Financial giant S&P turns to Snowflake for better cloud data distribution

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When Warren Breakstone wanted to make it easier for S&P Global Market Intelligence clients to consume the company’s mine of financial data, he turned to cloud data specialist Snowflake.

As Managing Director and Product Manager for Data Management Solutions at S&P Global Market Intelligence, Breakstone recognized that the choice of cloud data platform was a major concern for his organization, which is a division of the giant. S&P Global Financial. His team is continually looking for new ways to create innovative, data-driven products for its core clients, which include leading financial companies and businesses across a variety of industries.

The organization wanted to leverage the cloud and make it easier to use data held on the S&P Global Marketplace, which brings together company data and information from third-party sources. After an evaluation period, the organization started working with Snowflake last year. Here, Breakstone explains why he chose Snowflake and how its technology provides a platform for new innovations.

This interview has been edited for brevity and clarity.

VentureBeat: What was the purpose of the implementation?

Warren Brisepierre: What we focus on is data production – creating new data-driven products, connecting all of that and combining them so that customers can achieve incremental value. And then also make it available to customers the way they want to consume it. And that’s what we’ve really done with Snowflake, which is to make all of our data on the S&P global market available through the Snowflake distribution and pair it with the computational power of Snowflake, so customers can profit from queries. more important data and all the advantages of computing power, so that they can study, research, analyze and evaluate, not only our data, but our data in combination with their own data.

VentureBeat: What was the business challenge you were looking to solve?

Stone breaker: The big challenge has always been that different customers have different ways of bringing data into their environments. Some want it through our Xpressfeed solution, which is our mass delivery technology that automates data ingestion directly into their environments. Others want to access data through APIs. Then there is a third installment, which wants it through pre-packaged software products, such as our Capital IQ platform. The challenge is being able to support all the different customers and the different ways they want to consume data.

What Snowflake provides us is a modern addition to our distribution lineup and has additional benefits such as the ability to utilize computing power as the data grows larger and larger. Customers want to do new and interesting things by bringing together different sets of data, which is why the ability to access computing power is so important. This has opened up all kinds of new opportunities for us and our customers in the way we deliver new capabilities, new content, new products and added value.

VentureBeat: How did you deal with the issue of building versus buying?

Stone breaker: Rather, the challenge was to know who we were going to partner with. We have a lot of in-house delivery solutions, such as Xpressfeed, which we’ve enhanced with what’s called a loader, which is software that automates data ingestion for our customers. And it’s a great product and customers love it. But customers are also increasingly turning to the cloud. And this is where we had to make a decision: how to best approach this opportunity and with whom do we partner to achieve it? And that’s what brought us to Snowflake.

VentureBeat: Why did you choose the Snowflake cloud data platform?

Stone breaker: First and foremost, it was about being closely connected with our customers – and our customers were talking about Snowflake and the opportunities it offered them. As we did a fairly in-depth look at the landscape and the different partners, and knowing that we wanted to get into cloud-based distribution, the question was how to do it best. Snowflake was one of the alternatives we considered.

We then needed a solution that would support our customers based on their current situation. Customers use different solutions: some are on AWS, some on Google Cloud Platform, some on Azure. How do we support all of these different customers, based on the environments they’ve created? This was also another advantage in the Snowflake column, as it is a cloud independent solution; we can build it once and serve several.

VentureBeat: What are the other technological factors that led you to Snowflake?

Stone breaker: We ran various tests to see what the calculation looked like compared to other alternatives on the market and were very impressed. Part of that went back to the original architecture Snowflake was built on, where they separated their compute from their storage, and because you’ve separated those two, you can get a bit more compute performance.

Snowflake also has connections to other apps and tools in the space. Various visualization and analysis tools are already connected to Snowflake. Once we put our data into Snowflake, if a customer wants to use that data through a third-party visualization or analysis tool, more often than not that provider is already logged into Snowflake, which makes the process for us to get the data in this solution and in their much less complicated environment because there is a preexisting pipe.

VentureBeat: How did you implement the Snowflake cloud data platform?

Stone breaker: This involved a close partnership between our technology group and our product management organization, where we first prioritized – based on customer needs – what data we were going to add to the Snowflake environment and in what order. And then we were able to work with Snowflake to develop a rigorous and repeatable process, where we would be able to load the data into that environment. It was a very partnership-based approach. And we got there pretty quickly; much smoother than we expected.

The challenges were really one of prioritization. We have hundreds of different datasets, so where do we start? Are you starting with the largest and most complex data sets? Do you start with the simplest and easiest to load? We had a group of clients who partnered with us and helped us set those priorities. And it has been very helpful.

VentureBeat: What does the implementation mean for other investments in the data stack?

Stone breaker: We just introduced our Marketplace Workbench, which is a platform we built on Snowflake and Databricks, which are a partner of Snowflake. This new platform allows our customers to use our data in a collaborative development environment, using a programming language of their choice, be it Python, R or SQL, to get the most out of the data.

So what we’re happy about is that this is not a one-off, one-off opportunity for us. This is something we continue to build on, and we build on it in a way that is relevant to our clients. It’s not about us, it’s about how our customers are able to generate value and utility from these different connected solutions which are all built on our data.

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