Deep Learning, Real-time Analytics Take Center Stage at Strata

Published
  • April 07 2016, 6:30am EDT

By all appearances, last week’s Strata & Hadoop World conference in San Jose, CA, was a success, with over 5,000 attendees and over 100 exhibitors. Organizations of every size and from every industry were present, but many shared some of the same concerns and challenges when it comes to their data management and data analytics efforts.

Information Management spoke with Ron Bodkin, founder and president of Think Big, a Teradata company, for his take on what attendees were most wanting answers to.

 

Information Management: What are the most common themes that you are hearing among conference attendees and how do those themes align with what you expected?

Ron Bodkin: There was a lot of excitement about continuous applications – application of streaming techniques and more advanced state management to allow for Big Data applications that perform complex functionality in real time.

There was also a lot of excitement about applying more advanced deep learning techniques to build more sophisticated predictive models using the power of big data. Both are areas we are seeing as increasingly important for our practice and with our more advanced customers. You typically see a more advanced crowd at this conference and O’Reilly tends to emphasize emerging technologies rather than case studies from mainstream adoption.

 

IM: What are the most common challenges that attendees are facing with regard to data management and data analytics?

RB: It’s a mix. Some attendees are figuring out how to get started – what architectures to use and what technologies to use. Others are trying to get the right mix of governance and flexibility to build data lakes they can trust while still moving quickly and when to invest in building data models. Still others are working on how to scale the use of data science to drive analytics that produce business results. The most advanced attendees are asking how to build continuous applications for big data to drive real-time results.

 

IM:  What are the most surprising things that you are hearing from attendees?

RB: It’s refreshing to hear a lot more urgency and commitment to deliver results. In past years there has been more of a bent towards exploration but this year attendees are down to business and want to know how to achieve results.

IM: What does your company view as the top data issues or challenges in 2016?

RB: The bulk of the market is trying to get the right architecture, governance and security to manage data across the ecosystem including both data lakes and enterprise data warehouses. The biggest challenges they are facing are around how to scale ingestion to hundreds of feeds; what are the right processes to provide visibility into data quality and to enable collaboration while preserving agility.

More advanced companies are looking at how to enable better self-service analytics and how to scale the use of data science to speed up results and/or to drive new initiatives with Spark. In parallel, many companies are discovering that managing and operating Hadoop environments is complex, especially as they are increasingly running workloads with meaningful Service Level Agreements.

 

IM: How do these themes and challenges relate to our company’s market strategy this year?

RB: We are laser-focused on helping companies succeed with Big Data. We are investing in frameworks, partnerships and patterns to drive Data Lake success as part of an analytic ecosystem. We are also investing in scaling our Managed Services for Hadoop and our Spark analytics capabilities.

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