It\u2019s a common tactic to combine two technologies for synergy sake, but Lentiq really has a unique idea. It is combining the concept of the data lake with edge computing into what it calls \u201cinterconnected micro data lakes,\u201d or data pools.\n\u201cData pools\u201d are micro-data lakes that function like a data lake while supporting popular apps such as Apache Spark, Apache Kafka, and Streamsets software, or \u201ceverything a data scientist or data engineer needs,\u201d according to the company.\nThe data pools exist independently across different clouds, and governance rules are enforced only when the data moves, so each department will have the tools needed for their use cases and access to the data they need.\n\nLentiq says customers can build Data Lake as a Service with EdgeLake in fewer than 10 minutes, and the service comes with data and metadata management, application management, notebook sharing, data sharing, infrastructure, and budget management.\n\u201cLentiq EdgeLake\u2019s goal is to allow as many users as possible inside an organization to access data and to offer the environment where one can perform analytics and machine learning in a friendly manner. We strongly believe transformative innovation can only be achieved through a human-centric machine learning approach for all data projects,\u201d the company said in a blog post announcing the product.\nOn the surface the idea seems totally contradictory because data lakes are central repositories. Data lakes are a newer take on mass repositories like we first saw with data warehouses, except they operate very differently.\nFor starters, a data lake holds unstructured data, like images, PDFs, audio, logs, and so forth. Data warehouses are highly structured row and column data. Second, a data lake does not require special hardware or software, unlike a data warehouse. You can use any device that supports a flat file system, even a mainframe if you want.\nThe big difference, though, is that in a data warehouse, you process the data before it goes into storage. With a data lake, you fill it with whatever and process it later when you need it.\nAnd that\u2019s where it flies in the face of the edge. The edge is supposed to act as a filter for unnecessary data. An edge system getting car data, for instance, doesn\u2019t want sensor readings saying everything is normal, it wants the unusual or aberrant. That is what gets sent up to the main data center. And that's how a data warehouse operates.\nSo, it will be interesting to see how far and wide they push this. I think they are using the term \u201cedge\u201d because it\u2019s the hot buzzword, when they really are targeting departments and remote locations\/offices.