A data lake makes it possible to simplify and enhance the storage, management and analysis of Big Data,
using information from multiple different sources and keeping it in its native format.
Data lakes allow Big Data to be brought together in one place to facilitate transparent and dynamic processing and sharing. Underlying this is a Schema-on-read approach according to which data are captured in their native format, according to policies that standardize, for different types of data, the manner, timing and rules of entry.
Each element is associated with an identifier and metadata that allow it to be easily searched.
All formats and data sources can be integrated with Data Lake. Integration of data is done by preserving its native format with standardized procedures to reduce the timing of onboarding data streams.
Implementing a data lake starts withanalyzing your strategic goals and defining your data governance processes.
Information sources and flows are analyzed to provide solutions that fully meet your needs.
The analysis phase makes it easy to explore the data and create Machine Learning models by linking the tools already in use in order to extract value from the data in an automated and continuous manner.
A data lake is a solution that has advanced and articulated data storage and data analysis technologies at its core.
Simplifying, the components of a data lake can be ascribed to four categories:
A Data Lake is the ideal solution for all those realities that need to build data-driven business models, implementing cross-functional analysis on Big Data, and that have structured internal processes and professionalism to ensure data governance.
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