A data lake is an element of the Big Data infrastructure, a repository of a large amount of unstructured data generated or collected by a single company or government agency. Data in lakes is stored, as a rule, in an unsystematized form. Simply put, these are the data that “it’s a pity to throw away, and there’s nowhere to put it on.” Companies create data lakes for several reasons, including: the need to have all the materials…
The concepts of Data Lake and Process Data Storage (PIMS, Historian) are often perceived as synonymous and even confused by professionals. The reason for this is their purpose: collecting and storing data. However, this is the only thing they have in common. In fact, there is a significant difference between these two systems, ranging from architecture to the tasks for which they are built. Three key differences between a process data warehouse and a data lake are: data…
Data Warehouse VS Data Lake Data Warehouse (DWH) is a convenient solution for enterprises and organizations, the principles of which we decided to cover in our today’s article. Based on our own experience in building data warehouses for financial institutions, we will also try to present all the benefits of using DWH as clearly as possible, as well as compare it with its “competitor” – cloud storage. The data warehouse is a subject-oriented information database that is…
Data volumes are increasing at an accelerated pace every year. The number of streaming data has increased significantly, and unstructured data is increasingly eclipsing its structured counterparts. As a result, a business that works with large databases has to process information before loading, which requires a lot of time and effort. But all the same, in the end, some of the information is lost, but or could be useful in the future. And an innovative product is called upon…
Now everyone is talking about the benefits of big data. As a result, the business tries to work with large-scale databases, but faces a problem – all data is heterogeneous and unstructured, and it takes a long time to process it before loading it into the database. As a result, working with big data turns out to be too complicated and expensive, and some of the data is lost, although it could be useful in the future.…
