Architecting a Big Data Analytics Platform (Part 2)

What are the essential components of a data platform? This tutorial will explain how the various parts of the Hadoop, Spark and big data ecosystem fit together in production to create a data platform supporting batch, interactive, and real-time analytical workloads.

By tracing the flow of data from source to output, we’ll explore the options and considerations for components, including:

Acquisition: from internal and external data sources
Ingestion: offline and real-time processing
Storage
Analytics: batch and interactive
Providing data services: exposing data to applications

We’ll also give advice on:

Tool selection
The function of the major Hadoop components and other big data technologies such as Spark and Kafka
Integration with legacy systems

A leading expert on big data architecture and Hadoop, Stephen O'Sullivan brings 20 years of experience to creating scalable, high-availability data and applications solutions. A veteran of WalmartLabs, Sun, and Yahoo!, Stephen leads data architecture and infrastructure.

A problem solver by nature, Heather is passionate about helping organizations leverage data to drive competitive advantage. She draws across a diverse background in business and technology consulting to find the best solutions for her clients’ toughest data problems.