CONTENUTI:

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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 Synpase Analytics

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

 

Lab : Build reports using Power BI integration with Azure Synpase 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

• Use the integrated machine learning process in Azure Synapse Analytics
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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