Month: May 2022

News and commentary about the exam objective updates – DP-900 Microsoft Azure Data Fundamentals Exam Updates

Posted on by Theresa Holloway

News and commentary about the exam objective updates

The updates to the DP-900 exam objectives effective February 1, 2024, reveal a few noteworthy changes and refinements compared to the previous version. The following is commentary on each of the updates:

Audience Profile

■■ Before & After Update: The target audience remains consistent. The exam is aimed at candidates new to working with data in the cloud, requiring familiarity with core data concepts and Microsoft Azure data services.

150 CHAPTER 5 DP-900 Microsoft Azure Data Fundamentals Exam Updates

Describe Core Data Concepts (25–30%)

■■ Before & After Update: This section remains largely unchanged, focusing on representing data (structured, semi-structured, unstructured), data storage options, and common data workloads (transactional, analytical). The roles and responsibilities associated with these workloads are also consistently covered.

Identify Considerations for Relational Data on Azure (20–25%)

■■ Before & After Update: Both versions cover relational concepts, including features of relational data, normalization, SQL statements, and common database objects. A notable change is the explicit mention of the “Azure SQL family of products” in the updated objectives, offering a clearer focus on specific Azure services.

Describe Considerations for Working with Non-Relational Data on Azure (15–20%)

■■ Before & After Update: This section remains consistent in both versions, covering Azure storage capabilities (Blob, File, Table storage) and Azure Cosmos DB features. The emphasis on understanding Azure’s storage solutions and Cosmos DB’s use cases and APIs continues to be a crucial part of this section.

Describe an Analytics Workload on Azure (25–30%)

■■ Before Update: This section previously included details on Azure services for data warehousing, real-time data analytics technologies (Azure Stream Analytics, Azure Synapse Data Explorer, Spark Structured Streaming), and data visualization in Power BI.

■■ After Update: The updated objectives maintain the focus on large-scale analytics, data warehousing, and real-time data analytics but have removed specific mentions of technologies like Azure Stream Analytics, Azure Synapse Data Explorer, and Spark Structured Streaming. Instead, there’s a broader reference to “Microsoft cloud services for real-time analytics,” suggesting a more general approach. The section on Power BI remains similar, emphasizing its capabilities, data models, and visualization options.

General Observations:

■■ The updates indicate a shift toward a more generalized and possibly up-to-date over-view of Azure services, especially in the analytics workload section.
■■ The explicit mention of the Azure SQL family of products under relational data shows an emphasis on Azure-specific services.
■■ Overall, the changes seem to align the exam more closely with current Azure offerings and trends in cloud data management without significantly altering the core content or focus areas of the exam.

These updates suggest a continued emphasis on ensuring that candidates have a well-rounded understanding of Azure’s data services, both relational and non-relational, along with a solid grasp of analytical workloads as they pertain to Azure’s environment.

News and commentary about the exam objective updates CHAPTER 5 151

Azure Data Explorer – Describe an analytics workload on Azure

Posted on by Theresa Holloway

Azure Data Explorer

As the digital age progresses, the influx of data has transformed from a steady stream into a roaring torrent. Capturing, analyzing, and acting upon this data in real time is not just a luxury but a necessity for businesses to remain competitive and relevant. Enter Azure Data Explorer, a service uniquely equipped to manage, analyze, and visualize this deluge of information. This section is your comprehensive guide to understanding and harnessing its immense potential.

WHAT IS AZURE DATA EXPLORER?

Azure Data Explorer (ADX) is a fast, fully managed data analytics service for real-time analysis on large volumes of streaming data. It brings together big data and analytics into a unified platform that provides solutions to some of the most complex data exploration challenges.

Here are its key features and benefits:

■■ Rapid ingestion and analysis: One of the hallmarks of Azure Data Explorer is its abil-ity to ingest millions of records per second and simultaneously query across billions of records in mere seconds. Such speed ensures that you’re always working with the most recent data.

■■ Intuitive query language: Kusto Query Language (KQL) is the heart of Azure Data Explorer. If you’ve used SQL, transitioning to KQL will feel familiar. It allows you to write complex ad hoc queries, making data exploration and analysis a breeze.

■■ Scalability: ADX can scale out by distributing data and query load across multiple nodes. This horizontal scaling ensures that as your data grows, your ability to query it remains swift.

■■ Integration with other Azure services: ADX plays nicely with other Azure services, ensuring that you can integrate it seamlessly into your existing data infrastructure. Whether it’s ingesting data from Event Hubs, IoT Hub, or a myriad of other sources, ADX can handle it. Figure 4-18 shows the end-to-end flow for working in Azure Data Explorer and shows how it integrates with other services.

As a practical use case, imagine you’re overseeing the operations of a global e-commerce platform. Every click, purchase, and user interaction on your platform generates data. With Azure Data Explorer, you can ingest this data in real time. Using KQL, you can then run complex queries to gauge user behavior, analyze purchase patterns, identify potential website hiccups, and more, all in real time. By using this data-driven approach, you can make instantaneous decisions, be they related to marketing strategies or website optimization.

Azure Data Explorer stands as a formidable tool in the data analytics space, empowering users to make the most of their data. Whether you’re a seasoned data analyst or just starting, ADX offers a blend of power and flexibility that can transform the way you view and utilize data.

124 CHAPTER 4 Describe an analytics workload on Azure

FIGURE 4-18  Azure Data Explorer