MDAI-3 - MOC DP-203T00 - DATA ENGINEERING ON MICROSOFT AZURE

INFORMAZIONI SUL CORSO

durata

Durata:

4 GIORNI
categoria

Categoria:

Data & AI
qualifica

Qualifica istruttore:

Microsoft Certified Trainer
dedicato a

Dedicato a:

Professionista IT
produttore

Produttore:

Microsoft

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OBIETTIVI

  • Explore compute and storage options for data engineering workloads in Azure
  • Design and Implement the serving layer
  • Understand data engineering considerations
  • Run interactive queries using serverless SQL pools
  • Explore, transform, and load data into the Data Warehouse using Apache Spark
  • Perform data Exploration and Transformation in Azure Databricks
  • 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
  • Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
  • Analyze and Optimize Data Warehouse Storage
  • 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
  • Build reports using Power BI integration with Azure Synapase Analytics
  • Perform Integrated Machine Learning Processes in Azure Synapse Analytics

PREREQUISITI

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
  • DP-900 - Microsoft Azure Data Fundamentals

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 Synapase Analytics

  • 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

  • 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

 

 

INFO

  • Esame: DP-203 - Data Engineering on Microsoft Azure
  • Manuale: Materiale didattico ufficiale Microsoft in formato digitale
  • Prezzo manuale: 210 € incluso nel prezzo del corso a Calendario
  • Natura del corso: Operativo (previsti lab su PC)

PARTNERSHIP