Many companies in the corporate world have attempted to set up their first data lake. Maybe they bought a Hadoop distribution, and perhaps they spent significant time, money and effort connecting their CRM, HR, ERP and marketing systems to it. And now that these companies have well-crafted, centralized data repositories, in many cases…they just sit there.
But maybe data lakes fall into disuse because they’re not being looked at for what they are. Most companies see data lakes as auxiliary data warehouses. And, sure, you can use any number of query technologies against the data in your lake to gain business insights. But consider that data lakes can – and should – also serve as the foundation for operational, real-time corporate applications that embed AI and predictive analytics.
These two uses of data lakes — for (a) operational applications as well as for (b) insights and predictive analysis — aren’t mutually exclusive, either. With the right architecture, one can dovetail gracefully into the other. But what database technologies can query and analyze, build machine learning models, and power microservices and applications directly on the data lake?
Join us for this free 1-hour webinar from GigaOm Research. The Webinar features GigaOm analyst Andrew Brust, and Splice Machine CEO and Co-Founder, Monte Zweben. The discussion will explore how to leverage data lakes as the underpinning of application platforms, driving efficient operations, and predictive analytics that support real-time decisions.
In this 1-hour webinar, you will discover:
- Why data latency is the enemy and data currency is key to digital transformation success
- Why operational database workloads, analytics and construction of predictive models should not be segregated activities
- How operational databases can support continually trained predictive models
Register now to join GigaOm Research and Splice Machine for this free expert webinar.
Who Should Attend:
- Chief Data Officers
- Digital Transformation Facilitators
- Application Developers
- Business Analysts
- Data Engineers
- Data Scientists