Lambda Architecture is a useful framework to think about designing big data applications. This framework has been built initially at Twitter. In this presentation you will learn, based on concrete examples how to build deploy scalable and fault tolerant applications, with a focus on Big Data and Hadoop.
Target Audience: Architects & Developers
Prerequisites: Application Architecture and Integration
You will learn:
The idea is to see how to deploy Big Data Architecture and learn more about Hadoop, using concrete example
In this presentation I want to explain how Lambda Architecture helps developers, and architects to build better applications with some key requirements:
- Fault-tolerance against hardware failures and human errors
- Support for a variety of use cases that include low latency querying as well as updates
- Linear scale-out capabilities, meaning that throwing more machines at the problem should help with getting the job done
- Extensibility so that the system is manageable and can accommodate newer features easily
This includes streaming data into a large data store, and query data using different layers:
- Batch Layer: processing, transformation, machine learning
- Speed Layer: real time access to data
- Serving Layer: that expose data to user (API, Dataviz)
This will also be a good opportunity to look in detail, using real life use cases, how to deploy and use Hadoop.