Operations | Monitoring | ITSM | DevOps | Cloud

Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on. Much of that is true, at least theoretically. In reality, the way many organizations go about building data lakes leads to a variety of problems that undercut the value their data lakes are supposed to deliver.