Module 1: 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

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

Module 2: 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 3: Data exploration and transformation in Azure Databricks
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.

Lessons

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

Module 4: 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 5: Ingest and load data into the data warehouse
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.

Lessons

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

Module 6: Transform data with Azure Data Factory or Azure Synapse Pipelines
This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.

Lessons

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

Module 7: 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 8: End-to-end security with Azure Synapse Analytics
In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.

Lessons

Secure a data warehouse in Azure Synapse Analytics
Configure and manage secrets in Azure Key Vault
Implement compliance controls for sensitive data

Lab : End-to-end security with Azure Synapse Analytics

Secure Azure Synapse Analytics supporting infrastructure
Secure the Azure Synapse Analytics workspace and managed services
Secure Azure Synapse Analytics workspace data

After completing this module, students will be able to:

Secure a data warehouse in Azure Synapse Analytics
Configure and manage secrets in Azure Key Vault
Implement compliance controls for sensitive data

Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.

Lessons

Design hybrid transactional and analytical processing using Azure Synapse Analytics
Configure Azure Synapse Link with Azure Cosmos DB
Query Azure Cosmos DB with Apache Spark pools
Query Azure Cosmos DB with serverless SQL pools

Lab : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

Configure Azure Synapse Link with Azure Cosmos DB
Query Azure Cosmos DB with Apache Spark for Synapse Analytics
Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics

After completing this module, students will be able to:

Design hybrid transactional and analytical processing using Azure Synapse Analytics
Configure Azure Synapse Link with Azure Cosmos DB
Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics
Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics

Module 10: Real-time Stream Processing with Stream Analytics
In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.

Lessons

Enable reliable messaging for Big Data applications using Azure Event Hubs
Work with data streams by using Azure Stream Analytics
Ingest data streams with Azure Stream Analytics

Lab : Real-time Stream Processing with Stream Analytics

Use Stream Analytics to process real-time data from Event Hubs
Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
Scale the Azure Stream Analytics job to increase throughput through partitioning
Repartition the stream input to optimize parallelization

After completing this module, students will be able to:

Enable reliable messaging for Big Data applications using Azure Event Hubs
Work with data streams by using Azure Stream Analytics
Ingest data streams with Azure Stream Analytics

Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks
In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.

Lessons

Process streaming data with Azure Databricks structured streaming

Lab : Create a Stream Processing Solution with Event Hubs and Azure Databricks

Explore key features and uses of Structured Streaming
Stream data from a file and write it out to a distributed file system
Use sliding windows to aggregate over chunks of data rather than all data
Apply watermarking to remove stale data
Connect to Event Hubs read and write streams

After completing this module, students will be able to:

Process streaming data with Azure Databricks structured streaming

DP-203T00: Data Engineering On Microsoft Azure