For businesses that need a way to store, process and make sense of vast amounts of data from a variety of sources, a data lake seems like a no-brainer. Data lakes let users analyze more data faster and at less operational expense than a lot of other models. In theory, anyway. In practice, organizations are finding more challenges with their data lakes than benefits, often because they’re using external business intelligence (BI) and machine learning (ML) services to process their data. The result: Data is constantly entering and leaving the data lake, incurring extra costs and adding unnecessary complexity.
How do you make sure your data lake lives up to its promise? In this Digital Dialogue, executives from CHAOSSEARCH and Transeo highlight the most common issues that can make a data lake less useful than it can be, from a lack of multi-API support to not having the right kind of logging capability. Find out how CHAOSSEARCH can help you take advantage of your data lake the right way.