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Data Engineering on Microsoft Azure (DP-203T00) Certification Training
The instructor-led DP-203T00: Data Engineering on Microsoft Azure training in Qatar provides professionals with expertise to develop and maintain data solutions built on Microsoft Azure platforms. The course includes essential data principles about storage and processing and security as well as A
Data Engineering on Microsoft Azure (DP-203T00) Certification
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Course Overview
The DP-203T00: Data Engineering on Microsoft Azure training in Qatar empower learners with the skills required to construct and deploy data solutions through Microsoft Azure platform infrastructure. Students learn Azure data processing pipeline management as well as storage system optimization and data security maintenance in Azure platforms through this course curriculum.
The program enables participants to handle the required skills for working with Azure Synapse Analytics and Azure Data Lake and Azure Data Factory along with real-time data streaming solutions. Learners learn data ingestion and transformation techniques and orchestration methods that cover batch and real-time data processing in the course program. Standard operating procedures along with compliance requirements and performance enhancement techniques for cloud-based data solutions make up the core curriculum of the program.
Learners also learn to combine various data sources effectively for building scalable data architecture systems that demonstrate resistance to failures. Training provides three core lessons about boosting Azure Apache Spark speed and implementing encryption technologies and automating Azure Data Factory pipelines.
Through its certification preparation program, the course provides Azure data solution knowledge to data engineers and IT professionals and analysts. The program benefits professionals seeking advancement in their cloud-based data engineering career by teaching them to construct enterprise-level data platforms.
Microsoft Azure’s data ecosystem becomes fully understandable to trained professionals who achieve this training to develop next-generation business data solutions that operate at high performance while providing security and scalability.
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Course Objectives
- Explore the core principles of Microsoft Azure data engineering, covering storage, processing, and security aspects.
- Learn to create and manage data pipelines using Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake.
- Build skills in handling both batch and real-time data processing with Apache Spark and Azure Stream Analytics.
- Apply data transformation methods for structured and unstructured data across multiple Azure platforms.
- Enhance data storage efficiency, retrieval speed, and performance optimization using Azure SQL, Cosmos DB, and other storage options.
- Implement encryption, access control, and monitoring strategies to maintain data security, compliance, and governance.
- Automate data processing workflows with Azure Logic Apps, Data Factory pipelines, and Power BI integration for analytics.
- Connect data solutions with cloud-based applications to support scalable and enterprise-level architectures.
- Gain practical experience through hands-on labs, industry-based projects, and real-world case studies in Azure data engineering.
- Get ready for the DP-203 certification by mastering the design and implementation of Azure data solutions.
Audience
- IT Professionals
- Database Administrators
- Cloud Engineers
- Data Engineers
- Software Developers
- Business Intelligence Professionals
- Machine Learning Engineers
- Solution Architects
- Technical Consultants
- Data Analysts
Prerequisite
Recommended:
- AZ-900T00: Microsoft Azure Fundamentals
- DP-900T00: Microsoft Azure Data Fundamentals
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
Course Outline
Introduction to Data Engineering on Azure
- Understand key data engineering concepts and their role in modern data solutions
- Identify common data engineering tasks and best practices
- Explore Azure services designed for data engineering
Azure Data Lake Storage Gen2 Overview
- Learn the features, benefits, and use cases of Azure Data Lake Storage Gen2
- Configure and enable Azure Data Lake Storage Gen2 in an Azure Storage account
- Compare Azure Data Lake Storage Gen2 with Azure Blob Storage
- Understand its role in analytical processing and data workflows
- Explore real-world applications of Azure Data Lake Storage Gen2 in analytical workloads
Overview of Azure Synapse Analytics
- Identify business challenges addressed by Azure Synapse Analytics
- Explore its core features and capabilities for data integration and analytics
- Determine when to implement Azure Synapse Analytics in data solutions
Building Data Analytics Solutions Using Azure Synapse Serverless SQL Pools
- Querying Data in a Data Lake with Serverless SQL Pools
- Understand the capabilities and use cases of serverless SQL pools in Azure Synapse Analytics
- Query data stored in CSV, JSON, and Parquet files using serverless SQL pools
- Create external database objects for querying data in a serverless SQL pool
Transforming Data in a Data Lake with Serverless SQL Pools
- Utilize the CREATE EXTERNAL TABLE AS SELECT (CETAS) statement for data transformation
- Encapsulate CETAS statements within stored procedures for automated processing
- Integrate stored procedures into data pipelines for streamlined transformations
Creating and Managing a Lake Database in Azure Synapse Analytics
- Understand the key concepts and components of lake databases
- Explore database templates in Azure Synapse Analytics
- Create and configure a lake database for data management
Securing Data and Managing Users in Serverless SQL Pools
- Select appropriate authentication methods for Azure Synapse serverless SQL pools
- Manage user access and permissions for secure data operations
- Implement best practices for security and compliance in serverless SQL environments
- Performing Data Engineering with Azure Synapse Apache Spark Pools
Data Analysis with Apache Spark in Azure Synapse Analytics
- Understand core features and capabilities of Apache Spark
- Configure and manage a Spark pool in Azure Synapse Analytics
- Load, analyze, and visualize data using Spark notebooks
Data Transformation with Spark in Azure Synapse Analytics
- Modify and save DataFrames using Apache Spark
- Optimize performance by partitioning data files
- Transform data using SQL within Spark environments
Using Delta Lake in Azure Synapse Analytics
- Explore the key features and benefits of Delta Lake
- Create and manage Delta Lake tables in a Synapse Analytics Spark pool
- Register Delta Lake tables in the Spark catalog for streamlined access
- Enable real-time processing with Delta Lake tables for streaming data
- Query Delta Lake tables from an Azure Synapse Analytics SQL pool
Transferring and Transforming Data with Azure Synapse Analytics Pipelines
- Building Data Pipelines in Azure Synapse Analytics
- Understand core concepts of Azure Synapse Analytics pipelines
- Create and configure a pipeline using Azure Synapse Studio
- Implement data flow activities within pipelines
- Execute and monitor pipeline runs for data processing
Integrating Spark Notebooks with Azure Synapse Pipelines
- Explore notebook and pipeline integration concepts
- Implement Synapse notebook activities within a pipeline
- Use parameters to customize and enhance notebook activities
Implementing a Data Analytics Solution with Azure Synapse Analytics
- Introduction to Azure Synapse Analytics
- Identify key business problems addressed by Azure Synapse Analytics
- Explore core capabilities and features of Azure Synapse Analytics
- Determine the appropriate scenarios for using Azure Synapse Analytics
Querying Data Lake Files Using Azure Synapse Serverless SQL Pool
- Understand the capabilities and use cases of serverless SQL pools
- Query structured and semi-structured files (CSV, JSON, Parquet) using SQL
- Create external database objects for optimized query execution
Analyzing Data with Apache Spark in Azure Synapse Analytics
- Learn key features and functionalities of Apache Spark
- Configure and manage Spark pools within Azure Synapse Analytics
- Execute Spark notebooks to load, analyze, and visualize data
Leveraging Delta Lake in Azure Synapse Analytics
- Explore the core concepts and benefits of Delta Lake
- Create and manage Delta Lake tables within a Synapse Analytics Spark pool
- Register Spark catalog tables for efficient data access
- Utilize Delta Lake for real-time streaming data processing
- Query Delta Lake tables using Synapse Analytics SQL pools
Analyzing Data in a Relational Data Warehouse
- Design schema structures for relational data warehouses
- Develop fact, dimension, and staging tables for optimized storage
- Load data into relational warehouse tables using SQL
- Perform SQL-based queries to extract insights from relational data
Building Data Pipelines in Azure Synapse Analytics
- Understand essential concepts of Azure Synapse Analytics pipelines
- Design and implement pipelines using Azure Synapse Studio
- Incorporate data flow activities for transformation processes
- Execute, monitor, and optimize pipeline runs for data movement and transformation
Integrating Spark Notebooks with Azure Synapse Pipelines
- Explore notebook and pipeline integration concepts
- Implement Synapse notebook activities within a pipeline
- Use parameters to customize and enhance notebook activities
Implementing a Data Analytics Solution with Azure Synapse Analytics
- Introduction to Azure Synapse Analytics
- Identify key business problems addressed by Azure Synapse Analytics
- Explore core capabilities and features of Azure Synapse Analytics
- Determine the appropriate scenarios for using Azure Synapse Analytics
Querying Data Lake Files Using Azure Synapse Serverless SQL Pool
- Understand the capabilities and use cases of serverless SQL pools
- Query structured and semi-structured files (CSV, JSON, Parquet) using SQL
- Create external database objects for optimized query execution
Analyzing Data with Apache Spark in Azure Synapse Analytics
- Learn key features and functionalities of Apache Spark
- Configure and manage Spark pools within Azure Synapse Analytics
- Execute Spark notebooks to load, analyze, and visualize data
Leveraging Delta Lake in Azure Synapse Analytics
- Explore the core concepts and benefits of Delta Lake
- Create and manage Delta Lake tables within a Synapse Analytics Spark pool
- Register Spark catalog tables for efficient data access
- Utilize Delta Lake for real-time streaming data processing
- Query Delta Lake tables using Synapse Analytics SQL pools
Analyzing Data in a Relational Data Warehouse
- Design schema structures for relational data warehouses
- Develop fact, dimension, and staging tables for optimized storage
- Load data into relational warehouse tables using SQL
- Perform SQL-based queries to extract insights from relational data
Building Data Pipelines in Azure Synapse Analytics
- Understand essential concepts of Azure Synapse Analytics pipelines
- Design and implement pipelines using Azure Synapse Studio
- Incorporate data flow activities for transformation processes
- Execute, monitor, and optimize pipeline runs for data movement and transformation
Working with Data Warehouses Using Azure Synapse Analytics
- Analyzing Data in a Relational Data Warehouse
- Design schemas optimized for data warehousing
- Create and manage fact, dimension, and staging tables
- Load data into warehouse tables using SQL
- Execute SQL queries for data analysis
Loading Data into a Relational Data Warehouse
- Populate staging tables before transformation
- Load data into dimension tables
- Manage time dimensions effectively
- Implement Slowly Changing Dimensions (SCD) strategies
- Load fact tables with structured data
- Perform post-load performance optimizations
Managing and Monitoring Data Warehouse Activities
- Scale compute resources dynamically
- Pause and resume compute operations
- Manage workloads for optimized performance
- Use Azure Advisor for performance recommendations
- Leverage Dynamic Management Views (DMVs) for troubleshooting
Securing a Data Warehouse in Azure Synapse Analytics
- Implement network security configurations
- Set up Conditional Access policies
- Configure authentication mechanisms
- Apply column- and row-level security
- Use Dynamic Data Masking for sensitive information
- Implement encryption for enhanced security
Hybrid Transactional and Analytical Processing (HTAP) with Azure Synapse Analytics
- Designing Hybrid Transactional and Analytical Workflows
- Explore methodologies for integrating transactional and analytical processing
- Identify and utilize Azure Synapse Link services for hybrid data solutions
Configuring Azure Synapse Link for Azure Cosmos DB
- Set up Azure Synapse Link within a Cosmos DB account
- Enable analytical storage for seamless data processing
- Establish a linked service for Cosmos DB integration
- Perform data analysis using Apache Spark
- Query integrated datasets using Synapse SQL
Enabling Azure Synapse Link for SQL-Based Solutions
- Understand core features and benefits of Synapse Link for SQL environments
- Configure Synapse Link for Azure SQL Database connectivity
- Set up and integrate Synapse Link with Microsoft SQL Server
Developing a Real-Time Data Streaming Solution with Azure Stream Analytics
- Introduction to Azure Stream Analytics
- Gain insights into data streaming concepts
- Explore event-driven data processing mechanisms
- Understand the role of windowing functions in stream processing
- Set up and configure Azure Stream Analytics
Capturing and Processing Streaming Data in Azure Synapse Analytics
- Examine key use cases for real-time data ingestion in Azure Synapse
- Define and configure input and output sources for stream analytics jobs
- Write queries to capture and process continuous data streams in Synapse Analytics
- Execute streaming jobs and utilize real-time data in Azure Synapse
Building Real-Time Data Visualizations with Power BI
- Establish Power BI as an output destination for Stream Analytics
- Develop Stream Analytics queries to feed real-time data into Power BI
- Design interactive, live dashboards for visualizing streaming data
Enterprise Data Governance with Microsoft Purview
- Understanding Microsoft Purview for Data Management
- Assess whether Microsoft Purview aligns with your data governance and discovery needs
- Explore key functionalities of Microsoft Purview in managing enterprise data
Ensuring Data Reliability with Microsoft Purview
- Navigate, search, and oversee assets in the data catalog
- Integrate data catalog assets with Power BI for enhanced insights
- Utilize Microsoft Purview within Azure Synapse Studio for seamless data management
Organizing Data Assets with Microsoft Purview
- Understand asset classification and organization in Microsoft Purview
Managing Power BI Resources with Microsoft Purview
- Register and scan Power BI tenants for governance
- Utilize search and navigation features to locate data assets
- Explore schema structures and track data lineage for Power BI datasets
Integrating Microsoft Purview with Azure Synapse Analytics
- Register Azure Synapse Analytics database assets in Microsoft Purview
- Enable and configure integration between Microsoft Purview and Azure Synapse
- Perform searches within the Purview catalog directly from Synapse Studio
- Monitor and track data flow across Azure Synapse Analytics pipeline activities
About The Certification
This certification is tailored for professionals proficient in developing data analytics solutions by integrating and transforming data from diverse sources. It is well-suited for data engineers, business intelligence professionals, and data architects. The certification validates expertise in merging, transforming, and structuring data from structured, unstructured, and streaming sources into a suitable format for analytics.
As an Azure Data Engineer, you play a crucial role in assisting stakeholders with data exploration while designing and maintaining secure, compliant data pipelines. You utilize various Azure data services to store, cleanse, and refine datasets, ensuring they are optimized for analysis. Depending on business requirements, data architectures may include:
- Modern Data Warehouse (MDW)
- Big Data Solutions
- Lakehouse Architecture
In addition to data processing, you focus on ensuring that pipelines and storage solutions are efficient, scalable, and reliable. Key responsibilities include troubleshooting operational challenges, enhancing data quality, and optimizing performance.
Required Skills and Knowledge:
Candidates should possess a strong proficiency in data processing languages, including:
- SQL
- Python
- Scala
Moreover, a solid understanding of parallel processing and data architecture patterns is crucial. This certification validates expertise in designing and deploying solutions using:
- Azure Data Factory
- Azure Synapse Analytics
- Azure Stream Analytics
- Azure Event Hubs
- Azure Data Lake Storage
- Azure Databricks
Skills Assessed:
- Design and implement data storage (15–20%)
- Develop data processing solutions (40–45%)
- Secure, monitor, and optimize data storage and processing (30–35%)
Exam Details:
- Passing Score: 700 or higher
- Duration: 120 minutes
Certification Validity & Renewal
Previously, Microsoft role-based and specialty certifications had a validity of two years. However, since June 2021:
Certifications are now valid for one year.
- Renewal is free and can be done online through Microsoft Learn.
- The renewal window opens six months before the certification expires.
- Certifications obtained before June 2021 remain valid for two years and can be renewed under the updated policy.
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FAQ’s
What is the Microsoft Certified: Azure Data Engineer Associate certification?
This certification validates expertise in designing, implementing, and managing data solutions using Microsoft Azure. It is ideal for professionals working with structured and unstructured data, data transformation, and analytics. It demonstrates proficiency in integrating, securing, and optimizing data pipelines for scalable business insights.
Are there any prerequisites for this certification?
While there are no mandatory prerequisites, candidates should have a solid understanding of data processing languages like SQL, Python, or Scala. Experience with cloud data solutions, data storage, and analytics using Azure services such as Azure Synapse Analytics and Data Factory is highly recommended.
Which exam is required to obtain this certification?
Candidates must pass the DP-203: Data Engineering on Microsoft Azure exam, which evaluates skills in data integration, transformation, security, and performance optimization across Azure’s data ecosystem.
How long does the DP-203 exam take?
The exam duration is 120 minutes, consisting of multiple-choice questions, scenario-based problems, and hands-on assessments. It measures both theoretical knowledge and practical application of Azure data engineering concepts.
What topics are covered in the Azure Data Engineer certification?
The certification focuses on:
- Designing and implementing data storage solutions
- Developing and managing data processing using Azure Data Factory and Databricks
- Optimizing performance and security in data processing pipelines
- Managing structured, unstructured, and streaming data sources
Who should pursue this certification?
This certification is best suited for data engineers, business intelligence developers, data architects, and analytics professionals who want to build and optimize large-scale data solutions using Azure’s cloud infrastructure.
Is the DP-203 exam open book?
No, the exam is proctored and closed book. Candidates are expected to apply their knowledge in real-world scenarios without external resources. Hands-on experience with Azure data services is crucial for success.
What is the passing score for the DP-203 exam?
The minimum passing score is 700 out of 1000. Microsoft evaluates task accuracy, efficiency, and the ability to design effective data solutions.
In which languages is the DP-203 exam available?
The exam is offered in English, Japanese, Chinese (Simplified), Korean, French, German, and Spanish. Candidates should check the availability of their preferred language before registering.
Will this course help me prepare for the DP-203 exam?
Yes, this course is designed to align with DP-203 exam objectives, covering practical applications, real-world projects, and interactive exercises. Additional study resources, practice tests, and hands-on experience with Azure data services will improve exam readiness.
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Reviews
Interactive and Practical Learning! This program is ideal for professionals seeking expertise in data engineering using Microsoft Azure. It offers a structured approach to essential areas such as data storage, transformation, and security. Hands-on projects, real-world case studies, and engaging discussions enhance understanding. The emphasis on performance tuning, data governance, and scalable architecture makes this course highly beneficial.
Urvashi KhareProject Manager
Well-Structured and In-Depth Training! Covering topics from data ingestion and transformation to workflow automation, this course equips learners with critical Azure data engineering skills. Practical exercises, instructor-led assignments, and live discussions help professionals confidently develop and deploy data solutions. Detailed insights into Azure Data Factory, Synapse Analytics, and Databricks provide the knowledge needed to build efficient data systems.
Kuldeep SighData Analyst
Essential for Data Experts! This training delivers valuable insights into building and managing enterprise-level data solutions on Azure. Participants gain hands-on experience integrating structured, semi-structured, and streaming data while maintaining security and compliance. The course also covers advanced troubleshooting techniques, performance improvements, and best practices for handling large-scale data environments.
Radhey ShamSenior Project Manager
Comprehensive and Industry-Focused Course! With a strong emphasis on data engineering fundamentals, security, and optimization, this training is perfect for data architects, engineers, and analysts. Led by experienced professionals, it simplifies complex topics, making it easier to implement robust data pipelines. The structured approach to real-time data processing, automation, and cloud-based integration provides a solid foundation for designing and maintaining high-performance data solutions on Azure.
Tejay ReddyData Scientist
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