Strata + Hadoop World London 2016
Many of us will be at the Strata Conference + Hadoop World 2016 in London, and we’d love to see you there!
Wednesday, June 1
Big data and data science have great potential for accelerating business, but how do you reconcile the business opportunity with the sea of possible technical solutions? Fundamentally, data should serve the strategic imperatives of a business—those key strategic aspirations that define the future vision for an organization. A data strategy should guide your organization in two key areas: what actions your business should take to get started with data and where to realize the most value.
John Akred and Scott Kurth explain how to solve real business challenges with data.
- Why have a data strategy?
- Connecting data with business
- Devising a data strategy
- The data value chain
- New technology potentials
- Project development style
- Organizing to execute your strategy
11:30am-12:00pm in Capital Suite 7
Spark is white-hot, but why does it matter? Some technologies cause more excitement than others, and at first the only people who understand why are the developers who use them. The secret power of big data technologies is that they promote flexible development patterns, ready to adapt to business needs, and economic scaling—but years of focusing on the label “big” has obscured much of the value to those approaching the topic. Skepticism and hype fatigue are understandable reactions.
John Akred offers a tour through the hottest emerging data technologies of 2016—including Spark, Kafka, Docker and containers, and notebooks—and explains why they’re exciting, in the context of the new capabilities and economies they bring. John looks at the excitement surrounding this year’s emerging platforms of choice and explains where these platforms fit into a complete data architecture and what they have to offer in terms of new capabilities, efficiencies, and economy of use.
What are the essential components of a data platform? John Akred and Stephen O’Sullivan explain how the various parts of the Hadoop, Spark, and big data ecosystems 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, John and Stephen explore the options and considerations for components, including:
- Acquisition: from internal and external data sources
- Ingestion: offline and real-time processing
- Analytics: batch and interactive
- Providing data services: exposing data to applications
Thursday, June 2
11:15am-11:55am in the O’Reilly Media Booth
Stephen is happy to answer questions about how to create a data platform supporting batch and interactive and real-time analytical workloads as well as tool selection and how to integrate Hadoop components with legacy systems.