DP203T00-Data Engineering on Microsoft Azure Certification Training

Topic Covered:

• 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:

• Design a star schema for analytical workloads
• Populate slowly changing dimensions with Azure Data Factory and mapping data flows

Topic Covered:

• Design a Modern Data Warehouse using Azure Synapse Analytics
• Secure a data warehouse in Azure Synapse Analytics

Lab:

• Managing files in an Azure data lake
• Securing files stored in an Azure data lake

Topic Covered:

• 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:

• 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)

Topic Covered:

• 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:

• 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

Topic Covered:

• 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:

• 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

Topic Covered:

• Use data loading best practices in Azure Synapse Analytics
• Petabyte-scale ingestion with Azure Data Factory

Lab:

• 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

Topic Covered:

• Data integration with Azure Data Factory or Azure Synapse Pipelines
• Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Lab:

• Execute code-free transformations at scale with Azure Synapse Pipelines
• Create data pipeline to import poorly formatted CSV files
• Create Mapping Data Flows

Topic Covered:

• Orchestrate data movement and transformation in Azure Data Factory

Lab:

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

Topic Covered:

• Optimize data warehouse query performance in Azure Synapse Analytics
• Understand data warehouse developer features of Azure Synapse Analytics

Lab:

• Understand developer features of Azure Synapse Analytics
• Optimize data warehouse query performance in Azure Synapse Analytics
• Improve query performance

Topic Covered:

• Analyze and optimize data warehouse storage in Azure Synapse Analytics

Lab:

• Check for skewed data and space usage
• Understand column store storage details
• Study the impact of materialized views
• Explore rules for minimally logged operations

Topic Covered:

• 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:

• 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

Topic Covered:

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

Lab:

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

Topic Covered:

• 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:

• 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

Topic Covered:

• Process streaming data with Azure Databricks structured streaming

Lab:

• 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

Topic Covered:

• Create reports with Power BI using its integration with Azure Synapse Analytics

Lab:

• 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

Topic Covered:

• Use the integrated machine learning process in Azure Synapse Analytics

Lab:

• 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