Snowflake is delighted to share the findings of a new Forrester Consulting Total Economic Impact™ (TEI) study that examines the potential return on investment for organizations that procure Snowflake through Amazon Web Services (AWS) Marketplace and then use Snowflake as a core part of your application’s architecture. We commissioned the study in partnership with AWS.
This is the 3rd blog in our series on Snowflake Resource Optimization. In parts 1 and 2 of this blog series, we showed you how Snowflake’s unique architecture allows for a virtually unlimited number of compute resources to be accessed near-instantaneously. We also provided best practices for administering these compute resources to optimize performance and reduce credit consumption.
Public preview of the data lake export feature is now available. Snowflake announced a private preview of data lake export at the Snowflake virtual summit in June 2020. Data lake export is one of the key features of the data lake workload in the Snowflake Data Cloud. The feature makes Snowflake data accessible to the external data lake, and it enables customers to take advantage of Snowflake’s reliable and performant processing capabilities.
Data engineers love to use SQL to solve all kinds of data problems. For this and more, Snowflake is a perfect partner. Snowflake’s support for standard SQL and several SQL variations, combined with JavaScript stored procedures, has helped me solve complex data challenges. But sometimes you might have the need for custom code.
Snowflake connected with Margaret Sherman of Sonos at Data Cloud Summit 2020 to hear how the company is using the Data Cloud to understand customer preferences and enhance listening experiences. In a world where people are surrounded by a lot of noise, purity of sound in music and other content we seek out in the comfort of our homes can offer a welcome respite. There are lessons to be learned from a company reinventing home audio for today and tomorrow—and using the Data Cloud to do it.
Last year, Gartner declared that on-premises databases are now legacy technology. Are we ready to say the same for analytics and BI?
The 2020 Marketing LUMAscape showcases more than 8,000 tools marketers can use to generate leads, drive brand awareness, and measure all their marketing efforts. But with all those tools, comes a lot of disparate and siloed data. How do you bring them all together in a consistent, reliable, and fast way to understand your ROI, determine attribution, and see which of your marketing efforts are working?
In June, Snowflake announced the public preview of the external functions feature with support for calling external APIs via AWS API Gateway. With external functions, you can easily extend your data pipelines by calling out to external services, third-party libraries, or even your own custom logic, enabling exciting new use cases. For example, you can use external functions for external tokenization, geocoding, scoring data using pre-trained machine learning models, and much more.
As we covered in part 1 of this blog series, Snowflake’s platform is architecturally different from almost every traditional database system and cloud data warehouse. Snowflake has completely separate compute and storage, and both tiers of the platform are near instantly elastic. The need to do advanced resource planning, agonize over workload schedules, and prevent new workloads on the system due to the fear of disk and CPU limitations just go away with Snowflake.
The push to embrace cloud-based technologies has undoubtedly transformed IT infrastructures at every level of government. Federal, state, and local agencies have made significant strides in modernizing how data is collected, stored, and analyzed, all in service of their mission and in fulfillment of strategic IT mandates.
