Lakes are beautiful. If for some reason you don’t live anywhere near one I feel a little sorry for you. Aside from the obvious aesthetics, there is a natural beauty in how they work. On the surface, they may seem to be little more than a hole in the ground full of water. However, dip just below the surface, they are teeming ecosystems with a variety of species and smaller systems coexisting. They also provide abundant raw resources, in the form of fish, nutrient rich sediment, and of course water. One simply need to know how to extract those resources and put them to use. Those aspects form the basis of what is known as the data lake, or the lakehouse method of data management.
In this method, data is stored in its raw, unprocessed form in servers that are designed to be easy to access. Ah, but how useful is that data if it remains in a raw format? Not very, of course. Embedded in the data lake are algorithms that would normally be applied outside of the servers, either before or after storage. In the lakehouse model, the algorithms are there, swimming amongst the data, processing, sorting and moving it to where it needs to go. These ETLs (extraction transfer loads) can be set up to be constantly working, so as data streams into the lake, those algorithms will funnel into your pipeline, giving you a constant feed of freshly processed data. Perhaps best of all, there can be multiple ETLs operating in the data lake at once, making it possible to benefit from multiple analyses of the same or different data simultaneously. The lakehouse method allows all of this to take place with data in real time. Data can be processed and removed from the lake almost as quickly as it streams in.
This is in stark contrast to the older data warehouse method. In this model, data is stored, typically in a processed form in servers for later use. If a business wanted to know something, it would have to go into the servers and find it, with each new problem requiring a new set of software solutions to be engineered. This inevitably leads to stacks upon stacks of servers doing nothing more than storing data that isn’t getting or may never get used. All it does is take up valuable space and resources to simply maintain the servers. What’s more, different kinds of data would often be stored on different servers. Video here, audio there, text on another, and so on. This is just fine if you only need to analyze one kind of data. However, in the increasingly lighting-paced world we live in, that isn’t good enough. Businesses demand the ability to analyze multiple kinds of data at once, integrating them in order to get a better idea of the big picture. The lakehouse method of data storage and analysis allows for quicker and easier results that are more up to date than anything possible via the warehouse.
The lakehouse is also much better suited to our modern cloud computing world. Cloud computing is designed to be fast paced, easily shared, and quickly processed. It also generates data that businesses are interested in tracking faster than ever before. The data warehouse is just too slow and clunky compared to the data lake.
Whoever can best store, process and analyze data in the modern world is best suited to lead whatever field they are in. TARTLE understands this and that’s why we are working to return the power of your own data to you. If you wish, you can add your own data to the lake, working to provide a steady feed of source data, rather than that collected from third parties. This provides better data to the businesses that want to use it and allows you to actively participate in the process and be rewarded for it.
What’s your data worth?
