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Azure
$2295
Per Participant
DP-203: Data Engineering on Microsoft Azure course is intended for all data engineers. All the professionals who want to learn this course must also have the subject matter expertise. They should know how to integrate, transform, and consolidate the data. Learning this course will help professionals know about all the pipelines of data.
The data engineer further has to ensure that the data stores perform efficiently in an organized manner as per the business requirement. There are some other prerequisites for this course as well. The professionals must be strong about processing languages such as SQL, Python, or Scala. The main skills that they will acquire are implementation of data storage, development of data processing, data security, and optimizing the data storage and data processing.
This training is designed based on the objectives of the course variant DP-203T00-A.
Required
The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure.
The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
Microsoft Certified PartnerMicrotek Learning is a Microsoft Certified Partner for Learning Solutions. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).
This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.
Lab: Explore compute and storage options for data engineering workloads
After completing this module, students will be able to:
This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory.
Lab: Designing and Implementing the Serving Layer
After completing this module, students will be able to:
This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake.
Lab: Data engineering considerations
After completing this module, students will be able to:
In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).
Lab: Run interactive queries using serverless SQL pools
After completing this module, students will be able to:
This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.
Lab: Explore, transform, and load data into the Data Warehouse using Apache Spark
After completing this module, students will be able to:
This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.
Lab: Data Exploration and Transformation in Azure Databricks
After completing this module, students will be able to:
This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in an Azure Synapse pipeline for petabyte-scale data ingestion.
Lab: Ingest and load Data into the Data Warehouse
After completing this module, students will be able to:
This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.
Lab: Transform Data with Azure Data Factory or Azure Synapse Pipelines
After completing this module, students will be able to:
In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.
Lab: Orchestrate data movement and transformation in Azure Synapse Pipelines
After completing this module, students will be able to:
In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance.
Lab: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
After completing this module, students will be able to:
In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next, the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations.
Lab: Analyze and Optimize Data Warehouse Storage
After completing this module, students will be able to:
In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure the Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.
Lab: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
After completing this module, students will be able to:
In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.
Lab: End-to-end security with Azure Synapse Analytics
After completing this module, students will be able to:
In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real-time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.
Lab: Real-time Stream Processing with Stream Analytics
After completing this module, students will be able to:
In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.
Lab: Create a Stream Processing Solution with Event Hubs and Azure Databricks
After completing this module, students will be able to:
In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI.
Lab: Build reports using Power BI integration with Azure Synapase Analytics
After completing this module, students will be able to:
This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI.
Lab: Perform Integrated Machine Learning Processes in Azure Synapse Analytics
After completing this module, students will be able to:
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