Nanigans, a leading provider of advertising automation software, uses Snowflake’s cloud-built data warehouse to optimize more than $700M in annual, multi-channel ad spend for its commerce advertising customers. It also deploys Snowflake Data Sharing, so Nanigans’ customers can share live, governed and secure access to their non-PII data in order to optimize their advertising spend. “Snowflake already delivers the power, flexibility and unlimited concurrency we need to load, integrate, analyze and seamlessly share data,” Nanigans’ Product Manager, Nat Taylor said. “By adding automatic clustering and materialized views, we’ll get the answers we and our customers need even faster. And we can focus on data analysis, instead of performing manual tuning operations associated with on-premises and legacy cloud data warehouse solutions. ”
PDX, Inc., a leader in pharmacy management software and services, receives and processes patient and operational data from more than 9,000 pharmacies each day. “We’re constantly adding, deleting and updating data, and need to quickly analyze these new data sets,” PDX Inc.’s EVP of Software Development, John Foss said. “Snowflake’s automatic clustering feature enables us to store and update our data in the most efficient way possible. We have seen significant performance improvements when querying large clustered tables that are now maintained by the automatic clustering service.”
Snowflake’s automatic clustering provides the following benefits:
- Automated and optimized self-organization of data storage, removing the burden of manually re-clustering data
- Merging and dropping data, and closing gaps between data, which Snowflake manages automatically and in the background
- Non-blocking execution of ETL pipelines that contain DML statements
- Incremental clustering to tables as new data sets arrive
- Exponentially faster queries with lower maintenance and lower cost
- Significant performance improvement for queries that repeatedly use the same subquery results
- Automatic maintenance of materialized views when new data arrives or existing data in the base table is modified
- Fast DML operations on base tables in the presence of materialized views
- Always up-to-date data when accessed via materialized views independent of modifications in the base table
“Automatic clustering is the obvious next step for Snowflake, and similar to how we’ve automated security, maintenance and instant elasticity into our data warehouse-as-a-service,” Snowflake’s Vice President of Product, Christian Kleinerman said. “Materialized views in Snowflake take a fresh approach to an age old problem. We’ve leveraged the decades of experience we have with large-scale database processing, combined with the benefits of the cloud, to provide materialized views that are transactionally consistent without slowing down modifications to the base tables.”
Read this blog to find out how Snowflake uses Automatic Clustering and Materialized Views to improve its internal analytics and the associated benefits.
Resources
- Snowflake Computing: snowflake.com
- Snowflake Twitter: twitter.com/SnowflakeDB
- LinkedIn: linkedin.com/company/snowflake-computing
- YouTube: https://www.youtube.com/user/snowflakecomputing
- Snowflake is hiring: http://www.snowflake.net/about/careers/
About Snowflake Computing
Snowflake is the only data warehouse built for the cloud, enabling the data-driven enterprise with instant elasticity, secure data sharing and per-second pricing, across multiple clouds. Snowflake combines the power of data warehousing, the flexibility of big data platforms and the elasticity of the cloud at a fraction of the cost of traditional solutions. Snowflake: Your data, no limits. Find out more at snowflake.com.
Media Contact
Eszter Szikora
Snowflake Computing
press@snowflake.com
