Chapter 1: Introduction and The Need of Data LakeChapter 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 2: Data Just Got Bigger 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
2. Distributed systems vs massively parallel processing systems (MPP) 3. Respective use cases for distributed and MPP systems
4. Science for data 5. Learning of machines
6. Overview of data analytics and advanced data analytics Chapter 3: Emergence of Cloud Lakes 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 4: Phases in Managing Data Analytics Pipeline 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
Chapter 5: Data Ingestion in the Lake Chapter Goal: The chapter talks about the limitations about the traditional storage & how the big data technologies has emerged as the
About the Author: Harsh Chawla has been working on data platform technologies for last 14 years. He has been in various roles in the Microsoft world for last 12 years, going from CSS to services to technology strategy. He currently works as an Azure specialist with data and AI technologies and helps large IT enterprises build modern data warehouses, advanced analytics, and AI solutions on Microsoft Azure. He has been a community speaker and blogger on data platform technologies.
Pankaj Khattar is a seasoned Software Architect with over 14 years of experience in design and development of Big Data, Machine Learning and AI based products. He currently works with Microsoft on the Azure platform as a Sr. Cloud Solution Architect for Data & AI technologies. He also possesses extensive industry experience in the field of building scalable multi-tier distributed applications and client/server based development.
You can connect with him on LinkedIn at https: //www.linkedin.com/in/pankaj-khattar/
