Snowflake Computing is rolling out new capabilities for the Snowflake Elastic Data Warehouse that the startup says will automate and simplify administrative tasks for its cloud data warehouse service and improve data protection for diverse data and SQL queries in the cloud.
Partners said the new automation capabilities fit with their customers' demands to make data warehousing as simple as possible and allow users to focus less on administration and more on data analysis.
"More clients we are talking to don't want to manage [data warehouse] infrastructure," said Vikas Punna, vice president of the data management practice at Unico Solution, a New York-based solution provider with practices in data warehousing, data migration, business intelligence and master data management. "Our clients want minimal administrative work."
Snowflake began offering its cloud-based Snowflake Elastic Data Warehouse service in June 2015 as an alternative to traditional on-premise data warehouse systems. Such data warehouse projects can take months and even years to build, potentially cost millions of dollars, and have high failure rates. Increased use of big data technologies such as Hadoop and Spark in recent years has only added to that complexity.
Snowflake's value proposition is that businesses can use its service to set up a cloud data warehouse in a fraction of the time, at far lower costs. Snowflake is competing against other cloud data warehouse systems, including Amazon Redshift and Google BigQuery.
Since the launch of the San Mateo, Calif.-based company's service, Snowflake has attracted more than 200 business customers of all sizes – some with more than a petabyte of stored uncompressed data, said John Bock, products and marketing vice president, in an interview with CRN.
Snowflake updates its code on an almost weekly basis, and Bock said the company has been rolling out the new features and functionality in recent weeks.
Topping the list of enhancements is a new set of automated multicluster warehouse capabilities for scaling performance during times of increased system usage. A new "adaptive query results cache" improves performance in returning analytical results for reports and dashboards when queries are repeated. And a "time travel" feature makes it easier to recover data sets and queries from any earlier point in time.
Also new is the adaptive data management toolset for easier management of distributed data and metadata. And new multi-data-center resiliency features provide expanded system availability and disaster recovery capabilities.
The overall goal of the new features is to reduce the amount of time Snowflake customers spend administering their cloud data warehouses, Bock said.