Architecting Big Data Applications: Real-Time Application Engineering

Real-time systems have guaranteed response times that can be sub-seconds from the trigger. Meaning that when a user clicks a button, your app better respond—and fast. Architecting applications under real-time constraints is an even bigger challenge when you're dealing with big data. Excessive latency can cost you money, in terms of system resources consumed and customers lost. Luckily, big data technology and efficient architecture can provide the real-time responsiveness your business needs. In this course, you can learn about use cases and best practices for architecting real-time applications with technologies such as Kafka, Hazelcast, 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 real-time streaming, predictive analytics, parallel processing, and pipeline management. 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: fraud detection and product recommendations
Technology options
Designing solutions
Best practices