DP-203T00: Data Engineering on Microsoft Azure

Prepare to pass the Azure Data Scientist Associate Certification Exam in an immersive 4-day course.

Web 1920 – 86-min

Course Description

In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.

Who Should Attend?

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 includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.


About this course

Course Outline

  • Design and implement data storage (15–20%)

  • Develop data processing (40–45%)

  • Secure, monitor, and optimize data storage and data processing (30–35%)

Design and implement data storage (15–20%)

Implement a partition strategy

  • Implement a partition strategy for files

  • Implement a partition strategy for analytical workloads

  • Implement a partition strategy for streaming workloads

  • Implement a partition strategy for Azure Synapse Analytics

  • Identify when partitioning is needed in Azure Data Lake Storage Gen2

Design and implement the data exploration layer

  • Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster

  • Recommend and implement Azure Synapse Analytics database templates

  • Push new or updated data lineage to Microsoft Purview

  • Browse and search metadata in Microsoft Purview Data Catalog

Develop data processing (40–45%)

Ingest and transform data

  • Design and implement incremental loads

  • Transform data by using Apache Spark

  • Transform data by using Transact-SQL (T-SQL)

  • Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory

  • Transform data by using Azure Stream Analytics

  • Cleanse data

  • Handle duplicate data

  • Handle missing data

  • Handle late-arriving data

  • Split data

  • Shred JSON

  • Encode and decode data

  • Configure error handling for a transformation

  • Normalize and denormalize data

  • Perform data exploratory analysis

Develop a batch processing solution

  • Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory

  • Use PolyBase to load data to a SQL pool

  • Implement Azure Synapse Link and query the replicated data

  • Create data pipelines

  • Scale resources

  • Configure the batch size

  • Create tests for data pipelines

  • Integrate Jupyter or Python notebooks into a data pipeline

  • Upsert data

  • Revert data to a previous state

  • Configure exception handling

  • Configure batch retention

  • Read from and write to a delta lake

Develop a stream processing solution

  • Create a stream processing solution by using Stream Analytics and Azure Event Hubs

  • Process data by using Spark structured streaming

  • Create windowed aggregates

  • Handle schema drift

  • Process time series data

  • Process data across partitions

  • Process within one partition

  • Configure checkpoints and watermarking during processing

  • Scale resources

  • Create tests for data pipelines

  • Optimize pipelines for analytical or transactional purposes

  • Handle interruptions

  • Configure exception handling

  • Upsert data

  • Replay archived stream data

Manage batches and pipelines

  • Trigger batches

  • Handle failed batch loads

  • Validate batch loads

  • Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines

  • Schedule data pipelines in Data Factory or Azure Synapse Pipelines

  • Implement version control for pipeline artifacts

  • Manage Spark jobs in a pipeline

Secure, monitor, and optimize data storage and data processing (30–35%)

Implement data security

  • Implement data masking

  • Encrypt data at rest and in motion

  • Implement row-level and column-level security

  • Implement Azure role-based access control (RBAC)

  • Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2

  • Implement a data retention policy

  • Implement secure endpoints (private and public)

  • Implement resource tokens in Azure Databricks

  • Load a DataFrame with sensitive information

  • Write encrypted data to tables or Parquet files

  • Manage sensitive information

Monitor data storage and data processing

  • Implement logging used by Azure Monitor

  • Configure monitoring services

  • Monitor stream processing

  • Measure performance of data movement

  • Monitor and update statistics about data across a system

  • Monitor data pipeline performance

  • Measure query performance

  • Schedule and monitor pipeline tests

  • Interpret Azure Monitor metrics and logs

  • Implement a pipeline alert strategy

Optimize and troubleshoot data storage and data processing

  • Compact small files

  • Handle skew in data

  • Handle data spill

  • Optimize resource management

  • Tune queries by using indexers

  • Tune queries by using cache

  • Troubleshoot a failed Spark job

  • Troubleshoot a failed pipeline run, including activities executed in external services


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


This will be a virtual event hosted on Microsoft Teams. In the Microsoft Teams platform and sessions, your name, email address, or title may be viewable by other participants. By joining this event, you agree to this experience. 

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