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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 India delivers expertise to professionals who build and sustain data solutions on Microsoft Azure platforms. This course teaches fundamental data concepts which include storage and processing and security alongside Azur
Data Engineering on Microsoft Azure (DP-203T00) Certification
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Course Overview
The DP-203T00: Data Engineering on Microsoft Azure training in India focuses on building and implementing data solutions through Microsoft Azure platforms. The curriculum of this course teaches students how to handle Azure data processing pipelines and optimize storage systems and maintain data security within Azure platforms.
Through the program participants will master the skills required to work with Azure Synapse Analytics and Azure Data Lake and Azure Data Factory and real-time data streaming solutions. The educational program teaches students about data ingestion and transformation and orchestration skills that include both batch and real-time data processing approaches. The program teaches standard operating procedures for data governance alongside compliance requirements and performance enhancement techniques in cloud-based data solutions.
This training teaches students to efficiently merge different types of data sources into their data architecture designs that scale and resist failure. The training covers three essential concepts which involve maximizing Azure Apache Spark performance alongside encryption protocols and Azure Data Factory pipeline automation.
The program delivers Azure data solution expertise to data engineers and IT professionals and analysts through preparation for the DP-203 certification examination. The program suits professionals who want to move up in their cloud-based data engineering field through learning how to build enterprise-level data platforms.
Microsoft Azure’s data ecosystem will become fully understood by professionals who complete this training which enables them to create modern business data solutions that perform at high levels with security and scalability.
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Course Objectives
- Understand the fundamental concepts of Microsoft Azure data engineering, including data storage, processing, and security.
- Learn to design and implement data pipelines using Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake.
- Develop expertise in batch and real-time data processing with Apache Spark and Azure Stream Analytics.
- Implement data transformation techniques for structured and unstructured data across various Azure services.
- Optimize data storage, retrieval, and performance tuning using Azure SQL, Cosmos DB, and other Azure storage solutions.
- Ensure data security, compliance, and governance by applying encryption, access control, and monitoring best practices.
- Automate data workflows using Azure Logic Apps, Data Factory pipelines, and integration with Power BI for analytics.
- Integrate data solutions with cloud applications to enable scalable, enterprise-grade data architectures.
- Gain hands-on experience through practical labs, real-world projects, and case studies in Azure data engineering.
- Prepare for the DP-203 certification exam, demonstrating proficiency in designing and implementing Azure data solutions.
Audience
- Data Engineers
- Software Developers
- Business Intelligence Professionals
- Machine Learning Engineers
- Solution Architects
- Technical Consultants
- Data Analysts
- IT Professionals
- Database Administrators
- Cloud Engineers
Prerequisite
Recommended:
- AI+ Executive™
- AI+ Prompt Engineer™: Level 1
- AZ-900T00: Microsoft Azure Fundamentals
- DP-900T00: Microsoft Azure Data Fundamentals
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
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 designed for professionals skilled in developing data analytics solutions by integrating and transforming data from multiple sources. It is ideal for data engineers, business intelligence specialists, and data architects. This certification validates expertise in integrating, transforming, and consolidating data from structured, unstructured, and streaming sources into a suitable schema for analytics solutions.
As an Azure Data Engineer, your role involves helping stakeholders explore data while building and maintaining secure, compliant data pipelines. You leverage various Azure data services to store, cleanse, and enhance datasets, enabling effective analysis. Depending on business needs, data architectures may include:
- Modern Data Warehouse (MDW)
- Big Data Solutions
- Lakehouse Architecture
Beyond data processing, you also ensure that pipelines and storage solutions are efficient, scalable, and reliable. Troubleshooting operational issues, improving data quality, and optimizing performance are key aspects of your role.
Required Skills and Knowledge
Candidates should have a strong command of data processing languages, such as:
- SQL
- Python
- Scala
Additionally, familiarity with parallel processing and data architecture patterns is essential. The certification covers expertise in designing and implementing 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 were valid for two years. However, since June 2021:
- Certifications are valid for one year
- Renewal is free and online via Microsoft Learn
- The renewal period begins six months before expiration
- Certifications earned before June 2021 remain valid for two years and can be renewed under the new policy.
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FAQ’s
What is the Microsoft Certified Azure Data Engineer Associate credential?
This certification confirms expertise in creating and managing data solutions using Microsoft Azure. It is designed for professionals handling data storage, transformation, security, and analytics in cloud environments. Holding this certification demonstrates the ability to build and maintain scalable data pipelines on Azure.
Are there any prerequisites for earning this certification?
There are no strict prerequisites, but a background in SQL, Python, or Scala, along with experience in Azure Data Factory, Azure Synapse Analytics, and data integration frameworks, is recommended for a better understanding of the course.
Which exam is required to achieve this certification?
Candidates must clear the DP-203: Data Engineering on Microsoft Azure exam, which tests their skills in data ingestion, transformation, storage solutions, security measures, and real-time analytics within Azure.
How long does the DP-203 exam take?
The exam duration is 120 minutes, featuring multiple-choice questions, case studies, and scenario-based exercises that evaluate both theoretical understanding and practical application of Azure data engineering concepts.
What topics are included in the DP-203 certification?
The certification covers the following areas:
- Structuring and implementing Azure-based data storage
- Creating and maintaining data processing workflows
- Securing and ensuring compliance of cloud data
- Enhancing performance for batch and real-time data operations
Who should consider pursuing this certification?
This certification is ideal for data engineers, cloud specialists, database administrators, and analytics professionals working with large-scale data solutions. It benefits those responsible for ETL workflows, data pipeline management, and cloud-based analytical processes.
Is the DP-203 exam open book?
No, the exam is proctored and does not allow reference materials. Candidates need a thorough understanding of Azure data engineering tools and methodologies to succeed.
What is the required score to pass the DP-203 exam?
A minimum score of 700 out of 1000 is needed to pass. The evaluation is based on problem-solving skills, accuracy in designing data workflows, and proficiency in optimizing Azure-based solutions.
In what languages can the DP-203 exam be taken?
The exam is available in English, Japanese, Chinese (Simplified), Korean, French, German, and Spanish. Candidates should confirm language availability before scheduling.
Does this course effectively prepare candidates for the DP-203 exam?
Yes, the course is aligned with the exam structure and includes hands-on labs, real-world case studies, and interactive exercises. To enhance preparation, candidates are advised to use additional resources, practice tests, and gain hands-on experience with Azure services.
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Reviews
I recently completed the Data Engineering on Microsoft Azure (DP-203T00) Certification Training with Vinsys, and it has been a transformative experience for my career. The course provided a detailed introduction to Azure's data engineering tools and techniques, presented in a clear and engaging manner. The instructor was highly knowledgeable, simplifying complex concepts and providing practical insights that I can readily apply in my work. The training format included a mix of interactive discussions and hands-on exercises, allowing me to grasp the practical aspects of data engineering effectively. Additionally, the collaborative environment fostered networking opportunities with fellow participants, which was invaluable. I recommend this course to anyone looking to deepen their understanding of Azure data solutions or enhance their skills in data engineering.
Yavnav GurumaniData Scientist
Participating in the Data Engineering on Microsoft Azure Training offered by Vinsys was an eye-opening experience! The program laid a solid foundation in data engineering principles and Azure services, offering invaluable insights into real-world applications. The trainers were not only experts in their fields but also passionate about teaching. Their enthusiasm made the learning journey enjoyable and engaging. They took the time to ensure that every participant understood the concepts before progressing, which was greatly appreciated. The hands-on labs and case studies provided immediate applications of the theories, reinforcing the lessons learned. Moreover, the course materials and resources shared were a great asset for future reference. This training is ideal for anyone looking to advance in data engineering and cloud technology.
Ashish GuptaData Base Enginner
Enrolling in Vinsys’ Data Engineering on Microsoft Azure (DP-203T00) Certification Training has been a significant step forward in my professional development. The course is well-structured and covers all critical aspects of data engineering on Azure, delivered in an interactive and engaging format. The instructors were not only well-versed in the subject matter but also committed to ensuring each participant grasped the material fully. I particularly appreciated the practical exercises that allowed us to apply our learning directly to real-world scenarios, which greatly enhanced my understanding of the tools and technologies involved. Furthermore, the emphasis on teamwork and collaboration during projects enriched the learning experience. After completing the training, I now feel confident in my ability to contribute to data-driven initiatives within my organization. I highly recommend this course to anyone looking to advance their skills in data engineering and Azure technologies.
Ramesh TamakIT Head
My experience with Vinsys’ Data Engineering on Microsoft Azure Certification Training was exceptionally rewarding. The course provided a comprehensive overview of Azure's data services, crucial for anyone looking to excel in data engineering. The instructors were outstanding, demonstrating their expertise and creating a welcoming learning environment. They explained complex concepts clearly, making it easy for all participants to follow along. The focus on practical application was particularly beneficial; hands-on exercises and projects allowed us to actively engage with the material and understand how to implement it in our work settings. Additionally, the supportive feedback during the exercises encouraged us to ask questions and explore ideas freely. This training has significantly improved my skills, and I feel well-prepared to support data engineering projects within my organization. I highly encourage anyone interested in pursuing a career in data engineering to consider this course.
Jitendra BajpaiSoftware Engineer
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