform, and load data into the Data Warehouse using Apache Spark

  • Perform Data Exploration in Synapse Studio
  • Ingest data with Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Spark pools in Azure Synapse Analytics
  • Integrate SQL and Spark pools in Azure Synapse Analytics

After completing this module, students will be able to:

  • Describe big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Module 8: Orchestrate data movement and transformation in Azure Synapse Pipelines

  • In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.

Lessons

  • Orchestrate data movement and transformation in Azure Data Factory

Lab: Orchestrate data movement and transformation in Azure Synapse Pipelines

  • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines

After completing this module, students will be able to:

  • Orchestrate data movement and transformation in Azure Synapse Pipelines

Module 9: Azure Monitor and Log Analytics Workspace

  • Azure Monitor
  • Introduction to Log Analytics Workspace
  • Kusto Queries

Module 10: Azure ML Overview

  • Introduction to Azure Machine Learning: Features, Capabilities, and Use Cases
  • Azure ML Workflow: Data Preparation, Model Training, Deployment, and Monitoring

Module 11: Introduction to Fabric Analytics

  • Understanding Fabric Analytics: Architecture, Key Features, and Benefits
  • Data Integration and Processing: Connecting Sources, Data Transformation, and Real-Time Analytics
  • Insights Generation: Advanced Visualization, Reporting, and Decision-Making Capabilitie

T. Sanjay

Tech Enthusiast | Seasoned Corporate EnterT(r)ainer

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