A data lake is a system for storing raw data, usually in large amounts. A data lake can store data collected from anywhere, such as from social media sites, internet of things (IoT) devices, websites, and even sales activity. Data in a data lake can be structured, semi-structured, or unstructured. Structured data contains rows and columns from relational databases, semi-structured data contains tags and markers to separate elements, and unstructured data includes data such as PDFs, emails, and images. In contrast with traditional data warehouses, the type of data stored in a data lake is generally raw and unprocessed. Data lakes can either be on-premises or on the cloud.
Data lakes allow data scientists to access data in its raw state, and to then decide the best ways to utilize and manage the data. Proper data management is a key component of a data lake. Because of the large amounts of data stored within them, it is easy to let data deteriorate without being utilized in any way. Such a deteriorated data lake is often referred to as a data swamp. To avoid this, proper consideration should be given regarding how best to utilize the data.
Due to the large amount of data stored in data lakes, machine learning (ML) will typically be used to analyze the data. Cloud data lakes can allow for machine learning to automatically analyze data as it is ingested. There are multiple applications that allow for this type of analysis, including Apache spark, an open-source engine that can analyze a large scale of data. Cloud data lakes also allow for increased security, as cloud providers generally invest heavily in securing the data stored in their services through the use of firewalls and enhanced login systems, such as multifactor authentication (MFA).
Businesses and organizations can benefit from the use of cloud data lakes in multiple ways, including: