Data lakes are a relatively new concept. Data lakes are the solution to data silos, where data gets locked away and becomes inaccessible to other systems, departments, and users. For a more technical explanation, data lakes are architectures centered around the collection, storage, and management of data, designed to house huge quantities of data that are stored in their native format (or darn close to it). Continue Reading
If you’re in the position of managing organizational data, you’ve probably heard about the concept of data lakes. While data lakes are marked by their size, the primary difference between data lakes and the good ol’ data warehouse is that the data lake stores data in its native format. This means that you don’t have to determine a use for the data until it’s needed. You can store it now and worry about use cases later. Well, sort of. Data lakes are powerful tools as organizations begin to make headway in finding uses and tools to use big data. But you can’t just build a data lake and hope people find uses for it. Here are the biggest mistakes to avoid when constructing your data lake. Continue Reading
Have you ever seen the American television show ‘Hoarders: Buried Alive?’ If not, it’s an interesting sight to behold. The show chronicles homeowners who keep everything they can get their hands on, regardless of its worth or significance. Some people hoard a particular item, such as milk cartons or bottle tops. Others just hoard anything and everything they can get their hands on: items bought from shopping channels, collectibles, old clothing, and even garbage.
If you’re in the IT business, you’ve probably heard about the data lake concept. Even though the term was coined a few years ago, it’s only recently being adopted by enterprises. And it’s still pretty controversial. Continue Reading
As big data becomes a mainstay in the business, many organizations are abandoning the data warehouse for data lakes. With a data lake, you don’t have to worry about the relationships among the data or what the data is good for. You just pour all the data in and let it swim around until you’re ready to use it. When you’re ready to get started building and filling your data lake, here are some best practices to keep in mind for success. Continue Reading