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Mastering Azure Data Engineering: Part 4 - Hands-On
Rating: 4.6 out of 5(8 ratings)
1,370 students

Mastering Azure Data Engineering: Part 4 - Hands-On

Azure Data Mastery: Techniques & Best Practices for Data Factory, Synapse Analytics, DP-900, and DP-203
Created byLakshmi Narayana Singilidevi
Last updated 6/2024
English
English [Auto],

What you'll learn

  • Integrate Azure Blob Storage with SQL databases using Azure Data Factory, enabling seamless data transfer and synchronization.
  • Automate the creation of tables within Azure Data Factory pipelines, streamlining data ingestion and processing workflows.
  • Implement parameterization techniques in Azure Data Factory to enhance pipeline flexibility and reusability.
  • Utilize stored procedure activities within Azure Data Factory to execute complex data processing logic and manage database operations efficiently.

Course content

1 section • 7 lectures • 1h 50m total length

  • 16.Integrate Blob to SQL in Data Factory: Step-by-Step21:02

    Learn to copy data from blob storage toAzure SQL Database usingAzure Data Factory, building a copy data pipeline, configuring source and sink, and auto creating the target table.

  • 17.Automate Table Creation in Data Factory: Step-by-Step4:57

    Automate table creation in Azure Data Factory by auto creating tables from blob storage to Azure SQL Database, then trigger and monitor the pipeline.

  • 18.Parameterization in Data Factory26:17

    Learn to parameterize Azure Data Factory pipelines by passing dynamic database and table name parameters to a single pipeline, enabling dynamic source and destination datasets.

  • 19.Stored Procedure Activity in Azure Data Factory14:35

    Showcases creating and running a stored procedure through Azure Data Factory, connecting to Azure SQL Database, using dynamic values, and monitoring the pipeline.

  • 20.Transform Data with Derived Column in Data Factory27:38

    Create and run an Azure Data Factory data flow to derive columns, such as incrementing salaries and uppercasing names, then publish the pipeline to store results in blob storage.

  • 21.Efficient Data Filtering: Select All Activity in Data Factory15:23

    Learn to create a select statement in Azure Data Factory using a data flow to filter and project employee fields from blob storage and publish results to an output container.

  • Follow this channel for updates: https://www.youtube.com/@MithrammaIT/playlists0:14

Requirements

  • Yes, learners will need access to an Azure subscription or Azure Free Trial to complete the hands-on labs and exercises in this course. A subscription allows students to interact with Azure services directly, gaining practical experience in deploying, managing, and optimizing Azure Data Services. The Azure Free Trial provides a limited-time opportunity to explore Azure services with a credit balance, making it an ideal option for learners who do not have an existing subscription.

Description

Mastering Azure Data Engineering continues with Part 4, focusing on advanced Azure skills crucial for proficient data engineering. This module delves into integrating Azure Blob storage with SQL databases, automating table creation, parameterization techniques, stored procedure activities, and data transformation using derived columns. Participants will engage in step-by-step tutorials, hands-on labs, and practical exercises to solidify their understanding and expertise in these essential Azure Data Factory functionalities. By the end of the course, learners will possess the skills necessary to streamline data integration, automation, and transformation processes within Azure Data Factory, empowering them to tackle complex data engineering challenges with confidence.

Course Objectives:

  1. Master the process of integrating Blob storage with SQL databases in Azure Data Factory.

  2. Automate table creation tasks within Azure Data Factory pipelines for enhanced efficiency.

  3. Implement parameterization techniques to enhance flexibility and reusability in data workflows.

  4. Utilize stored procedure activities effectively for executing database operations within Azure Data Factory.

  5. Learn how to transform data using derived columns, enabling advanced data manipulation capabilities.

  6. Efficiently filter data using the Select All activity in Azure Data Factory to optimize data processing workflows.

Target Audience:

  • Experienced Data Engineers

  • Data Architects

  • Azure Developers

  • IT Professionals seeking advanced Azure data engineering skills

Prerequisites:

  • Proficiency in Azure Data Factory fundamentals.

  • Familiarity with SQL databases and data transformation concepts.

  • Basic understanding of Azure services and cloud computing principles.

Delivery Format:

  • Instructor-led training sessions

  • Hands-on labs and tutorials

Who this course is for:

  • This course is ideal for: 1. Aspiring data professionals seeking to enhance their knowledge and skills in Azure Data Services. 2. IT professionals aiming to validate their expertise with the DP-900 certification. 3. Developers interested in leveraging Azure for data storage, analytics, and visualization. 4. Anyone looking to transition into a career involving cloud-based data management and analytics. 5. Students and learners eager to explore the fundamentals of Azure Data Services in a practical, hands-on manner.

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