The world is producing an ever-increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. The Hadoop ecosystem and AWS provide a plethora of tools for solving big data problems. But what tools should you use, why, and how?
Siva Raghupathy demonstrates how to use Hadoop innovations in conjunction with Amazon Web Services innovations, showing how to simplify big data processing as a data bus comprising various stages: collect, store, process/analyze, and consume. Siva then discusses how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on before providing reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Siva Raghupathy leads the Americas Big Data Solutions Architecture team at AWS, where he guides developers and architects in building successful big data solutions on AWS. Previously, as a principal technical program manager for AWS Database Service, Siva gathered emerging NoSQL requirements and wrote the first version of DynamoDB product specification. Later, as a development manager for Amazon Relational Database Services (RDS), he drove several enhancements. Prior to AWS, Siva spent several years at Microsoft.
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