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.

In modern industry, safety is pretty much a founding principle of good business, and without it, your company would not exist for very long, whether due to reputational damage or legal action. Keeping everyone safe is vital, and the good news for you is that tech has made it easier than ever to achieve.

Historically, the manufacturing world has been defined by the "Data Silo." The factory floor-dominated by Operational Technology (OT)-and the server room-governed by Information Technology (IT)-existed as two distinct ecosystems. They spoke different languages, prioritized different metrics, and rarely interacted.

Every Operations professional knows the dread of the 3 AM pager duty call. In the software world, this usually means a server crash or a database deadlock. But in the world of manufacturing and physical operations, the stakes-and the costs-are often higher. A halted assembly line in an automotive plant or a pharmaceutical facility can cost upwards of $10,000 per minute.

With more and more business organizations adopting cloud storage to handle their data, the problem of redundancy or duplication of files has become of great concern. Duplicated information does not only eat up the precious storage space but also increases expenditure and lowers productivity. The reason why companies end up paying to store files that they do not require is because duplication of files over time occurs particularly when more than one team or department is doing a common project. Learning to detect and remove duplicate data is a key to the cost control and ability to manage data in general.