Data lakes are data architecture silos where massive volumes of data can be stored in one place for analytics at a later date. In the past, there were several components of a data storage system like data marts, data warehouses, and more. But now so many are not required as most databases are run in the cloud.

Snowflake Data Lake

A cloud-operated data warehousing solution, the Snowflake Data Lake offers unlimited computing and storage facilities. Users can use computing and storage resources as per requirements by paying only for the quantum used. This is critical for businesses, especially small and mid-sized ones as they can scale up quickly to meet any sudden surge in demand for storage facilities without investing in additional hardware and software.

Snowflake Data Lake is also a high-performance data warehousing solution. Multiple intricate queries can be simultaneously executed by large numbers of users without facing any lag or drop in speeds. Further, because of the extendable architecture structure, it is possible to seamlessly load databases into the cloud environment, eliminating the need for organizations to choose a specific data lake or data warehouse to operate on. Data generated by Kafka, for example, can be moved to a cloud bucket from where the data can be transformed to a columnar format with Apache Spark.

Snowflake Data Lake can also load native data and enable cutting-edge analysis in mixed data formats. Its scalability also enables it to adjust and react instantly to any increase or decrease in data volumes.

Snowflake Data Lake Features

There are several technologically advanced features of Snowflake Data Lake.

•Scalable capabilities: Snowflake Data Lake has dynamic and highly scalable computing resources that vary according to the volume of data requirements and the number of users. When there is a change in computing needs, the volume of resources allotted scales up and down automatically without affecting running queries. This auto-adjustment by the compute engine works seamlessly to meet any heavy demand for resources without affecting the speed and performance.

•Single point data storage: Huge volumes of both semi-structured and structured data like JSON, CSV, tables, Parquet, ORC, and more are directly ingested into Snowflake Data Lake without using separate silos for data storage.

•Affordable Storage of data: The Snowflake Data Lake platform offers highly flexible and affordable data storage with users required to pay only the base cost of data usage of Snowflake cloud providers, namely Microsoft Azure, Amazon S3, and Google Cloud.

•Assured data consistency: With guaranteed data consistency on the Snowflake Data Lake, data can be easily manipulated to carry out cross-database links and multi-statements transactions.

With all these features at their disposal, Snowflake Data Lake users can maximize scaling options along with affordable computing and storage opportunities. However, DBAs have to face the challenge of correlating the older features of data lakes with that of the advanced Snowflake Data Lake.