Architecting Big Data Applications: Batch Mode Application Engineering (2017)

Course details

Batch mode consolidates data-related operations in order to reduce the load on networks. Batch mode helps software architects build big data applications that operate smoothly and efficiently under real-world conditions. In this course, you can learn about use cases and best practices for architecting batch mode applications using technologies such as Hive and Apache Spark.

There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for data acquisition, transport, processing, storage, and service. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthand.

Learning objectives
Components of a big data application
Big data app development strategies
Use cases: archiving audit logs and performing customer analytics
Technology options
Designing solutions
Best practices

Concepts

0. Introduction

01 - Welcome
02 - Platforms vs. applications
03 - Software architecture vs. design
04 - Notes on use cases

1. Intro to Big Data Applications

05 - Big data characteristics
06 - Traditional vs. big data applications
07 - Big data application modules
08 - Technologies for big data
09 - Strategy for big data apps

2. Use Case 1 - Data Warehouse (DW)

10 - DW - Analyze the problem
11 - DW - Outline the solution
12 - DW - Consider technologies
13 - DW - Lay out the architecture
14 - DW - Design key elements
15 - Best practices - Data acquisition

3. Use Case 2 - Log Accumulation (LA)

16 - LA - Analyze the problem
17 - LA - Outline the solution
18 - LA - Consider technologies
19 - LA - Lay out the architecture
20 - LA - Design key elements
21 - Best practices - Data transport

4. Use Case 3 - IT Operations Analytics (OA)

22 - OA - Analyze the problem
23 - OA - Outline the solution
24 - OA - Consider technologies
25 - OA - Lay out the architecture
26 - OA - Design key elements
27 - Best practices - Data processing

5. Use Case 4 - Customer 360 (C360)

28 - C360 - Analyze the problem
29 - C360 - Outline the solution
30 - C360 - Consider technologies
31 - C360 - Lay out the architecture
32 - C360 - Design key elements
33 - Best practices - Data storage

6. Use Case 5 - Customer Analytics (CA)

34 - CA - Analyze the problem
35 - CA - Outline the solution
36 - CA - Consider technologies
37 - CA - Lay out the architecture
38 - CA - Design key elements
39 - Best practices - Data service

Conclusion

40 - Next steps

Architecting Big Data Applications: Batch Mode Application Engineering (2017)

Authors

Kumaran Ponnambalam