Intricate machines and technologies now collect an incredible amount of data — over 2.5 quintillion bytes every day! — from equipment sensors, logs, users, consumers, and elsewhere. That data must be stored in a way that allows businesses to leverage it: to report on the past, to understand the present, and to predict the future.
Data warehouses support reporting and analytics on historical data while data lakes support newer use cases that leverage data for machine learning, predictions, and real-time analysis. The question is: do you need both and why?
We’re happy to present a conversation between thought leaders in analytics and machine learning to understand how companies are making the choice between data lakes and data warehouses and how you can make the same assessment in your organization.
Join John Riewerts, VP of Engineering at Acxiom, and Mohit Bhatnagar, SVP Product Management at Qubole, to learn:
- How data lakes and data warehouses support for the diversity of use cases and data types
- The core differences between the two technologies and the factors used to determine their usefulness
- Why the manner in which data is stored--open vs. proprietary data formats--is key to your organization’s long-term goals