A data lake is a place to store all kinds of Big Data, whether it’s structured data from business applications or unstructured data from mobile apps, social media, or Internet of Things (IoT) devices. Because data is stored in its natural format – structured, unstructured, semi-structured, or binary – conversion, normalization, or other processing may be needed to enable analytics across multiple data types. Most data lakes are cloud based due to the large volumes of data they store, the need for high-speed connections to distributed sources, and the need for scalability.
ETL stands for “extract, transform, and load.” Together these activities make up the process used to take data from the source and convert it into a usable format – and then move it into a data warehouse or other data store. ETL is especially useful on transactional data, but more advanced tools can also manage a variety of unstructured data types.
A data mart is a partitioned segment of a data warehouse that is oriented to a specific business area or team, such as finance or marketing. Data marts make it easier for departments to quickly access the data and insights that are relevant to them, and also to control their own data sets within the larger data store.
Data models are a foundational element of software development and analytics. A data model is a description of how data is structured, and the form in which the data will be stored in the database. A data model provides a framework of relationships between data elements within a database, as well as a guide for use of the data.
Data modeling is the process of creating data models. When creating a database or data warehouse structure, the designer starts with a diagram of how data will flow into and out of the database or data warehouse. This flow diagram is used to define the characteristics of the data formats, structures, and database handling functions to efficiently support the data flow requirements. The modeling provides a standardized method for defining and formatting database contents consistently across systems, enabling different applications to share the same data.
An enterprise data warehouse (EDW) stores all current and historical business data in one place – the embodiment of master data management, data warehousing, and a data strategy based on a holistic approach to data management. EDWs provide a welcoming environment for analytics software and the maintenance of accurate, company-wide KPIs and reporting. Many EDWs are cloud-based for scalability, access, and ease of use.