|Microsoft Azure Data Fundamentals Exam Ref DP-900 (Microsoft Press)|
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Author: Daniel Seara & Francesco Milano
This book aims to introduce Azure data services and their use with different types of data and workloads, how does it fare?
As a consequence of the current Covid pandemic, businesses are increasing their use of the cloud, so it makes sense to learn about cloud systems, such as Microsoft’s Azure.
How can you break into a new subject area, like Azure? One way is to study for the exams - although perhaps not as useful as practical hands-on experience, it does at least offer an introduction to the subject, and you’ll probably come into contact with a wider range of topics than in a hands-on role. This book aims to “Demonstrate your real-world foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services.”
The book is targeted at business users, functional consultants, and other professionals that want to know more about the core Azure data services and their use with relational data, non-relational data, and analytics workloads. It assumes you have some basic knowledge of core data concepts and are beginning to work with data in the cloud. This book is structured to match the exam syllabus.
Below is a chapter-by-chapter exploration of the topics covered.
Chapter 1: Describe core data concepts
The book opens with a look at the core data workloads, namely:
Each is described with examples, and helpful related information (e.g. in the context of the modern data warehouse). The discussion of batch processing includes a look at Big Data, and the associated 5 Vs (volume, velocity, variety, veracity and value). The discussion on variety is extended to include the common division of data into: structured, semi-structured, and unstructured formats – these provide the data that underlie the next 2 chapters (i.e. relational and non-relational data). There’s a very helpful table comparing stream and batch processing, highlighting data volumes, latency, memory usage etc. Next, there’s a look at the characteristics of relational data (i.e. tabular structure), including a brief theoretical interlude, before looking at the more practical usage (e.g. naming conventions).
The chapter moves on to look at data analytics, starting with the components of the analytics curve (i.e. descriptive, diagnostic, predictive, and prescriptive analyses). You might also need to know about Cognitive analysis for the exam – I’m not sure why it wasn’t discussed here. This is extended with a brief look at the Team Data Science Process, which provides a useful framework for implementing projects. There’s a brief description of some related Azure services. Next, there’s a discussion around ETL and ELT, which allows data to be moved and transformed between a source and target system. This is followed with a discussion on data visualization and some popular chart types.
This chapter introduces concepts and terms that lay the foundation to understanding data processing in general, and processing in Azure in particular. The chapter concludes with a Thought Experiment, which provides a problem scenario to answer questions about. The chapter is easy to read, with helpful links to more in-depth topics, a useful summary, helpful exam tips throughout, useful diagrams and tables, good flow and links between topics within the chapter and between chapters. These traits apply to the whole of the book.
Chapter 2: Describe how to work with relational data on Azure
Relational databases have been around for a relatively long time (1970s). Structured data is arranged as rows and columns in a table. Tables are linked via relationships (primary keys and foreign keys). The chapter starts with a look at describing relational data workloads. This tends to be either Online Transactional Processing (OLTP) typically involving relatively fast queries applied to a small amount of data, or Online Analytical Processing (OLAP) which typically involves relatively slow queries applied to large amounts of data. The contrasting processing systems often need the data to be modeled in a certain way (normalized for OLTP, and star schema for OLAP). Both approaches are outlined with useful explanations and diagrams, together with helpful discussions on indexes, views, and stored procedures.
After laying down the background, the chapter next looks at what Azure provides for relational services. First there’s an overview of the delivery models on offer (i.e. IaaS, PaaS, SaaS), where you trade off the amount of control you have over the hardware/software against the ease of maintenance etc. Next, the various database offerings are explained, namely:
Each offering is explained with helpful screenshots and tables. The walkthroughs themselves don’t include screenshots, but do include details of the relevant options – this is probably a smart move, since the Azure screen content can change rapidly.
The next section takes a look at various basic management tasks you’re likely to perform with your relational database systems, including:
The chapter ends with a look at some SQL basics, including DDL (e.g. CREATE, ALTER, DROP a table), and DML (e.g. SELECT, INSERT, DELETE data).
This chapter should feel very familiar if you’re from a traditional relational database background. Some of the material might seem quite basic (e.g. what is an index). It provides a good introduction to what relational databases are on offer via Azure.
|Last Updated ( Tuesday, 07 September 2021 )|