Microsoft Data Engineering on Microsoft Azure Training (DP-203)

In this course, the student will learn about data engineering 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. 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. 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 to create a real-time analytical system to create real-time analytical solutions.

Microsoft Data Engineering on Microsoft Azure Training (DP-203) Delivery Methods

• In-Person

• Online

Microsoft Data Engineering on Microsoft Azure Training (DP-203) Course Benefits

• Explore compute and storage options for data engineering workloads in Azure

• Run interactive queries using serverless SQL pools

• Perform data Exploration and Transformation in Azure Databricks

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

• Ingest and load Data into the Data Warehouse

• Transform Data with Azure Data Factory or Azure Synapse Pipelines

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

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

• Perform end-to-end security with Azure Synapse Analytics

• Perform real-time Stream Processing with Stream Analytics

• Create a Stream Processing Solution with Event Hubs and Azure Databricks

• Continue learning and face new challenges with after-course one-on-one instructor coaching

Microsoft DP-203 Training Outline

Module 1: Explore compute and storage options for data engineering workloads

In this module, you will learn how to use Azure Synapse Analytics to:

• 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: Run interactive queries using Azure Synapse Analytics serverless SQL pools

In this module, you will learn how to:

• 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 ( ACL s)

Module 3: Data exploration and transformation in Azure Databricks

In this module, you will learn how to use various Apache Spark DataFrame methods to:

• Explore and transform data in 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 4: Explore, transform, and load data into the Data Warehouse using Apache Spark

In this module, you will learn how to:

• 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 5: Ingest and load data into the data warehouse

In this module, you will learn how to:

• 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 6: Transform data with Azure Data Factory or Azure Synapse Pipelines

In this module, you will learn how to:

• 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 a data pipeline to import poorly formatted CSV files
• Create Mapping Data Flows

Module 7: Orchestrate data movement and transformation in Azure Synapse Pipelines

In this module, you will learn how to:

• 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 8: End-to-end security with Azure Synapse Analytics

In this module, you will learn how to:

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

In this module, you will learn how 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 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 10: Real-time Stream Processing with Stream Analytics

In this module, you will learn how 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

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 of the stream input to optimize the parallelization

Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks

In this module, you will learn how to:

• 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