Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
Specifically completing:
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
Lab : Explore compute and storage options for data engineering workloads
After completing this module, students will be able to:
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
Lab : Run interactive queries using serverless SQL pools
After completing this module, students will be able to:
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
Lab : Data Exploration and Transformation in Azure Databricks
After completing this module, students will be able to:
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
Lab : Explore, transform, and load data into the Data Warehouse using Apache Spark
After completing this module, students will be able to:
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
Lab : Ingest and load Data into the Data Warehouse
After completing this module, students will be able to:
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
Lab : Transform Data with Azure Data Factory or Azure Synapse Pipelines
After completing this module, students will be able to:
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
Lab : Orchestrate data movement and transformation in Azure Synapse Pipelines
After completing this module, students will be able to:
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
Lab : End-to-end security with Azure Synapse Analytics
After completing this module, students will be able to:
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
Lab : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
After completing this module, students will be able to:
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
Lab : Real-time Stream Processing with Stream Analytics
After completing this module, students will be able to:
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
Lab : Create a Stream Processing Solution with Event Hubs and Azure Databricks
After completing this module, students will be able to: Process streaming data with Azure Databricks structured streaming
Preparation for exam: DP-203
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.
