The Snowflake Elastic Data Warehouse

By B. Dageville, Thierry Cruanes, Marcin Zukowski, V. Antonov, Artin Avanes, J. Bock, Jonathan Claybaugh, D. Engovatov, Martin Hentschel, Jiansheng Huang and 8 more. SIGMOD Conference, 2016

401 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/00a2aeb87287d59eaf118e96f0cdbb622a7fec42

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. A. Ghodsi
2. Matei A. Zaharia
3. Christos Kozyrakis
4. Reynold Xin
5. Ion Stoica
6. Jimmy J. Lin
7. Christina Delimitrou
8. Sheng-Chieh Lin

In Management Science and Operations Research

1. Christos Kozyrakis
2. Matei A. Zaharia
3. Hai Jin

Citations in the corpus

• Milvus: A Purpose-Built Vector Data Management System (2021, methodology): “The storage layer is based onAmazon S3 (also used in Snowflake [16]) because S3 is highly available.”
• Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics (2021, background): “Cloud-native warehouses such as Snowflake and BigQuery [20, 29] have seen good commercial success, but they are still not the primary data store in most large organizations: the majority of data continues to be in data lakes, which can easily store the time-series, text, image, audio and semi-struct”
• Pocket: Elastic Ephemeral Storage for Serverless Analytics (2018, background): “Examples include Amazon Redshift [14], Google BigQuery [37], Azure SQL Data Warehouse [58], and Snowflake [26].”