Data Engineering on Microsoft Azure DP-203

Data Engineering on Microsoft Azure DP-203

DP-203: Data Engineering on Microsoft Azure Training in Coimbatore

Course Overview

The DP-203 certification course, officially titled Data Engineering on Microsoft Azure, is designed for professionals who want to design and implement data solutions using Microsoft Azure data services. This course focuses on building secure, scalable, and optimized data pipelines and analytics platforms for modern enterprise workloads.

At Linux Training Center in Coimbatore, this instructor-led course prepares you for the DP-203 certification exam and equips you with job-ready skills to work as a cloud data engineer in real-time business environments.

Why Choose DP-203?

As organizations move toward data-driven decision-making, there’s a rising demand for professionals who can handle large-scale data integration, transformation, and analytics. The DP-203 certification validates your ability to work with services like Azure Synapse Analytics, Azure Data Lake, Azure Data Factory, Databricks, and Azure Stream Analytics.

If you’re aiming to work on enterprise data engineering projects, this course is your gateway to mastering end-to-end data solutions in Azure.

Who Should Enroll?

This course is perfect for aspiring data engineers, ETL developers, business intelligence professionals, cloud engineers, and data analysts who want to specialize in cloud-based data platforms. It is also ideal for those preparing for the Microsoft Certified: Azure Data Engineer Associate certification.

Prior experience in SQL, data warehousing, or any cloud platform will be helpful but not mandatory.

What You Will Learn

  • Designing and building data pipelines using Azure Data Factory

  • Building data lakes and managing unstructured data with Azure Data Lake Storage

  • Implementing data transformation using Azure Databricks and Synapse Analytics

  • Managing data security, compliance, and governance

  • Developing real-time data processing solutions with Azure Stream Analytics

  • Monitoring, optimizing, and troubleshooting big data workflows

This course combines theoretical knowledge with practical labs to help you solve real-world data engineering problems.

Course Highlights

  • DP-203 exam-aligned curriculum

  • Hands-on labs with Azure tools and real-time datasets

  • Training led by certified Azure data professionals

  • Mock exams and certification guidance

  • Flexible weekday and weekend batches

  • Job placement assistance post-training

Career Opportunities

After completing this course, you can apply for roles such as Azure Data Engineer, Big Data Engineer, ETL Developer, Cloud Data Architect, and Data Platform Specialist. The DP-203 certification is recognized globally and opens doors to high-paying roles in cloud and data-driven industries.

Why Linux Training Center?

We’re known in Coimbatore for offering industry-relevant, hands-on training that goes beyond theory. Our DP-203 course is structured to ensure practical exposure, exam success, and career readiness, all backed by expert instructors and dedicated placement support.

Master data engineering in the cloud with DP-203 training at Linux Training Center, Coimbatore. Learn to design, build, and manage scalable data solutions using Microsoft Azure.

DP-203 Course Syllabus

Modules

Design and Implement Data Storage (15-20%)

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

Develop Data Processing (40-45%)

Ingest and transform data - Design and implement incremental loads
- Transform data by using Apache Spark
- Transform data by using Transact-SQL (T-SQL) in Azure Synapse Analytics
- 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
- Avoiding duplicate data by using Azure Stream Analytics Exactly Once Delivery
- Handle missing data
- Handle late-arriving data
- Split data
- Shred JSON
- Encode and decode data
- Configure 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 resources
- Create 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

Secure, monitor, and optimize data storage and data processing (30-35%)

Implement data security - Implement data masking
- Encrypt data at rest and in motion
- Implement row-level and column-level security
- Implement Azure role-based access control (RBAC)
- Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
- Implement a data retention policy
- 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
- Monitor stream processing
- Measure performance of data movement
- Monitor and update statistics about data across a system
- Monitor data pipeline performance
- Measure query performance
- Schedule and monitor pipeline tests
- Interpret Azure Monitor metrics and logs
- Implement a pipeline alert strategy Optimize and troubleshoot data storage and data processing - Compact small files
- Handle skew in data
- Handle data spill
- Optimize resource management
- Tune queries by using indexers
- Tune queries by using cache
- Troubleshoot a failed Spark job
- Troubleshoot a failed pipeline run, including activities executed in external services

Related Posts