The Reasons For Having Your Data Lake on Snowflake

 

The concept of Data Lake was introduced more than a decade back and due to its many cutting-edge features immediately had a positive impact on the business community. It was unheard of in those times to have all unstructured, semi-structured, and structured data in one single repository. But it took some time before data lakes could get over the initial teething problems and become a viable alternative to the existing data marts and data warehouses.



When Snowflake Data Lake was presented to the business community, they found that it was a solution to all their data warehousing issues and was exactly what they were looking for. The architecture of data lakes can store high volumes of data. These can be processed and analyzed at any time to get detailed insights about the various aspects of an enterprise. All the data are stored in one single location as against the earlier versions where they lay scattered in various silos such as data marts and data warehouses.

The Snowflake Data Lake architecture can be extended to allow effective data movement in the same cloud ecosystem. An example will clear the point better. For example, consider that data generated via Kafka is placed in a cloud bucket. Now, from this bucket, the data can be transformed into a columnar format and persisted into the conformed data zone. This helps businesses avoid the worries of having to choose between a data lake and a data warehouse. Snowflake can adjust automatically to unlimited data storage and computing requirements.

Given all benefits accrued to the world’s leading businesses, it is hardly surprising that they have opted for Snowflake Data Lake as their data management system.

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