DP-203: Data Engineering on Microsoft Azure Certification

This DP-203: Data Engineering on Microsoft Azure Certification training course tailored for the DP-203 exam aims to provide you with the necessary expertise and understanding to thrive in the field of data engineering through the utilization of Microsoft Azure services. The DP-203 exam evaluates candidates on their ability to devise and execute data storage solutions, manage data workflows, enhance performance, and safeguard data solutions employing various Microsoft Azure services.

|| About Data Engineering on Microsoft Azure Certification


To convey the knowledge and abilities required to create and implement data engineering solutions on Azure, the DP-203T00: Data Engineering on Microsoft Azure course is offered. It covers all of the Azure services, such as Azure Stream Analytics, Azure Databricks, Azure Synapse Analytics, and Azure Data Lake Storage Gen2. Through exercises that complement the teachings, learners will have hands-on experience in data engineering with Azure Synapse Apache Spark Pools, real-time analytics with Azure Stream Analytics, and data querying with serverless SQL pools. With lessons on end-to-end security, the course delves even deeper into security and compliance. It also looks at how to integrate Power BI for reporting and Azure Synapse Analytics for machine learning procedures.


Upon completion of this course, students will have a solid background in data engineering techniques, which enables them to create safe and scalable cloud-based data solutions. Professionals wishing to use Azure for analytics, data processing, and insight-gathering that can lead to commercial value would benefit from this course. 

|| What will I learn?

  • Gain proficiency in building and managing data pipelines, data lakes, and data warehouses using Azure services.
  • Learn how to design and implement data storage solutions, data processing solutions, and data security solutions in Azure.
  • Develop skills in ensuring data security, privacy, and compliance in Azure data solutions.
  • Prepare for the DP-203 certification exam through hands-on labs and practice exams.
  • Understand the principles and components of data engineering on Microsoft Azure.
  • Explore data integration, transformation, and orchestration techniques in Azure.

|| What will I learn?

  • Gain proficiency in building and managing data pipelines, data lakes, and data warehouses using Azure services.
  • Learn how to design and implement data storage solutions, data processing solutions, and data security solutions in Azure.
  • Develop skills in ensuring data security, privacy, and compliance in Azure data solutions.
  • Prepare for the DP-203 certification exam through hands-on labs and practice exams.
  • Understand the principles and components of data engineering on Microsoft Azure.
  • Explore data integration, transformation, and orchestration techniques in Azure.

|| Requirements

  • Basic understanding of data engineering concepts and principles.
  • Familiarity with Microsoft Azure fundamentals and cloud computing concepts is beneficial.

|| Requirements

  • Basic understanding of data engineering concepts and principles.
  • Familiarity with Microsoft Azure fundamentals and cloud computing concepts is beneficial.

    • Lectures - Data Engineering on Microsoft Azure
    • Introduction to Azure Data Services:
    • Overview of Azure data services and the Azure Data platform.
    • Understanding the benefits of cloud-based data solutions compared to on-premises alternatives.
    • Exploring different Azure data services including Azure Synapse Analytics, Azure Data Lake Storage, Azure Databricks, and Azure Data Factory.


    • Data Storage and Processing:
    • Provisioning and configuring Azure Storage accounts for different data types and workloads.
    • Implementing data ingestion and processing pipelines using Azure Data Factory.
    • Working with structured, semi-structured, and unstructured data formats.


    • Data Transformation and Analysis:
    • Performing data transformation and preparation tasks using Azure Data Factory Data Flows.
    • Exploring data analysis and visualization capabilities with Azure Synapse Analytics.
    • Implementing data exploration and machine learning experiments with Azure Databricks.


    • Big Data Processing and Analytics:
    • Setting up big data clusters in Azure Synapse Analytics for scalable data processing.
    • Implementing real-time data processing solutions using Azure Stream Analytics.
    • Building and deploying machine learning models with Azure Machine Learning.


    • Data Integration and Orchestration:
    • Implementing data integration solutions to connect disparate data sources and systems.
    • Orchestrating complex data workflows and dependencies using Azure Data Factory.
    • Integrating data from on-premises and cloud-based sources using Azure Hybrid Connections and Data Gateway.


    • Data Security and Governance:
    • Implementing data security and access control measures using Azure Active Directory.
    • Configuring data encryption, masking, and anonymization techniques to protect sensitive data.
    • Implementing data governance policies and compliance standards in Azure data solutions.


    • Data Monitoring and Optimization:
    • Monitoring data pipelines and workflows for performance, reliability, and scalability.
    • Implementing logging, telemetry, and alerting mechanisms using Azure Monitor.
    • Optimizing data storage and processing costs through resource management and utilization.


    • Data Storage and Archiving Strategies:
    • Implementing data storage and archiving strategies using Azure Blob Storage and Azure Data Lake Storage.
    • Configuring tiered storage and lifecycle management policies for efficient data management.
    • Implementing data retention and archival policies to comply with regulatory requirements.


    • Data Quality and Master Data Management:
    • Implementing data quality solutions to ensure data accuracy, completeness, and consistency.
    • Performing data cleansing, validation, and enrichment tasks using Azure Data Factory and Azure Databricks.
    • Implementing master data management (MDM) solutions to manage and govern organizational data assets.


    • Data Pipeline Automation and DevOps:
    • Implementing continuous integration and continuous deployment (CI/CD) pipelines for data solutions.
    • Automating data pipeline provisioning, deployment, and monitoring using Azure DevOps and Azure Resource Manager (ARM) templates.
    • Leveraging Infrastructure as Code (IaC) principles for managing and scaling data infrastructure.


    • Lab - Data Engineering on Microsoft Azure
    • Exercise 1: Setting Up Azure Data Lake Storage Gen2
    • Objective: Provision and configure Azure Data Lake Storage Gen2.
    • Create an Azure Data Lake Storage Gen2 account in your Azure subscription.
    • Configure access control settings using Azure AD integration.
    • Create file systems within the storage account for organizing data.
    • Upload sample data files (e.g., CSV, JSON) to the storage account.
    • Set up access control lists (ACLs) to manage permissions on files and directories.


    • Exercise 2: Implementing Data Ingestion with Azure Data Factory
    • Objective: Design and deploy a data ingestion pipeline using Azure Data Factory.
    • Set up linked services for connecting to the source and destination data stores (e.g., Azure Blob Storage, Azure SQL Database).
    • Define datasets to represent the source and destination data entities.
    • Create a pipeline with a Copy Data activity to ingest data from the source to the destination.
    • Configure data movement settings such as partitioning, compression, and concurrency.
    • Schedule the pipeline to run at regular intervals or trigger it based on specific events.
    • Monitor pipeline execution and track data ingestion progress using Azure Data Factory monitoring tools.


    • Exercise 3: Data Transformation with Azure Databricks
    • Objective: Perform data transformation tasks using Azure Databricks.
    • Create an Azure Databricks workspace in your Azure subscription.
    • Set up clusters with the appropriate configurations for data processing tasks.
    • Import sample data into Databricks workspace from Azure Data Lake Storage Gen2 or Blob Storage.
    • Implement data transformation logic using Spark DataFrame APIs or SQL queries.
    • Explore data visualization capabilities in Databricks notebooks.
    • Optimize data processing performance by tuning Spark configurations and cluster settings.


    • Exercise 4: Real-Time Data Processing with Azure Stream Analytics
    • Objective: Build and deploy a real-time data processing solution using Azure Stream Analytics.
    • Create an Azure Stream Analytics job in your Azure subscription.
    • Configure input sources to ingest streaming data from event hubs, IoT Hubs, or other streaming platforms.
    • Define stream analytics queries to perform real-time data transformation and analysis.
    • Configure output sinks to store or visualize the processed data (e.g., Azure Blob Storage, Azure SQL Database, Power BI).
    • Test the stream analytics job by sending sample data through the input sources.
    • Monitor job execution and troubleshoot any issues using Azure Stream Analytics monitoring tools.


    • Exercise 5: Building Data Pipelines with Azure Synapse Analytics
    • Objective: Design and deploy data pipelines using Azure Synapse Analytics.
    • Create an Azure Synapse Analytics workspace in your Azure subscription.
    • Define linked services to connect to various data sources and destinations.
    • Design data flow activities to orchestrate data movement and transformation tasks.
    • Implement data ingestion, transformation, and loading (ETL) logic using SQL scripts or Spark jobs.
    • Optimize data processing performance by leveraging distributed query processing and parallel execution.
    • Monitor pipeline execution and track data pipeline performance using Azure Synapse Analytics monitoring capabilities.

Get in touch

|| Scope & Career Opportunities of Data Engineers on Microsoft Azure Certifications

The future scope for professionals with the DP-203: Data Engineering on Microsoft Azure certification is highly promising. As data-driven decision-making becomes increasingly critical across industries, the demand for skilled data engineers continues to grow. The widespread adoption of cloud computing, particularly on Azure, amplifies this demand, providing ample opportunities for certified professionals to design, implement, and manage robust data solutions.


As organizations increasingly rely on cloud platforms, the need for skilled Azure data engineers has surged, with 95% of Fortune 500 firms leveraging Azure cloud services. The big data market is expanding rapidly, projected to reach $273.4 billion by 2026, which fuels demand for data experts, especially those proficient in Azure data engineering. The DP-203 certification opens doors to exciting roles such as data engineer, data analyst, or database administrator within the Azure ecosystem, and professionals who hold this certification often report job promotions and salary increments. Additionally, Microsoft certifications are recognized worldwide, making the DP-203 a valuable asset for career growth.


Additionally, as organizations leverage advanced analytics and AI, the expertise to handle large data volumes and implement sophisticated data processing pipelines becomes essential. Certified data engineers will play pivotal roles in various industries, including finance, healthcare, and retail, ensuring data governance and compliance with evolving regulatory requirements. This certification not only enhances career growth and salary prospects but also opens doors to advanced positions such as Senior Data Engineer and Data Architect. Moreover, the integration with other Microsoft technologies and the continuous evolution of big data tools and frameworks mean that certified professionals will remain at the forefront of innovation, making significant contributions to their organizations' strategic goals and maintaining a competitive edge in the tech industry.

|| Average Annual Salary of Professionals with Data Engineering on Microsoft Azure Certification

The average salary of professionals with a DP-203: Data Engineering on Microsoft Azure certification in India varies based on experience, location, and specific role. Typically, the annual salary for an Azure Data Engineer in India ranges between ₹4.0 Lakhs to ₹15.0 Lakhs, with an average of ₹8.2 Lakhs. This certification enables professionals to work with Azure data services, integrate data from various sources, and design analytics solutions. Salaries can vary significantly depending on the company, city, and the individual's skill set. Cities like Bangalore, Mumbai, and Delhi often offer higher salaries due to the concentration of tech companies and higher cost of living.

In the United States, the salary range for an Azure Data Engineer is from $111,491 to $138,695 per year, with an average base salary of $126,015. Experienced professionals can earn up to $156,863 annually, particularly in high-paying cities such as San Jose, Santa Clara, and Fremont in California.


|| Top Companies Hiring for AZ-900T01: Microsoft Azure Fundamentals Certified Professionals

Professionals with DP-203: Data Engineering on Microsoft Azure certification are highly sought after by top companies globally. In India, leading IT firms like Tata Consultancy Services (TCS), Infosys, and Wipro are prominent recruiters for Azure-certified roles. Multinational corporations such as Accenture and Capgemini also offer opportunities. Globally, companies like Microsoft, Amazon Web Services (AWS), Google Cloud Platform (GCP), IBM, and Deloitte actively hire Azure-certified professionals for data engineering roles. These companies recognize the value of Azure certification and actively seek professionals to build and manage their data solutions on the Azure platform.

placement report

|| Frequently asked question

The Data Engineering on Microsoft Azure Certification (DP-203) is designed to validate the skills and knowledge required to design, implement, and monitor data engineering solutions using Microsoft Azure services. It covers various aspects of data engineering, including data ingestion, transformation, storage, and analysis on the Azure platform.

The DP-203 certification is suitable for data engineers, data architects, data analysts, and anyone involved in designing and implementing data engineering solutions on Microsoft Azure. It is ideal for individuals who have experience with Azure services and want to specialize in data engineering.

Yes, upon passing the DP-203 certification exam, candidates will receive the Microsoft Certified: Data Engineer Associate certification. This certification demonstrates their proficiency in designing and implementing data engineering solutions on Microsoft Azure.

You can verify the authenticity of your Microsoft Certified: Data Engineer Associate certification by providing your certification ID or access code on the official Microsoft certification website. Employers and recruiters can also verify certifications directly through the Microsoft Certification Verification portal.

Yes, the Microsoft Certified: Data Engineer Associate certification can serve as a prerequisite for other Azure certifications. Many advanced Azure certifications require candidates to have foundational knowledge of data engineering concepts, which is covered in the DP-203 certification. However, it's essential to review the specific prerequisites for each certification exam before proceeding.

Related courses

AZ-104T00-A: Microsoft Azure Administrator
Read more
AZ-204: Developing Solutions for Microsoft Azure
Read more
DP-300: Administering Microsoft Azure SQL Solutions
Read more
AZ-900 Certification : Microsoft Azure Fundamentals
Read more
AZ-305: Designing Microsoft Azure Infrastructure Solutions
Read more
AZ-400: Designing and Implementing Microsoft DevOps Solutions
Read more
PL-300 Microsoft Power BI Data Analyst course
Read more
× Popup Image

Useful links

Categories

IT Infrastructure

Development

Data Science

Work with us

Company

Copyright Baroda Institute of Technology© 2024 | Design and Developed By Bit infotech