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(DP-203)Azure Data Engineering Certification and Training
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- Extensive Program with 2 Modules View all
- 56+ Hours Of Interactive Learning
- 20+ Assignments 4+ Live Projects
- Latest content, as per Microsoft Updates
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Discover the power of Azure Data Factory with Kloud Academy’s DP-203 training. In this comprehensive course, learn to orchestrate and automate data workflows, integrating diverse data sources, transforming data, and deploying solutions at scale. Gain the skills needed to create efficient data pipelines and leverage Azure services effectively. Empower your data-driven decisions and enhance your data engineering expertise through hands-on exercises and real-world scenarios.
Boost your career with our Azure Data Factory Training and Certification Course, available online. Register today to take the first step towards Success
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4.4Enroll in our instructor-led Azure Data Factory Training and Certification (DP-203) Course with Live online sessions.
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Why Enroll for Azure Data Engineering (DP-203) Training Program ?
Microsoft, Deloitte, Capgemini, TCS, IBM, Wipro, Cognizant, PwC, Ernst & Young, KPMG and many other industry leaders worldwide use Data engineering for their cloud computing needs
With the increasing adoption of cloud technology by businesses in India, there is a rising demand for skilled Azure Data Engineers, resulting in a favorable job outlook for professionals in this field.
As per industry surveys, the average annual salary for an Azure Data Engineer in India ranges from INR 6,00,000 to INR 15,00,000.
Boost your career as an Azure Data Engineer with our Certification Training and reap the benefits
Unlock the potential of Azure Data Factory with Kloud Academy’s DP-203 training and elevate your career as an Azure Data Engineer. Master data integration, orchestration, and transformation skills with hands-on projects and expert guidance. Earn your certification to validate your expertise and open doors to lucrative opportunities in the rapidly growing field of data engineering. Boost your career prospects and become a sought-after professional in the data-driven world.
Why choose KloudCourse Academy for Azure Data Engineering Training?
Live Interactive Learning
- World-Class Certified Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Access to resources
- Video Tutorials
- Practice Quizzes
- Step-by-Step Lab Guides
Post Course Support
- Learning Assistance
- Job Guidence
- Resume Building
Hands-On Projects
- Industry-Relevant Projects
- Cloud Migration Included
- Quizzes & Assignments
DP 203 Training Certification
- KloudCourse Training Certificate
- Graded Performance Certificate
- Certificate of Completion
About your Azure Data Engineer Training Course
Microsoft Azure Data Engineer Course Skills Covered
- Understanding of the Azure Data Platform
- Azure storage implementation skills
- Familiar with key Azure services
- Azure data integration knowledge
- Data solutions design and deployment proficiency
- Big data processing with HDInsight and Databricks
Azure Data Engineering Training and Certification Tools Covered
- Azure Data Factory
- Azure Databricks
- Azure Stream Analytics
- Elastic Beanstalk
- Azure Cosmos DB
- Azure Storage
- Azure SQL Database and Azure SQL Managed Instance
- Azure Data Lake Storage
- Azure Synapse Analytics
- Azure Cognitive Services
- Azure HDInsight
- Azure Data Explorer
- Azure Data Share
Explore Our Top-Tier Azure Data Engineering Training Course Curriculum
Learn how to configure Azure Active Directory, including features like Azure AD join and self-service password reset.
Learning objectives:
Describe ways to represent data
- Describe features of structured data
- Describe features of semi-structured
- Describe features of unstructured data
Identify options for data storage
- Describe common formats for data files
- Describe types of databases
Describe common data workloads
- Describe features of transactional workloads
- Describe features of analytical workloads
Identify roles and responsibilities for data workloads
- Describe responsibilities for database administrators
- Describe responsibilities for data engineers
- Describe responsibilities for data analysts
Hands-On
- Understanding different data representation methods.
- Identifying features and characteristics of structured data.
- Exploring features and characteristics of semi-structured data.
- Exploring features and characteristics of unstructured data.
- Identifying options for data storage.
- Learning about common data file formats.
- Understanding different types of databases.
- Understanding common data workloads.
- Understanding features and requirements of transactional workloads.
- Understanding features and requirements of analytical workloads.
Skills You Will Learn
- Understanding various data representation methods.
- Familiarity with features and characteristics of structured, semi-structured, and unstructured data.
- Knowledge of data storage options.
- Familiarity with common data file formats.
- Understanding different types of databases and their characteristics.
- Understanding different data workloads and their requirements.
- Identifying roles and responsibilities for managing data workloads.
- Understanding responsibilities of database administrators, data engineers, and data analysts.
Learn how to configure Azure Active Directory, including features like Azure AD join and self-service password reset.
Learning objectives:
Describe relational concepts
- Identify features of relational data
- Describe normalization and why it is used
- Identify common structured query language (SQL) statements
- Identify common database objects
- Describe relational Azure data services
Describe relational Azure data services
- Describe the Azure SQL family of products including Azure SQL Database, Azure SQL
- Managed Instance, and SQL Server on Azure Virtual Machines
- Identify Azure database services for open-source database systems
Hands-On
- Understand relational concepts in databases.
- Identify features of relational data.
- Describe normalization and its purpose in database design.
- Identify common SQL statements for database querying.
- Identify common database objects such as tables, views, and indexes.
- Describe Azure’s relational data services.
- Understand the Azure SQL family of products, including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines.
- Identify Azure database services for open-source database systems.
Skills You Will Learn
- Understanding relational database principles.
- Familiarity with the features and characteristics of relational data.
- Knowledge of normalization and its significance in database design.
- Proficiency in using common SQL statements for data manipulation and retrieval.
- Familiarity with various database objects and their functions.
- Understanding Azure’s relational data services and their capabilities.
- Knowledge of the Azure SQL family of products and their applications.
- Identifying suitable Azure database services for open-source database systems.
Learn how to configure Azure Active Directory, including features like Azure AD join and self-service password reset.
Learning objectives:
Describe capabilities of Azure storage
- Describe Azure Blob storage
- Describe Azure File storage
- Describe Azure Table storage
Describe capabilities and features of Azure Cosmos DB
- Identify use cases for Azure Cosmos DB
- Describe Azure Cosmos DB APIs
Hands-On
- Explore Azure storage capabilities.
- Learn about Azure Blob storage and its features.
- Understand Azure File storage and its functionalities.
- Discover Azure Table storage and its characteristics.
- Explore Azure Cosmos DB capabilities and features./li>
- Identify various use cases for Azure Cosmos DB.
- Learn about the different APIs available for Azure Cosmos DB.
Skills You Will Learn
- Understanding the benefits and capabilities of Azure storage.
- Familiarity with Azure Blob storage and its use cases.
- Knowledge of Azure File storage and its applications.
- Understanding the purpose of Azure Table storage.
- Familiarity with the capabilities and features of Azure Cosmos DB.
- Identifying suitable use cases for Azure Cosmos DB.
- Knowledge of the different APIs available for Azure Cosmos DB.
Learn how to configure Azure Active Directory, including features like Azure AD join and self-service password reset.
Learning objectives:
Describe common elements of large-scale analytics
- Describe considerations for data ingestion and processing
- Describe options for analytical data stores
- Describe Azure services for data warehousing, including Azure Synapse Analytics, Azure Databricks, Azure HDInsight, and Azure Data Factory
Describe consideration for real-time data analytics
- Describe the difference between batch and streaming data
- Describe technologies for real-time analytics including Azure Stream Analytics, Azure Synapse Data Explorer, and Spark structured streaming
Describe data visualization in Microsoft Power BI
- Identify capabilities of Power BI
- Describe features of data models in Power BI
- Identify appropriate visualizations for data
Hands-On
- Understand large-scale analytics elements.
- Explore data ingestion and processing considerations.
- Discover options for analytical data stores.
- Explore Azure services for data warehousing.
- Differentiate between batch and streaming data processing.
- Explore technologies for real-time analytics.
- Learn data visualization using Microsoft Power BI.
Skills You Will Learn
- Understanding large-scale analytics components.
- Proficiency in data ingestion and processing techniques.
- Familiarity with analytical data store options.
- Knowledge of Azure services for data warehousing.
- Differentiating between batch and streaming data processing.
- Familiarity with real-time analytics technologies.
- Data visualization skills using Microsoft Power BI.
Learn how to configure Azure Active Directory, including features like Azure AD join and self-service password reset.
Learning objectives:
Implement a partition strategy
- Implement a partition strategy for files
- Implement a partition strategy for analytical workloads
- Implement a partition strategy for streaming workloads
- Implement a partition strategy for Azure Synapse Analytics
- Identify when partitioning is needed in Azure Data Lake Storage Gen2
Design and implement the data exploration layer
- Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster
- Recommend and implement Azure Synapse Analytics database templates
- Push new or updated data lineage to Microsoft Purview
- Browse and search metadata in Microsoft Purview Data Catalog
Hands-On
- Implement partition strategies for various scenarios.
- Implement partition strategy for files, analytical workloads, streaming workloads, and Azure Synapse Analytics.
- Identify the need for partitioning in Azure Data Lake Storage Gen2.
- Design and implement the data exploration layer.
- Execute queries using SQL serverless and Spark cluster-based compute solutions.
- Recommend and implement Azure Synapse Analytics database templates.
- Update data lineage in Microsoft Purview.
- Browse and search metadata in Microsoft Purview Data Catalog.
Skills You Will Learn
- Proficiency in implementing partition strategies.
- Query creation and execution using SQL serverless and Spark cluster-based solutions.
- Ability to design and implement data exploration layers.
- Knowledge of Azure Synapse Analytics database templates.
- Understanding data lineage and its integration with Microsoft Purview.
- Familiarity with browsing and searching metadata in Microsoft Purview Data Catalog.
Learn how to configure Azure Active Directory, including features like Azure AD join and self-service password reset.
Learning objectives:
Ingest and transform data
- Design and implement incremental loads
- Transform data by using Apache Spark
- Transform data by using Transact-SQL (T-SQL)
- Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory
- Transform data by using Azure Stream Analytics
- Cleanse data
- Handle duplicate data
- Handle missing data
- Handle late-arriving data
- Split data
- Shred JSON
- Encode and decode dataConfigure error handling for a transformation
- Normalize and denormalize data
- Perform data exploratory analysis
Develop a batch processing solution
- Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory
- Use PolyBase to load data to a SQL pool
- Implement Azure Synapse Link and query the replicated data
- Create data pipelines
- Scale resources
- Configure the batch size
- Create tests for data pipelines
- Integrate Jupyter or Python notebooks into a data pipeline
- Upsert data
- Revert data to a previous state
- Configure exception handling
- Configure batch retention
- Read from and write to a delta lake
Develop a stream processing solution
- Create a stream processing solution by using Stream Analytics and Azure Event Hubs
- Process data by using Spark structured streaming
- Create windowed aggregates
- Handle schema drift
- Process time series data
- Process data across partitions
- Process within one partition
- Configure checkpoints and watermarking during processing
- Scale resourcesCreate tests for data pipelines
- Optimize pipelines for analytical or transactional purposes
- Handle interruptions
- Configure exception handling
- Upsert data
- Replay archived stream data
Manage batches and pipelines
- Trigger batches, Handle failed batch loads
- Validate batch loads
- Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines
- Schedule data pipelines in Data Factory or Azure Synapse Pipelines
- Implement version control for pipeline artifacts
- Manage Spark jobs in a pipeline
Hands-On
- Configure error handling for transformations.
- Normalize and denormalize data.
- Perform exploratory data analysis.
- Develop batch processing solutions using Azure services.
- Load data with PolyBase to a SQL pool.
- Query replicated data using Azure Synapse Link.
- Create and manage data pipelines, scale resources, and configure batch size.
- Create tests and integrate Jupyter/Python notebooks.
- Upsert data, revert to previous states, and configure exception handling.
- Configure batch retention and work with Delta Lake.
- Develop stream processing solutions with Stream Analytics and Azure Event Hubs.
- Process data using Spark structured streaming and create windowed aggregates.
- Handle schema drift, time series data, and data across partitions.
- Configure checkpoints, watermarking, and scale resources.
- Optimize pipelines for analytical or transactional purposes.
- Handle interruptions and exceptions.
- Upsert data and replay archived stream data.
- Manage batches and pipelines, trigger and validate batch loads.
- Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines.
- Schedule data pipelines and implement version control.
- Manage Spark jobs in a pipeline.
Skills You Will Learn
- Ingesting and transforming data.
- Designing and implementing incremental loads.
- Transforming data with Apache Spark or Transact-SQL (T-SQL).
- Using Azure Synapse Pipelines or Azure Data Factory for data ingestion and transformation.
- Transforming data using Azure Stream Analytics.
- Cleaning, handling duplicates, missing data, and late-arriving data.
- Splitting data, shredding JSON, encoding and decoding data.
- Configuring error handling for transformations.
- Normalizing and denormalizing data.
- Performing exploratory data analysis.
- Loading data to a SQL pool using PolyBase..
- Querying replicated data with Azure Synapse Link.
- Creating and managing data pipelines, scheduling and version control.
- Working with stream processing solutions using Azure services.
- Processing data with Spark structured streaming and windowed aggregates.
- Handling schema drift, time series data, and data across partitions.
- Configuring checkpoints, watermarking, and scaling resources.
- Optimizing pipelines for analytical or transactional purposes.
- Handling interruptions and exceptions.
- Upsetting and reverting data, managing batches and pipeline loads.
Master Azure Data Engineering with our expertly crafted Certification Course.
Azure Data Factory (ADF) is a Microsoft cloud-based data integration service. As an Azure Data Engineer, DP-203 certification validates your expertise in designing, deploying, monitoring, and managing data integration solutions using ADF. ADF allows you to create data pipelines to ingest, transform, and load data from various sources to different destinations efficiently.
- Data Professionals
- Data Engineers
- Data Analysts
- Database Administrators
- Cloud Architects
- ETL Developers
- Business Intelligence Developers
- Data Integration Specialists
- Data Warehousing Professionals
- Comprehensive Curriculum: Full Coverage
- Expert Instructors: Industry Professionals
- Hands-on Training: Practical Experience
- Real-world Scenarios: Applied Learning
- Azure Data Factory Skills: In-demand Expertise
- Certification Preparation: Exam Readiness
- Flexible Learning: Self-paced Options
- Career Advancement: Better Opportunities
- Accessible Anytime, Anywhere: Convenient Learning
- Interactive Learning: Engaging Classroom Environment
- Direct Interaction: In-person Discussions with Instructors
- Hands-on Practice: Access to On-site Labs
- Immediate Feedback: Instant Q&A Sessions
- Peer Collaboration: Networking Opportunities
- Structured Schedule: Consistent Learning Pace
- Personalized Guidance: Tailored Support
- In-depth Discussions: Comprehensive Understanding
- Enhanced Motivation: Group Learning Experience
- Exam Preparation: Targeted Training for Certification Success
- The Azure Data Factory course offered by Kloud Course Academy typically requires participants to have a basic understanding of cloud computing concepts. Familiarity with data integration, data transformation, and SQL querying is beneficial. Prior knowledge of data storage and data processing concepts would also be helpful.
- Azure Data Factory (ADF) is a powerful data integration service by Microsoft, designed to move, transform, and orchestrate data between different sources and destinations. It plays a key role in data engineering workflows and is widely used by Azure Data Engineers to create efficient data pipelines. ADF seamlessly integrates with Azure Data Lake, enabling data professionals to ingest, process, and analyze large volumes of data stored in the Azure cloud environment.
Azure Data Factory skills encompass the ability to design and manage data integration workflows, connecting data from diverse sources and destinations. As an Azure Data Engineer, proficiency in Azure Data Factory is crucial for efficiently orchestrating data pipelines, including seamless integration with Azure Data Lake. These skills empower data professionals to handle data movement, transformation, and processing effectively, facilitating data-driven decision-making and advanced analytics using Azure Data Lake’s capabilities.
The Azure Data Factory training course offered by Kloud Course Academy is ideal for aspiring Azure Data Engineers, as well as professionals working with Azure Data Lake and seeking to enhance their data integration skills.
- Azure Data Engineers
- Data Professionals
- Azure Data Lake Users
- Cloud Solution Architects:
- Data Analysts
- Business Intelligence Developers:
- Database Administrators
- ETL Developers
- Data Integration Specialists:
- Data Warehousing Professionals
- IT Professionals and Developers
To take the Azure Data Factory online course, you need a computer/laptop with internet access, a compatible web browser, and a display resolution of 1280×720 or higher.
To perform practical exercises for the Azure Data Factory certification course at Kloud Course Academy, you will have access to a hands-on lab environment provided by the academy, where you can apply the concepts learned in the course and gain practical experience with Azure Data Factory.
Acquire valuable experience in Azure Data Engineering with our advanced Training and Certification Real-Time Projects.
Real-Time Data Processing with Azure Stream Analytics
Data Warehousing with Azure Synapse Analytics
ETL Pipeline Automation with Azure Data Factory
Big Data Processing with Azure Databricks
Take pride in your achievements as an Azure Data Engineer with our Azure Data Engineering Certification.
Microsoft offers a cloud-based data integration service called Azure Data Factory. It is used to create, schedule, and orchestrate data pipelines that move and transform data from various sources to different destinations in the Azure cloud environment.
To learn Azure Data Factory, aspiring Azure Data Engineers should have a basic understanding of cloud computing concepts and data integration, and familiarity with Azure Data Lake and related technologies. DP-203 certification training can provide the necessary knowledge to master Azure Data Factory’s capabilities and effectively manage data workflows in the cloud environment.
Designing and implementing data storage solutions
Designing and developing data processing solutions
Creating and enforcing data security and compliance
Monitoring and optimizing data storage and data processing
Designing and implementing data integration solutions
The Azure Data Factory Training for DP-203 will offer two options for self-paced learning: instructor-led online classroom training and videos from Kloud Course Academy. Self-Paced Training costs $316 when IGST (18%) is added.
The duration of the Azure Data Factory exam (DP-203) is 150 minutes (2 hours and 30 minutes).
To pass the Azure Data Factory exam (DP-203), candidates need to achieve a minimum passing score of 700 out of 1000 points.
It’s important to note that the passing score is subject to be adjusted by Microsoft periodically.
The Azure Data Factory exam (DP-203) is offered in multiple languages, including English, Japanese, Chinese (Simplified), and Korean.
Azure Data Factory primarily uses a graphical user interface (GUI) for data pipeline design and orchestration. Additionally, it supports Azure Data Factory Mapping Data Flow, which allows users to visually design and execute complex data transformations using a low-code approach with data flow expressions and transformations. PowerShell, Python, REST, and. NET are all supported languages.
This test evaluates your ability to carry out the following technical tasks:
- Development and implementation of into practice data storage (15–20%)
- Build your data processing (40–45%)
- Ensure data storage and processing are secure, monitored, and optimized (30–35%).
- Development and implementation of into practice data storage (15–20%)
- Build your data processing (40–45%)
- Ensure data storage and processing are secure, monitored, and optimized (3