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Microsoft Certified Azure Data Engineer (DP-203)

Excel in Azure Data Management and gain expertise in designing, implementing and managing data solutions in the Cloud for optimal performance and insights.

course: Microsoft Certified Azure Data Engineer (DP-203)

Duration: 4 days

Format: Virtual or Classroom

prepare-exam Prepares for Exam : Data Engineering on Microsoft Azure (DP-203)

certification-icon Prepares for Certification : Microsoft Certified: Azure Data Engineer Associate

ktk-icon Attend this and 60+ other Microsoft courses for FREE with Unlimited Microsoft Training

Overview

Master Azure Data Engineering with our specialized training course. Learn to design and implement data solutions using Azure data services. From data ingestion to data transformation, this course covers all aspects of Azure data engineering. With hands-on labs and expert-led instruction, you'll gain the skills needed to pass the DP-203 exam and become certified as a Microsoft Certified Azure Data Engineer. Enroll now and become an expert in Azure data engineering with our comprehensive training and certification course.

This course includes
  • intructor-iconInstructor-led training
  • intructor-iconPractice test
  • intructor-iconPre-reading
  • intructor-iconPersonal Learning Path
  • intructor-iconCertification Guarantee
  • intructor-iconEmail, chat and phone support

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Who is this course for?

Who is the Microsoft Certified Azure Data (DP-203) Training Course for?

The Microsoft Certified Azure Data Engineer (DP-203) certification is designed for professionals who design and implement the management, monitoring, security, and privacy of data using the Azure data services. The certification is ideal for data engineers, data architects, and database administrators who want to validate their skills and knowledge in this area. The DP-203 exam covers various topics related to Azure data services, such as designing and implementing data storage solutions, designing and implementing data processing solutions, designing and implementing data security and compliance, and monitoring and optimizing data solutions. The exam also covers topics related to designing and implementing data integration and data migration solutions, using Azure Synapse Analytics and Azure Databricks, and working with Power BI and Azure Analysis Services.

Curriculum

What you will learn during our DP-203 Microsoft Certified Azure Data Engineer course.

  • 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 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 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 compression
  • Implement partitioning
  • Implement sharding
  • Implement different table geometries with Azure Synapse Analytics pools
  • Implement data redundancy
  • Implement distributions
  • Implement data archiving
  • 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
  • Deliver data in a relational star schema
  • Deliver data in Parquet files
  • Maintain metadata
  • Implement a dimensional hierarchy
  • Ingest and transform data
  • Transform data by using Apache Spark
  • Transform data by using TransactSQL
  • 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
  • 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 latearriving 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
  • 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
  • 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
  • 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 rolebased access control (Azure RBAC) and POSIXlike Access Control List (ACL) for Data Lake Storage Gen2
  • Design rowlevel and columnlevel security
  • Implement data masking
  • Encrypt data at rest and in motion
  • Implement rowlevel and columnlevel security
  • Implement Azure RBAC
  • Implement POSIXlike 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
  • 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)
  • Compact small files
  • Rewrite userdefined 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

Preparation

How to best be prepared for our DP-203 Microsoft Certified Azure Data Engineer course.

  • [Dictionary item: Orange-check] Fundamental understanding of Microsoft Azure and its data services, such as Azure SQL Database, Azure Cosmos DB, Azure Data Lake Storage, and Azure Synapse Analytics.
  • [Dictionary item: Orange-check] Proficiency in using SQL for querying and manipulating data.
  • [Dictionary item: Orange-check] Familiarity with data integration techniques and technologies, including ETL (Extract, Transform, Load) processes.
  • [Dictionary item: Orange-check] Experience in designing and implementing data storage solutions, including relational and non-relational databases.
  • [Dictionary item: Orange-check] Knowledge of big data concepts and technologies, such as Apache Hadoop and Apache Spark.
  • [Dictionary item: Orange-check] Understanding of data warehousing principles and architectures.
  • [Dictionary item: Orange-check] Proficiency in data modeling and schema design.
  • [Dictionary item: Orange-check] Experience in developing and deploying data pipelines and workflows.
  • [Dictionary item: Orange-check] Familiarity with data governance, security, and compliance requirements.
  • [Dictionary item: Orange-check] Strong problem-solving and analytical skills.
  • [Dictionary item: Orange-check] Excellent communication and collaboration skills.
  • [Dictionary item: Orange-check] Completion of relevant training or certification courses on Azure fundamentals and data engineering is recommended but not required.

Meet our instructors

Meet some of the Readynez Instructors you can meet on your course. They are experts, passionate about what they do, and dedicated to give back to their industry, their field, and those who want to learn, explore, and advance in their careers.

tiago-costa

Tiago Costa

Tiago Costa is Microsoft MVP and a Cloud Architect and Advisor and International speaker on the Microsoft Cloud.

Tiago Costa is a Cloud Architect and Advisor on Microsoft Azure. For the past years he has been architecting and developing solutions using Microsoft Azure for some of the fortune 500 companies.

Due to his strong real-world experience, Tiago regularly teaches Microsoft Azure classes around the world. Microsoft Azure MVP since 2016 for his community efforts in promoting and sharing knowledge with the community.

Tiago has 40+ Microsoft Certifications including MCT – Microsoft Certified Trainer - and got awarded as MCT Regional Lead every year since 2016. He’s the founder of the Azure Portugal User Group. He loves diving into new technologies and to share his experience at conferences and training classes, but what excites him about his work is to help others exploring new frontiers in technology impacting their life’s, making the world a better place for everyone. His free time is spent with his family in the sunny Lisbon, Portugal building cool and geek projects

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tiago-costa

Ed Baker

Ed is a Microsoft MVP for many years and has authored several Microsoft Official Curriculum Courses for Azure, Windows Server and Windows.

He is a 25 year IT veteran with experience in the UK Police and armed forces. Ed is a former Microsoft Technical Evangelist and is an MCT Regional Lead for the UK and he currently holds the most prestigious Microsoft MVP accreditation. Ed also helps run the UK MS Cloud User Group and the annual Evolve Conference in the UK.

Ed has authored several Microsoft Official Curriculum Courses for Azure, Windows Server and Windows as well as writing courses for Opsgility and other online providers.
Ed is a regular conference speaker at events such as TechEd, Ignite, TechSummit, SpiceWorld and more.

Ed now spends his time mixed between teaching Microsoft Azure, Windows Server and Enterprise Mobility topics. In addition Ed provides consultancy services to Small and Medium Enterprises as well as direct to Microsoft UK and Microsoft Corp.

 

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FAQ

FAQs for the Microsoft Certified Azure Data Engineer (DP-203) course.

Microsoft Certified Azure Data Engineer certification validates expertise in designing and implementing data solutions using Azure data services. It demonstrates proficiency in building and managing data pipelines, data storage solutions, and data processing architectures on the Azure platform.

Readynez's DP-203 Microsoft Certified Azure Data Engineer course is your gateway to mastering data engineering on Azure. Prepare for the exam and become certified as an Azure Data Engineer with our comprehensive training program. Gain the skills and knowledge needed to design and implement data solutions that drive business insights and innovation.

Prerequisites for Microsoft Certified Azure Data Engineer certification include fundamental knowledge of Azure services, data engineering concepts, and experience with data processing technologies. While there are no mandatory prerequisites, having hands-on experience with data engineering tasks and familiarity with Azure data services is beneficial for exam preparation.

The cost of the DP-203 exam €142.

The DP-203 exam covers topics such as designing data storage solutions, implementing data processing solutions, building data pipelines, managing data security and compliance, and optimizing data performance on the Azure platform. It assesses candidates' abilities to design and implement end-to-end data solutions using Azure data services effectively.

Yes, Microsoft Certified Azure Data Engineer certification is valuable for professionals seeking to specialize in designing and implementing data solutions on the Azure platform. It demonstrates expertise in building scalable and reliable data architectures, performing data integration and transformation, and implementing advanced analytics solutions, opening up career opportunities in data engineering and analytics roles.

The time it takes to become Microsoft Certified Azure Data Engineer certified varies depending on individual experience, study habits, and dedication. Typically, candidates spend several weeks to a few months preparing for the DP-203 exam by studying relevant materials and gaining hands-on experience with Azure data services.

Yes, the DP-203 exam can be taken online from the comfort of your own home or office. Microsoft offers online proctoring options for many of its certification exams, including the DP-203 exam. Ensure your computer meets the technical requirements and follow the registration process to schedule your online exam session.

The difficulty of passing the DP-203 exam varies based on individual experience, preparation, and familiarity with Azure data services and data engineering concepts. Candidates with a solid understanding of designing and implementing data solutions using Azure services, combined with effective study strategies, are more likely to succeed in passing the exam.

The passing score is typically around 700 out of 1000.

Maintain your Microsoft Certified Azure Data Engineer certification by staying updated with the latest Azure data services, updates, and best practices for data engineering. Engage in continuous learning through relevant training programs, webinars, and community forums. Additionally, complete recertification requirements as outlined by Microsoft to ensure your certification remains current.

Salary potential after obtaining Microsoft Certified Azure Data Engineer certification varies depending on factors such as location, industry, experience, and job role. Professionals with expertise in designing and implementing data solutions using Azure data services can command competitive salaries in data engineering and analytics roles, offering excellent earning potential.

Reviews

Feedback from our Azure Data Engineer (DP-203) delegates.

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Thomas Persson

The instrcutor was brilliant. The best I've taken courses from, both in classrom and on-line.

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Montserrat Peidro

I am really enjoying the training. The Readynez trainer is fun, clear and gives lots of examples that make the subject easy to follow and understand. The mix of explanation, slides, video and self-paced tutorial is perfect.

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