DP-203: Data Engineering on Microsoft Azure-Course Overview

Note: Exam DP-203 is replacing exams DP-200 and DP-201. DP-200 and DP-201 will retire on June 30, 2021.

The DP-203: Data Engineering on Microsoft Azure course from CloudThat provides comprehensive training and study materials to help candidates prepare for the DP-203 certification exam. This course replaces the DP-200 and DP-201 exams, which retired on June 30, 2021.

Through this updated curriculum, learners gain the necessary skills to successfully clear the DP-203 exam and become certified Azure Data Engineers.

After Completing DP-203 certification training, students will be able to:

  • Design and implement data storage
  • Design and develop data processing
  • Secure, monitor, and optimize data storage
  • Secure, monitor, and optimize data processing

Upcoming Batches

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2025-01-22

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2025-02-01

2025-02-04

Key Features of DP-203 certification training

  • Our training modules are designed with a blend of practical and theoretical learning to maximize impact.

  • With 50%-60% hands-on lab sessions, we promote Thinking-Based Learning (TBL).

  • Our interactive virtual and in-person classes focus on Problem-Based Learning (PBL), while Microsoft-certified instructor-led sessions emphasize Competency-Based Learning (CBL).

  • Well-structured real-world use cases simulate industry challenges.

  • Our Learning Management System (LMS) and Exam Ready platform ensure full support, and as a Microsoft Learning Partner, we provide an edge in preparing for azure dp 203, azure data engineer certification, and dp 203 exam.

Who Should Attend:

  • Subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions and data processing.

What are the prerequisites for DP-203 certification training?

The prerequisites of DP-203 exam include:

  • A foundational knowledge of core data concepts and how they’re implemented using Azure data services.
  • Experience in designing and building scalable data models, cleaning and transforming data, and enabling advanced analytic capabilities that provide meaningful business value using Microsoft Power BI.

Learning Objectives of DP-203 Data Engineering on Microsoft Azure Training

  • Get started with data engineering on Azure: It provides a comprehensive platform for data engineering including introduction to services like ADLS Gen 2, Azure Synapse Analytics.
  • Build data analytics solutions using Azure Synapse serverless SQL pools: Learn how to store data in, transform data using, secure and manage the serverless SQL pools.
  • Perform data engineering with Azure Synapse Apache Spark Pools: This module covers how to store data in, analyze data using, and use delta lake of Apache Spark Pools.
  • Work with Data Warehouses using Azure Synapse Analytics: Understanding on how to load, analyze, optimize, and manage data in relational data warehouse.
  • Transfer and transform data with Azure Synapse Analytics pipelines: Azure Synapse Analytics enables data integration through the use of pipelines, which you can use to automate and orchestrate data transfer and transformation activities.
  • Work with Hybrid Transactional and Analytical Processing Solutions using Azure Synapse Analytics: Learn how to integrate Synapse Analytics with other Azure Data Services. Hybrid Transactional and Analytical Processing (HTAP) is a technique for near real time analytics without a complex ETL solution. In Azure Synapse Analytics, HTAP is supported through Azure Synapse Link.
  • Implement a Data Streaming Solution with Azure Stream Analytics: Discover techniques for ingesting, processing, and visualizing real-time data with Data streaming solutions.
  • Govern data across an enterprise: Learn how to use Microsoft Purview to register and scan data, catalog data artifacts, find data for reporting, and manage Power BI artifacts to improve data governance in your organization.
  • Data engineering with Azure Databricks: Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud. The learning objectives are designed to impart a comprehensive understanding of Azure Data Platform as a tool for data analysis and visualization. They aim to prepare participants for both the DP-203 exam and real-world data engineering scenarios.

What makes CloudThat a compelling choice for DP-203 training for Data Engineering on Microsoft Azure?

  • With over 11 years of experience, CloudThat delivers comprehensive Azure DP 203 training for Data Engineering on Microsoft Azure.
  • We’ve trained around 6.5 lakh professionals and served over 100 corporate clients globally.
  • Our Azure Data Engineer certification courses, led by Microsoft-certified trainers, emphasize practical learning, dedicating 50-60% of the training to hands-on lab sessions.
  • Our Azure Data Engineer course focuses on scenario-based problem-solving. CloudThat’s DP 203 certification includes instructor-led, competency-based learning, and has a strong track record working with Fortune 500 companies.
  • We're proud partners with Microsoft, AWS, GCP, and VMware.

Course Outline Download Course Outline

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

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

  • 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

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

Certification

    • By earning DP-203 certification, you can become Microsoft Certified Azure Data Engineer
    • Demonstrate abilities to Design and implement data storage, data processing and data security features
    • On successful completion of DP-203: Data Engineering on Microsoft Azure training aspirants receive a Course Completion Certificate from us
    • By successfully clearing the DP-203 exams, aspirants earn Microsoft Certification

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Reviews

A
Asif Ali

Excellent training sessions provided by CloudThat. I have attended a few webinars on Microsoft Azure and the trainers are really knowledgeable with good real time experience on Azure Cloud. The materials and the test prep kit along with the interactive training sessions really helps in clearing the certification exams. I would recommend everyone who is looking to make a career in cloud domain to register for the trainings provided by CloudThat.

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Jawed Akhtar

I had attend the Microsoft Azure training today.it was so good and nice to explain very clearly and it was really helpful for my upcoming professional careers.

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Remya Ravi

Great and valuable training session. Thank you.

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