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Certification BootCamp for Exam DP-203: Data Engineering on Microsoft Azure


Duration: 4 Days


The DP-203 Data Engineering on Microsoft Azure certification training course from CloudThat offers candidates proper training and relevant study material to prepare and successfully clear the DP-203 exam.

This exam measures candidate’s ability to accomplish the following technical tasks: design and implement data storage; design and develop data processing; design and implement data security; and monitor and optimize data storage and data processing.

Microsoft Certified: Azure Data Engineer Associate - Acclaim

An Azure Data Engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints. In Azure Data Engineer Associate track, there are two certifications namely DP-200 & DP-201 respectively. After attending both the trainings candidates can design and implement the management, monitoring, security and privacy of data using the full stack of Azure data services to satisfy business needs. However, soon both these exams

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After successful completion of this DP-203 exam training course, candidates will be able to:

  • Design and implement data storage
  • Design and develop data processing
  • Design and implement data security
  • Monitor and optimize data storage and data processing

Who Should Attend

Azure Data Engineers who integrate, transform, and consolidate data from various structured and unstructured data systems into structures that are suitable for building analytics solutions.


  • A candidate must have solid knowledge of data processing languages, such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.
  • Candidate should have subject matter expertise integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions.

Course Outline

Module 1: Design and Implement Data Storage  Design a data storage structure

  • Design an Azure Data Lake solution
  • Recommend file types for storage
  • Recommend file types for analytical queries
  • Design for efficient querying
  • Design for data pruning
  • Design a folder structure that represents the levels of data transformation
  • Design a distribution strategy
  • Design a data archiving solution
  • Design a partition strategy
  • Design a partition strategy for files
  • Design a partition strategy for analytical workloads
  • Design a partition strategy for efficiency/performance
  • Design a partition strategy for Azure Synapse Analytics
  • Identify when partitioning is needed in Azure Data Lake Storage Gen2

Design the serving layer

  • Design star schemas
  • Design slowly changing dimensions
  • Design a dimensional hierarchy
  • Design a solution for temporal data
  • Design for incremental loading
  • Design analytical stores
  • Design metastores in Azure Synapse Analytics and Azure Databricks

Implement physical data storage structures

  • Implement compression
  • Implement partitioning
  • Implement sharding
  • Implement different table geometries with Azure Synapse Analytics pools
  • Implement data redundancy
  • Implement distributions
  • Implement data archiving

Implement logical data structures

  • Build a temporal data solution
  • Build a slowly changing dimension
  • Build a logical folder structure
  • Build external tables
  • Implement file and folder structures for efficient querying and data pruning

Implement the serving layer

  • Deliver data in a relational star schema
  • Deliver data in Parquet files
  • Maintain metadata
  • Implement a dimensional hierarchy

Module 2: Design and Develop Data Processing Ingest and transform data

  • Transform data by using Apache Spark
  • Transform data by using Transact-SQL
  • Transform data by using Data Factory
  • Transform data by using Azure Synapse Pipelines
  • Transform data by using Stream Analytics
  • Cleanse data
  • Split data
  • Shred JSON
  • Encode and decode data
  • Configure error handling for the transformation
  • Normalize and denormalize values
  • Transform data by using Scala
  • Perform data exploratory analysis

Design and develop a batch processing solution

  • Develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks
  • Create data pipelines
  • Design and implement incremental data loads
  • Design and develop slowly changing dimensions
  • Handle security and compliance requirements
  • Scale resources
  • Configure the batch size
  • Design and create tests for data pipelines
  • Integrate Jupyter/IPython notebooks into a data pipeline
  • Handle duplicate data
  • Handle missing data
  • Handle late-arriving data
  • Upsert data
  • Regress to a previous state
  • Design and configure exception handling
  • Configure batch retention
  • Design a batch processing solution
  • Debug Spark jobs by using the Spark UI

Design and develop a stream processing solution

  • Develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
  • Process data by using Spark structured streaming
  • Monitor for performance and functional regressions
  • Design and create windowed aggregates
  • Handle schema drift
  • Process time series data
  • Process across partitions
  • Process within one partition
  • Configure checkpoints/watermarking during processing
  • Scale resources
  • Design and create tests for data pipelines
  • Optimize pipelines for analytical or transactional purposes
  • Handle interruptions
  • Design and configure exception handling
  • Upsert data
  • Replay archived stream data
  • Design a stream processing solution

Manage batches and pipelines

  • Trigger batches
  • Handle failed batch loads
  • Validate batch loads
  • Manage data pipelines in Data Factory/Synapse Pipelines
  • Schedule data pipelines in Data Factory/Synapse Pipelines
  • Implement version control for pipeline artifacts
  • Manage Spark jobs in a pipeline

Module 3: Design and Implement Data Security Design security for data policies and standards

  • Design data encryption for data at rest and in transit
  • Design a data auditing strategy
  • Design a data masking strategy
  • Design for data privacy
  • Design a data retention policy
  • Design to purge data based on business requirements
  • Design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2
  • Design row-level and column-level security

Implement data security

  • Implement data masking
  • Encrypt data at rest and in motion
  • Implement row-level and column-level security
  • Implement Azure RBAC
  • Implement POSIX-like ACLs for Data Lake Storage Gen2
  • Implement a data retention policy
  • Implement a data auditing strategy
  • Manage identities, keys, and secrets across different data platform technologies
  • 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

Module 4:Monitor and Optimize Data Storage and Data Processing  Monitor data storage and data processing

  • Implement logging used by Azure Monitor
  • Configure monitoring services
  • Measure performance of data movement
  • Monitor and update statistics about data across a system
  • Monitor data pipeline performance
  • Measure query performance
  • Monitor cluster performance
  • Understand custom logging options
  • Schedule and monitor pipeline tests
  • Interpret Azure Monitor metrics and logs
  • Interpret a Spark directed acyclic graph (DAG)

Optimize and troubleshoot data storage and data processing

  • Compact small files
  • Rewrite user-defined functions (UDFs)
  • Handle skew in data
  • Handle data spill
  • Tune shuffle partitions
  • Find shuffling in a pipeline
  • Optimize resource management
  • Tune queries by using indexers
  • Tune queries by using cache
  • Optimize pipelines for analytical or transactional purposes
  • Optimize pipeline for descriptive versus analytical workloads
  • Troubleshoot a failed spark job
  • Troubleshoot a failed pipeline run

About the Trainer

A Certified Microsoft Azure Trainer.

Other Details

For latest batch dates, fees, location, technical queries and general inquiries, contact our sales team at: +91 8880002200 or email at

Upcoming Batches

India Online Enroll
  • 9 Aug 2021 - 12 Aug 2021 (Mon - Thu)
  • 6 Sep 2021 - 9 Sep 2021 (Mon - Thu)
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