Benefits Of Data Lakes For SaaS

A Benefits of data lake for SaaS is defined as a data system designed primarily for unstructured data, where information can be stored in object storage units or blobs for analysis, machine learning, and other uses of the data elsewhere ex-situ. To gain a competitive edge in the market, organizations have collected a lot of data  from their consumers and key stakeholders. The demand for storage space for this data is therefore increasing, which data lakes provide.

Benefits of data lakes

Data stored in raw format

In a data lake, you don’t need to pre-model the data at ingest time. The data is simply stored in its raw form. Data analysts apply exploratory analytics to this raw data to help businesses optimize their performance.

Democratization of data

Data lakes democratize data as data is made available to all employees in the organization through a data management platform. It is left to the users to choose the data according to their business requirements.

Agility

While warehouses are ideal for repetitive tasks, data lakes are beneficial when data sources and their size are constantly changing. The agility of data lakes makes it easy for data scientists to experiment with data models and arrive at solutions that drive business growth.

Versatility

A Benefits of Data Lakes for SaaS is extremely versatile as it stores data from various sources such as social media feeds, XML, multimedia, IoT sensors, binaries and log files.

Offers schema flexibility

Data warehouses require data to be in a specific schema. Since the data lake is schema-free, it is very useful for analysts to perform experimental analysis and develop new patterns without having to worry about the initial data structure.

It enables users

Data Lakes allow data scientists to directly access and run queries on the data lake. It thus removes the dependence of analysts on IT teams and helps to save time.

Conclusion

A modern cloud data warehouse as a service is recommended to help us solve data management challenges and easily scale our data, and data integration tools to build a managed data lake.