Get a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will learn from the authors' experience working with large-scale enterprise customer engagements.

This book includes comprehensive coverage of how:

• To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure
• The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem
• These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions

Data platform professionals who have been working on relational data stores, non-relational data stores, and bigdata technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure.

You will understand the:

• Concepts of data lake analytics, the modern data warehouse, and advanced data analytics
• Architecture patterns of the modern data warehouse and advanced data analytics solutions
• Phases-such as Data Ingestion, Store, Prep and Train, and Model and Serve-of data analytics solutions and technology choices available on Azure under each phase
• In-depth coverage of real-time and batch mode data analytics solutions architecture
• Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight

Chapter Goal: The chapter introduces the readers to the concept & need of a data lake in this big data environment.The chapter also covers how to create a data lake & architecture patterns to be followed for data lake analytics.

No of pages    15

Sub -Topics

1.      Relational and non-relation data stores

2.      Base for data: relational and non-relational databases

3.      Warehouses of data: data warehouses

4.      Markets for data: data marts

5.      Introduction to data lake

6.      Need to create a data lake

Chapter Goal: Today, enterprises have mix of relational and non-relational stores. However, when it comes to analyzing all this data there must be a neutral platform which can understand these types of data. This introduces us to modern world concepts of distributed data storage & processing. It also talks about data sciences & machine learning concepts & how they are revolutionizing the data analysis world.

No of pages : 20

Sub - Topics: 

1.      Massively parallel processing, distributed data and spark the Hadoop

3.      Respective use cases for distributed and MPP systems

5.      Learning of machines

Chapter Goal: The chapter enlighten the users with multiple cloud-based technologies available which are scalable, agile and performance in terms of computation, storage & analytics options. It goes into details about the suggested architecture on Microsoft Azure to solve Modern data warehouse, analytics use cases.

No of pages: 20

Sub - Topics:

1.      Data travels to Cloud with added benefits

2.      Overview of phases of data analytics architecture

3.      Available products under each phase on Microsoft Azure 

Chapter Goal: This chapter covers in-depth context of this book. After we understand the background, this chapter will provide understanding of what are the phases of building entire data analytics pipeline. All the phases discussed in this book are critical to understand and any analytics solution will adhere to this common principle some way or the other. In each phase, there are different solutions to cater respective issues. It covers the data life cycle from upstream to downstream applications.

No of pages: 20

Sub - Topics:

1.      Real time and batch mode data processing

2.      Phases in data Management

·         Ingest

·         Store

·         Analytics

·         Visualization

3.      Cloud data lake architecture patterns