DP-203

DP-203: Data Engineering on Microsoft Azure Masterclass with Labs [Coming Soon!]

Introduction
Master data engineering, design and implement data storage, develop, secure, monitor, and optimize data storage and data processing. From Zero to Hero!

What you will Learn in this course?

This course takes you on a journey from the fundamentals, perfect for beginners, and smoothly transitions to advanced topics. You’ll not only get a solid start but also gain insights into how real-world systems apply these concepts to create robust, fault-tolerant solutions.

Dive in and get your hands on with real-world skills!

What Are the Requirements or Prerequisites for taking the DP-203 course?

  • This course is designed to teach the concepts related to Azure Data Engineering with hands-on and practical experience. To fully participate and follow along, it is recommended to have an active Azure account but it is not mandatory.
  •  You must have a solid knowledge of data processing languages, including Python, Scala, and SQL.
  • It would help if you comprehended parallel processing and data architecture patterns. Proficiency in utilizing the following is necessary for developing data processing solutions:
    • Azure Data Factory
    • Azure Synapse Analytics
    • Azure Stream Analytics
    • Azure Event Hubs
    • Azure Data Lake Storage
    • Azure Databricks
  • 5 Important Data Engineering Concepts in Microsoft Azure – https://kloudsaga.com/important-data-engineering-concepts-microsoft-azure/

Who is this course for?

This course is for those who want to enhance their skills in data engineering concepts and pursue the Microsoft Certified: Azure Data Engineer Associate.

What is Skills Objectives for DP-203?
The skills are divided into these functional components-

  • Design and implement data storage (15–20%)
  • Develop data processing (40–45%)
  • Secure, monitor, and optimize data storage and data processing (30–35%)

What is DP-203 Exam Summary?

Microsoft Data Engineering Associate Exam Summary:
Exam Name: DP-203: Data Engineering on Microsoft Azure
Exam Code: DP-203
Cost: $165 USD*
Duration: 120 Minutes
Number of Questions: 45–60
Passing Score Required: 700/1000
Schedule Exam: Pearson VUE
Practice Tests: https://courses.kloudsaga.com/courses/microsoft-azure-data-engineer-exam-practice-sets

Azure Storage Overview

Azure Storage is Microsoft’s cloud storage solution designed for modern data storage scenarios. It provides highly available, massively scalable, durable, and secure storage for various data objects in the cloud. Here are the key points about Azure Storage:

  1. Data Objects:
    • Azure Storage handles different types of data objects, including:
      • Azure Blobs: A massively scalable object store for text and binary data. It also supports big data analytics through Data Lake Storage Gen2.
      • Azure Files: Managed file shares for cloud or on-premises deployments.
      • Azure Queues: A messaging store for reliable communication between application components.
      • Azure Tables: A NoSQL store for schemaless storage of structured data.
      • Azure Disks: Block-level storage volumes for Azure Virtual Machines (VMs).
  2. Key Benefits:
    • Durable and Highly Available:
      • Data redundancy ensures safety during transient hardware failures.
      • Replicate data across data centres or geographical regions for additional protection.
    • Secure:
      • All data written to an Azure storage account is encrypted by the service.
      • Fine-grained access control allows you to control who can access your data.
    • Scalable:
      • Azure Storage is designed to meet the storage and performance needs of modern applications.
    • Managed:
      • Microsoft handles hardware maintenance, updates, and critical issues.
    • Accessible:
      • Data stored in Azure Storage is accessible from anywhere in the world over HTTP or HTTPS.
      • Client libraries are available for various languages (e.g., .NET, Java, Python, Node.js).
  3. Tools and Interfaces:
    • Developers and IT professionals can use:
      • Azure PowerShell and Azure CLI for scripting data management tasks.
      • Azure portal and Azure Storage Explorer for user-interface tools.

Example Scenarios

  1. Web Applications:
    • Store static assets (images, CSS, JavaScript) in Azure Blobs.
    • Use Azure Files for shared configuration files across VMs.
  2. Big Data Analytics:
    • Leverage Azure Data Lake Storage Gen2 for large-scale data analytics.
    • Process data using Azure Databricks, HDInsight, or other analytics services.
  3. Backup and Disaster Recovery:
    • Use Azure Blob storage for backup and archival.
    • Replicate data across regions for disaster recovery.
  4. IoT and Telemetry Data:
    • Store sensor data, logs, and telemetry in Azure Tables or Blobs.
    • Analyze data using Azure Stream Analytics or other services.

Azure Storage services are foundational for building scalable, reliable, and secure cloud applications.

Azure Data Lake Gen-2 Storage

Data can store structured, semi-structured, and unstructured files in the data lake and then consume that from big data processing technologies, such as Apache Spark.
Azure Data Lake Storage Gen2 provides a cloud-based solution for data lake storage in Microsoft Azure, and underpins many large-scale analytics solutions built on Azure.
Azure Data Lake Storage Gen2 is constructed on the foundation of Azure Blob Storage, combining the strengths of both. Here’s what it entails:
1. Blob Storage: This economical option offers high availability and lifecycle management features. It’s an excellent choice for storing unstructured data.
2. Data Lake Storage: Beyond Blob Storage, Data Lake Storage provides additional capabilities:

  • Hierarchical storage: Organize your data in a structured manner.
  • Fine-grained security: Control access at a granular level.
  • Hadoop compatibility: Seamlessly integrate with Hadoop-based frameworks.

For efficient big data processing on Azure, follow this approach:
1. Store your data in Azure Data Lake Storage (ADLS).
2. Process it using Spark (a faster version of Hadoop) on Azure Databricks.
This combination ensures optimal performance and flexibility for your big data workloads.