Data Lake – Why is it important for you?
Milanamos and its mother company Predictive Mobility enjoyed multiple rewards since 2014 for bringing innovations to market, the latest being rewarded within the Top 10 Worldwide Big Data Companies in 2020. Being these successes, and the recognition of our continuous effort in research and development, one master piece is our database.
Data Lake: What is it?
We have a developed a Data Lake database, allowing to gather, store, and compute all type of data, on their native form. A database is a storage location that houses structured data. We usually think of a database on a computer—holding data, easily accessible in a number of ways. Arguably, you could consider your smartphone a database on its own, thanks to all the data it stores about you.
The next step up from a database is a data warehouse. Data warehouses are large storage locations for data that you accumulate from a wide range of sources. For decades, the foundation for business intelligence and data discovery/storage rested on data warehouses. Their specific, static structures dictate what data analysis you could perform.
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration. Data lakes exploit the biggest limitation of data warehouses: their ability to be more flexible. This agility makes it easy for data developers and data scientists to easily configure and reconfigure data models, queries, and applications.
Data Lake: Your Benefits!
- A Data Lake system supports non-traditional data types, like web server logs, sensor data, social network activity, text and images;
- The data loading process is quick, and Data Lake adapt to change very well, at the opposite of Data Warehouse requiring plenty of time and resource for small modifications;
- Data lakes contain all data and data types, which enables users to access data before it has been transformed and structured, this will allow users to get their results faster than a traditional data warehouse approach;
- Data Lakes are recognized for predictive and advanced analytics while Data Warehouse are performing only for operational reports.
