Learn about the differences between big data platforms and big data applications.
- [Instructor] Before we start this course, I want to make a clear distinction between what a software application is and how it is different from a software platform. This is because we are going to focus on architecting applications in this course, not platforms. A software platform or technology provides a generic set of capabilities that can be utilized by others to build specific applications.
Its goal is to help others build applications utilizing these generic capabilities. Examples of software platform capabilities include horizontal scaling, parallelism, security and access, networking, storage and retrieval, monitoring and management. Some popular big data platforms include Hadoop, Apache Spark, MongoDB, Apache Kafka, Apache Mesos, and Apache Mahout.
A software application, on the other hand, solves a specific business use case. It focuses on achieving the end goal of a business. It may utilize one or more software platforms for this purpose. Examples of such applications include customer relationship management, eCommerce website, custom reporting and analytics, predicting customer churn.
This course focuses on architecting software applications. It uses software platforms where possible to solve specific business use cases. I want to make this important distinction so as to avoid any misunderstandings about the goals of this course.
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.
- 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
Skill Level Advanced
Big Data Foundations: Program Managementwith Alan Simon1h 11m Intermediate
1. Intro to Big Data Applications
2. Use Case 1: Data Warehouse (DW)
3. Use Case 2: Log Accumulation (LA)
4. Use Case 3: IT Operations Analytics (OA)
5. Use Case 4: Customer 360 (C360)
6. Use Case 5: Customer Analytics (CA)
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