Overview
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. Participants 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 layers and focus on data engineering considerations for working with source files. 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.
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Attendees will also learn the various ways they can transform the data using the same technologies that is used to ingest data. Students 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. Participants 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.
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The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
Job Role: Data Engineer
Preparation for Exam: DP-700
Skills Gained
● Explore compute and storage options for data engineering workloads in Azure
● Design and Implement the serving layer
● Understand data engineering considerations
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 ![go to link]()
AND
DP-900: Microsoft Azure Data Fundamentals ![go to link]()
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COURSE OUTLINE
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
Module 2: 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
Module 3: 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
Module 4: 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
Module 5: 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
Module 6: 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
Module 7: 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
Module 8: 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
Module 9: 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
Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse
In this module, students will learn strategies to optimize 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 optimize and improve query performance.
Lessons
Optimize data warehouse query performance in Azure Synapse Analytics
Understand data warehouse developer features of Azure Synapse Analytics
Lab : Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
Understand developer features of Azure Synapse Analytics
Optimize data warehouse query performance in Azure Synapse Analytics
Improve query performance
Module 11: Analyze and Optimize Data Warehouse Storage
In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations.
Lessons
Analyze and optimize data warehouse storage in Azure Synapse Analytics
Lab : Analyze and Optimize Data Warehouse Storage
Check for skewed data and space usage
Understand column store storage details
Study the impact of materialized views
Explore rules for minimally logged operations
Module 12: 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
Module 13: 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
Module 14: 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
Module 15: 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
Module 16: Build reports using Power BI integration with Azure Synapase Analytics
In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI.
Lessons
Create reports with Power BI using its integration with Azure Synapse Analytics
Lab : Build reports using Power BI integration with Azure Synapase Analytics
Integrate an Azure Synapse workspace and Power BI
Optimize integration with Power BI
Improve query performance with materialized views and result-set caching
Visualize data with SQL serverless and create a Power BI report
Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics
This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI.
Lessons
Use the integrated machine learning process in Azure Synapse Analytics
Lab : Perform Integrated Machine Learning Processes in Azure Synapse Analytics
Create an Azure Machine Learning linked service
Trigger an Auto ML experiment using data from a Spark table
Enrich data using trained models
Serve prediction results using Power BI
