Data Engineering on Microsoft Azure - DP-203T00

Dieses Seminar richtet sich an:

• Datenexperten, Datenarchitekten und Business-Intelligence-Experten geeignet, die mehr über Data Engineering und das Erstellen von Analyselösungen mit Datenplattformtechnologien erfahren möchten 
• Die sekundäre Zielgruppe für diesen Kurs sind Datenanalysten und Datenwissenschaftler, die mit Analyselösungen arbeiten, die auf Microsoft Azure basieren.

 
 

Für dieses Seminar werden folgende Vorkenntnisse empfohlen:
Die Kenntnisse zu Cloud Computing und Kerndatenkonzepten sowie Berufserfahrung mit Datenlösungen sind notwendig
 
Folgende Seminare werden zur Vorbereitung empfohlen:
AZ-900 – Microsoft Azure Fundamentals
DP-900 – Microsoft Azure Data Fundamentals

 

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

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

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

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

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

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

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

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

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

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

Analyze and Optimize Data Warehouse Storage

• Analyze and optimize data warehouse storage in Azure Synapse Analytics

Lab: Analyze and Optimize Data Warehouse Storage

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

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

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

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

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

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