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  • DP-203: Data Engineering on Microsoft Azure [203T00-A] (EN)

  • Group Training

    As Azure Data Engineers learn to integrate and transform data to build structured analytics solutions!

    Training code
    CGADP203CE
    Spoken Language
    English
    Language Materials
    English
    Dayparts
    8
    Price
    €2.400,00
    excl. VAT No extra costs.

    Book DP-203: Data Engineering on Microsoft Azure [203T00-A] (EN) now

    In group training, we use several learning methods to help you obtain the knowledge, give you helpful insights and get you inspired. Check the Spoken language and Language materials on the left for language info.

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    What is DP-203: Data Engineering on Microsoft Azure [203T00-A]

    In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines.
    The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.
    This course uses MOC (Microsoft Official Courseware) and will be given by an experienced MCT (Microsoft Certified Trainer).
    See the below modules for more information:
    Module 1: Explore compute and storage options for data engineering workloads
    This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.

     
     

    Who should attend DP-203: Data Engineering on Microsoft Azure [203T00-A]

    The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

    Prerequisites

    Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
    Specifically completing:

    • AZ-900 - Azure Fundamentals
    • DP-900 - Microsoft Azure Data Fundamentals

    Objectives

    After completing this course, you will be able to:
    Explore compute and storage options for data engineering workloads in Azure

    • Design and Implement the serving layer
    • Understand data engineering considerations
    • Run interactive queries using serverless SQL pools
    • Explore, transform, and load data into the Data Warehouse using Apache Spark
    • Perform data Exploration and Transformation in Azure Databricks
    • Ingest and load Data into the Data Warehouse
    • Transform Data with Azure Data Factory or Azure Synapse Pipelines
    • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
    • Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
    • Analyze and Optimize Data Warehouse Storage
    • Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
    • Perform end-to-end security with Azure Synapse Analytics
    • Perform real-time Stream Processing with Stream Analytics
    • Create a Stream Processing Solution with Event Hubs and Azure Databricks
    • Build reports using Power BI integration with Azure Synpase Analytics
    • Perform Integrated Machine Learning Processes in Azure Synapse Analytics

    Exam information


    • Exam duration (minutes): 120
    • % extra time for non-native speakers: -
    • Number of exam questions: 40-60
    • Minimum correct questions: Variable
    • Exam style: Multiple choice
    • Open book: No
     
    Incompany

    As Azure Data Engineers learn to integrate and transform data to build structured analytics solutions!

    Training code
    CGADP203CE
    Spoken Language
    English
    Language Materials
    English
    Dayparts
    8
    Price
    €2.400,00
    excl. VAT No extra costs.

    With an Incompany training you have several advantages:

    - You choose the location
    - You experience the training with your colleagues, so it is always in line with your practice
    - The trainer can tailor explanations, examples and assignments to your organization
    - In consultation exercises can be adapted to organization-specific questions

    Request more information or a quote.

     

    What is DP-203: Data Engineering on Microsoft Azure [203T00-A]

    In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines.
    The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.
    This course uses MOC (Microsoft Official Courseware) and will be given by an experienced MCT (Microsoft Certified Trainer).
    See the below modules for more information:
    Module 1: Explore compute and storage options for data engineering workloads
    This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.

     
     

    Who should attend DP-203: Data Engineering on Microsoft Azure [203T00-A]

    The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

    Prerequisites

    Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
    Specifically completing:

    • AZ-900 - Azure Fundamentals
    • DP-900 - Microsoft Azure Data Fundamentals

    Objectives

    After completing this course, you will be able to:
    Explore compute and storage options for data engineering workloads in Azure

    • Design and Implement the serving layer
    • Understand data engineering considerations
    • Run interactive queries using serverless SQL pools
    • Explore, transform, and load data into the Data Warehouse using Apache Spark
    • Perform data Exploration and Transformation in Azure Databricks
    • Ingest and load Data into the Data Warehouse
    • Transform Data with Azure Data Factory or Azure Synapse Pipelines
    • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
    • Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
    • Analyze and Optimize Data Warehouse Storage
    • Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
    • Perform end-to-end security with Azure Synapse Analytics
    • Perform real-time Stream Processing with Stream Analytics
    • Create a Stream Processing Solution with Event Hubs and Azure Databricks
    • Build reports using Power BI integration with Azure Synpase Analytics
    • Perform Integrated Machine Learning Processes in Azure Synapse Analytics

    Exam information


    • Exam duration (minutes): 120
    • % extra time for non-native speakers: -
    • Number of exam questions: 40-60
    • Minimum correct questions: Variable
    • Exam style: Multiple choice
    • Open book: No
     

  • Related

    Fields of Expertise
    Data
     

  • e-CF competences with this course

     

    At Capgemini Academy we believe in transparency and clarity in the training landscape. That is why, in the table below, we show you to which e-CF competence this training or certification contributes. For more information about how to use the e-Competence Framework read more here. If you want to know how you can apply the e-CF within your organization, read more on this page.

    e-Competence Level12345
    A.5.Architecture Design     
    A.6.Application Design     
    B.2.Component Integration     
    B.6.ICT System Engineering