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.

• 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

• Combine streaming and batch processing with a single pipeline
• 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

This module teaches how to design and implement data stores in a modern data warehouse to optimise 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 optimise 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

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

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

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.

• 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 Data Frames 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 Data Frames in Apache Spark Pools in Azure Synapse Analytics
• Integrate SQL and Apache Spark pools in Azure Synapse Analytics

This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.

• Describe Azure Databricks
• Read and write data in Azure Databricks
• Work with DataFrames in Azure Databricks
• Work with DataFrames advanced methods in Azure Databricks

Lab: Data Exploration and Transformation in Azure Databricks

• Use DataFrames in Azure Databricks to explore and filter data
• Cache a DataFrame for faster subsequent queries
• Remove duplicate data
• Manipulate date/time values
• Remove and rename DataFrame columns
• Aggregate data stored in a DataFrame

After completing this module, students will be able to:

• Describe Azure Databricks
• Read and write data in Azure Databricks
• Work with DataFrames in Azure Databricks
• Work with DataFrames advanced methods in Azure Databricks

This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion.

• Use data loading best practices in Azure Synapse Analytics
• Petabyte-scale ingestion with Azure Data Factory

Lab: Ingest and load Data into the Data Warehouse

• Perform petabyte-scale ingestion with Azure Synapse Pipelines
• Import data with PolyBase and COPY using T-SQL
• Use data loading best practices in Azure Synapse Analytics

After completing this module, students will be able to:

• Use data loading best practices in Azure Synapse Analytics
• Petabyte-scale ingestion with Azure Data Factory

This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flows, and perform data movement into one or more data sinks.

• Data integration with Azure Data Factory or Azure Synapse Pipelines
• Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Lab: Transform Data with Azure Data Factory or Azure Synapse Pipelines

• Execute code-free transformations at scale with Azure Synapse Pipelines
• Create data pipeline to import poorly formatted CSV files
• Create Mapping Data Flows

After completing this module, students will be able to:

• Perform data integration with Azure Data Factory
• Perform code-free transformation at scale with Azure Data Factory

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

• 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

In this module, students will learn strategies to optimise data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimise and improve query performance.

• Optimise data warehouse query performance in Azure Synapse Analytics
• Understand data warehouse developer features of Azure Synapse Analytics

Lab: Optimise Query Performance with Dedicated SQL Pools in Azure Synapse

• Understand developer features of Azure Synapse Analytics
• Optimise data warehouse query performance in Azure Synapse Analytics
• Improve query performance

After completing this module, students will be able to:

• Optimise data warehouse query performance in Azure Synapse Analytics
• Understand data warehouse developer features of Azure Synapse Analytics