Amazon Redshift Data Sharing

“Many FactSet clients are undertaking cloud transformation and technology modernization programs in an effort to reduce costs, consolidate their infrastructures and vendors, and eliminate duplicated data. To meet the evolving needs of our clients, FactSet provides flexible solutions that can be seamlessly integrated into a number of preferred workflow environments including AWS Redshift, making the adoption and implementation of our content and solutions turnkey. By leveraging Redshift’s data sharing capabilities, FactSet Standard DataFeeds are instantly available in our clients’ Redshift cluster. This allows them to outsource all ETL processes to FactSet, resulting in streamlined data, reduced time to market, more efficient data integrity, and a simplified process for data discovery, linking, and testing.”

Namita Jain, Product Owner – Cloud & Managed Services

“Prior to data sharing, our process for exchanging data with our clients using Amazon Redshift was not as efficient as it could be. We would typically spin up two additional clusters twice per week, restore, unload, copy, drop schemas, and grant privileges. With data sharing, we can share data with our clients with little to no down time. With less than 10 SQL statements, we securely achieve what used to be a much longer process. This feature gives us more flexibility, saves time and cost, and increases client satisfaction.”

Samantha Corkery, Principal Database Administrator - Epsilon

“At Warner Bros. Games, we build and maintain complex data mobility infrastructures to manage data movements across single game clusters and consolidated business function clusters. However, developing and maintaining this system monopolizes valuable team resources and introduces delays that impede our ability to act on the data with agility and speed. Using the Redshift data sharing feature, we can remove the entire subsystem we built for data copying, movement, and loading between Redshift clusters. This will empower all of our business teams to make decisions on the right datasets more quickly and efficiently. Additionally, Redshift data sharing will also allow us to re-architect compute provisioning to more closely align with the resources needed to execute those functions’ SQL workloads—ultimately enabling simpler infrastructure operations.”

Kurt Larson, Technical Director - Warner Bros. Analytics

“The data sharing feature seamlessly allows multiple Redshift clusters to query data located in our RA3 clusters and their managed storage. This eliminates our concerns with delays in making data available for our teams, and reduces the amount of data duplication and associated backfill headache. We now can concentrate even more of our time making use of our data in Redshift and enable better collaboration instead of data orchestration.”

Steven Moy, Engineer - Yelp

“At Fannie Mae, we adopted a de-centralized approach to data warehouse management with tens of Amazon Redshift clusters across many applications. While each team manages their own dataset, we often have use cases where an application needs to query the datasets from other applications and join with the data available locally. We currently unload and move data from one cluster to another cluster, and this introduces delays in providing timely access to data to our teams. We have had issues with unload operations spiking resource consumption on producer clusters, and data sharing allows us to skip this intermediate unload to Amazon S3, saving time and lowering consumption. Many applications are performing unloads currently in order to share datasets, and we plan to convert all such processes to leveraging the new data sharing feature. With data sharing, we can enable seamless sharing of data across application teams and give them common views of data without having to do ETL. We are also able to avoid the data copies between pre-prod, research, and production environments for each application. Data sharing made us more agile and gave us the flexibility to scale analytics in highly distributed environments like Fannie Mae.”

Amy Tseng, Enterprise Databases Manager - Fannie Mae

“Shared storage allowed us to focus on what matters: making data available to end-users. Data is no longer stuck in a myriad of storage mediums or formats, or accessible only through select APIs, but rather in a single flavor of SQL.”

Marco Couperus, Engineering Manager - home24