Snowflake Computing Automates Administration and Business Critical Resiliency for Cloud Data Warehousing

Print Friendly, PDF & Email

Snowflake_logoSnowflake Computing, the cloud data warehousing company, today announced new, automation capabilities for the Snowflake Elastic Data Warehouse that dramatically simplify data warehousing administration and data protection for diverse data and SQL analytics in the cloud. Snowflake’s automation capabilities eliminate the manual, administrative overhead and complexity associated with managing and protecting data warehouses for multi-structured big data.

Pains with existing approaches

Traditional data warehousing generates slow, inflexible and costly data analytic cycles – from data import to business insights – which creates user pain and frustration. This has been true especially for data warehousing, and even for big data solutions such as Hadoop, when there is a need to apply fast, low-latency SQL analytics on diverse, multi-structured data.

At the same time, the database analytics industry has entered a new era of needing to fulfill the needs of diverse-data driven applications in combination with the requirements of normal BI and reporting tools. Manual administrative overhead, complex implementation and concurrent data processing delays make integration of analytics with diverse-data driven apps difficult.

Snowflake: Automating data warehousing

Snowflake is delivering a set of new innovations that address the complexity and admin overhead pain-points that typically accompany the warehousing and analytics of large, diverse data sets. These capabilities automatically:

  • Scale performance in the event of a high concurrency – without delay or operator intervention
  • Optimize performance for dashboarding and reporting – return results for repeated queries faster and without manually evaluating or re-issuing the queries
  • Implement data milestoning – to make it easy to recover data, at or before any specified point, and automatically rerun queries in the event of data loss or corruption
  • Distribute data and manage metadata – to easily facilitate performance scaling and query optimization without the typical manual data re-distribution and tuning efforts
  • Implement high availability & disaster recovery – for continuous data access and data recovery in the event of the unthinkable

Other approaches to diverse, big data warehousing and analytics are like completing an obstacle course,” said Bob Muglia, CEO of Snowflake Computing. “Snowflake is a walk in the park and will enable data and analytics infrastructure executives, architects, managers and developers to allocate more of their precious time toward higher value strategic projects that can impact their business.”

Key Snowflake automation features

Snowflake is focused on developing technology that significantly simplifies, automates, accelerates and protects the process of warehousing and querying the diverse, multi-structured data that every enterprise produces. New capabilities include:

  • Multi-cluster warehouse: Different from simply scaling nodes or storage capacity, this feature automatically creates a new data warehouse to scale concurrency. Made possible by Snowflake’s unique architecture that separates compute and storage layers, and with no disruption to running processes, automatic warehouse scaling ensures smooth performance to complete queries quickly during periods with high concurrency.
  • Adaptive query results cache: Assures highest performance when repeating queries from a collection of BI tools, reports and dashboards.
  • Time travel: Ensures automatic, continuous data protection and milestoning without interruption to query processing. Applicable for recovery of both data and temporal queries, and offers users the ability to define data retention periods and to recover data at or before any milestone point.
  • Adaptive data management: Produces automatic data distribution, metadata creation and more. Unlike traditional systems, in which data distribution needs to be manually and statically configured and metadata needs to be manually updated, Snowflake automatically and dynamically updates data distribution tasks and metadata information.
  • Multi-datacenter resiliency: Built-in fault tolerance and disaster recovery across data, compute and the full Snowflake service.

Snowflake customer Snagajob is an online, 24×7 service, connecting hourly workers to employers. “Snowflake automates manual tasks such as scaling out for high concurrency and scaling up for increased query complexity,” said Robert Fehrmann, principal architect at Snagajob. “With Snowflake, we reduce costs and re-focus our database and analytics talent on more strategic projects that move our business forward.”

Snowflake has an ecosystem of popular data visualization and BI tools and enables rapid development of diverse-data driven applications. This allows Snowflake customers like Snagajob, ResearchNow and CapSpecialty to easily employ structured and machine data together for SQL analytics. Snowflake customers can take advantage of cloud scale and elasticity without the complexity and inflexibility of other big data warehouse and SQL analytics database solutions.


Sign up for the free insideBIGDATA newsletter.

Speak Your Mind