DP-203 Data Engineering on Microsoft Azure Print

In this course, the students will design various data platform technologies into solutions that are in line with business and technical requirements. This can include on-premises, cloud, and hybrid data scenarios which incorporate relational, NoSQL, or Data Warehouse data. They will also learn how to design process architectures using a range of technologies for both streaming and batch data. The students will also explore how to design data security, including data access, data policies, and standards. They will also design Azure data solutions, which includes the optimization, availability, and disaster recovery of big data, batch processing, and streaming data solutions.

Accredited course for Continuing Education of Pedagogical Staff

Course length: 4 days (8:30 - 16:00)

Dates

• Students will be able to

  • Prepare for the DP-203 certification exam
  • Use Azure services to implement data solutions

• Course requirements

  • AZ-900 - Azure Fundamentals
  • DP-900 - Microsoft Azure Data Fundamentals

• This course is intended for

  The audience for this course is Data Professionals, Data Architects, and Business Intelligence Professionals who want to learn about the data platform technologies that exist on Microsoft Azure. The secondary audience for this course is individuals who develop applications that deliver content from the data platform technologies that exist on Microsoft Azure.

• Literature

  All participants will receive original Microsoft student materials.

• Hardware

  Classrooms are equipped with high-performance computers with Internet access and the possibility of wireless connection.

• Syllabus

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

  • Lesson: Introduction to Azure Synapse Analytics
  • Lesson: Describe Azure Databricks
  • Lesson: Introduction to Azure Data Lake storage
  • Lesson: Describe Delta Lake architecture
  • Lesson: Work with data streams by using Azure Stream Analytics
  • Lab: Explore compute and storage options for data engineering workloads

  Module 2: Design and implement the serving layer

  • Lesson: Design a multidimensional schema to optimize analytical workloads
  • Lesson: Code-free transformation at scale with Azure Data Factory
  • Lesson: Populate slowly changing dimensions in Azure Synapse Analytics pipelines
  • Lab :Designing and Implementing the Serving Layer

  Module 3: Data engineering considerations for source files

  • Lesson: Design a Modern Data Warehouse using Azure Synapse Analytics
  • Lesson: Secure a data warehouse in Azure Synapse Analytics
  • Lab: Data engineering considerations

  Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools

  • Lesson: Explore Azure Synapse serverless SQL pools capabilities
  • Lesson: Query data in the lake using Azure Synapse serverless SQL pools
  • Lesson: Create metadata objects in Azure Synapse serverless SQL pools
  • Lesson: Secure data and manage users in Azure Synapse serverless SQL pools
  • Lab: Run interactive queries using serverless SQL pools

  Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark

  • Lesson: Understand big data engineering with Apache Spark in Azure Synapse Analytics
  • Lesson: Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Lesson: Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Lesson: Integrate SQL and Apache Spark pools in Azure Synapse Analytics
  • Lab: Explore, transform, and load data into the Data Warehouse using Apache Spark

  Module 6: Data exploration and transformation in Azure Databricks

  • Lesson: Describe Azure Databricks
  • Lesson: Read and write data in Azure Databricks
  • Lesson: Work with DataFrames in Azure Databricks
  • Lesson: Work with DataFrames advanced methods in Azure Databricks
  • Lab: Data Exploration and Transformation in Azure Databricks

  Module 7: Ingest and load data into the data warehouse

  • Lesson: Use data loading best practices in Azure Synapse Analytics
  • Lesson: Petabyte-scale ingestion with Azure Data Factory
  • Lab: Ingest and load Data into the Data Warehouse

  Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines

  • Lesson: Data integration with Azure Data Factory or Azure Synapse Pipelines
  • Lesson: Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
  • Lab: Transform Data with Azure Data Factory or Azure Synapse Pipelines

  Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines

  • Lesson: Orchestrate data movement and transformation in Azure Data Factory
  • Lab: Orchestrate data movement and transformation in Azure Synapse Pipelines

  Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse

  • Lesson: Optimize data warehouse query performance in Azure Synapse Analytics
  • Lesson: Understand data warehouse developer features of Azure Synapse Analytics
  • Lab: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse

  Module 11: Analyze and Optimize Data Warehouse Storage

  • Lesson: Analyze and optimize data warehouse storage in Azure Synapse Analytics
  • Lab: Analyze and Optimize Data Warehouse Storage

  Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

  • Lesson: Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Lesson: Configure Azure Synapse Link with Azure Cosmos DB
  • Lesson: Query Azure Cosmos DB with Apache Spark pools
  • Lesson: Query Azure Cosmos DB with serverless SQL pools
  • Lab: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

  Module 13: End-to-end security with Azure Synapse Analytics

  • Lesson: Secure a data warehouse in Azure Synapse Analytics
  • Lesson: Configure and manage secrets in Azure Key Vault
  • Lesson: Implement compliance controls for sensitive data
  • Lab: End-to-end security with Azure Synapse Analytics

  Module 14: Real-time Stream Processing with Stream Analytics

  • Lesson: Enable reliable messaging for Big Data applications using Azure Event Hubs
  • Lesson: Work with data streams by using Azure Stream Analytics
  • Lesson: Ingest data streams with Azure Stream Analytics
  • Lab: Real-time Stream Processing with Stream Analytics

  Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks

  • Lesson: Process streaming data with Azure Databricks structured streaming
  • Lab: Create a Stream Processing Solution with Event Hubs and Azure Databricks

  Module 16: Build reports using Power BI integration with Azure Synpase Analytics

  • Lesson: Create reports with Power BI using its integration with Azure Synapse Analytics
  • Lab: Build reports using Power BI integration with Azure Synpase Analytics

  Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

  • Lesson: Use the integrated machine learning process in Azure Synapse Analytics
  • Lab: Perform Integrated Machine Learning Processes in Azure Synapse Analytics