Snowflake skeptics have reservations about data warehouse attempt to break into financial services • The Register
Cloud-based data warehouse darling Snowflake has launched its latest business in financial services, while Teradata, a mainstay of data warehousing for banks and insurers, is trying to broaden its appeal with machine learning implementations.
Although the world has been focused on the stratospheric rise of Snowflake – its value rose from $ 1.5 billion in 2018 to $ 120 billion shortly after its IPO last year – it might struggle to break into lucrative financial services markets, according to insiders and industry experts.
The so-called cloud-native data warehouse company launched what it calls the Financial Services Data Cloud this week, along with the claim that 57% of Fortune 500 companies in the industry are on its platform. It is described as an industry-friendly platform that brings together Snowflake technology with “partner-delivered solutions” and “industry-critical data sets”.
The idea is for financial services companies to use it to launch new customer-centric products, the process being made easier by the fact that all the data is already on the platform, according to the company.
Snowflake says companies will be able to use built-in security and governance capabilities to collaborate on data projects. Features include private connectivity for multiple public clouds, enhanced encryption with bring your own key (BYOK), built-in classification and anonymization of sensitive data, and integration with third-party token providers in accordance with SOX standards.
The platform provides the ability to securely share data across multiple public clouds with support for sharing from multi-tenant environments. A data catalog should arrive via Alation. Meanwhile, there are plenty of features being offered through partners, including Aladdin Data Cloud from investment management firm BlackRock – a Snowflake-based system designed to help investment managers make more use of data – SI Cognizant, the Dataiku and Deloitte data integration platform.
Matt Glickman, Snowflake veep, said the company’s platform is becoming the “destination of choice for traditional and alternative datasets” in financial services, while third-party technology equipment is also helping customers build new services with the system.
He also claimed that by working with service companies, financial services clients were “putting more and more of their key production workloads into a snowflake.”
This might come as a surprise to some suspicious observers. An insider with years of experience working with data warehousing systems in financial services said banks and insurers have struggled to see the reasons to move their trusted on-premises systems to the cloud because safety, performance and cost remained issues.
While Teradata has built a reputation in financial services, where it counts HSBC and Lloyds among its clients, Snowflake is new to supporting core workloads in this market.
The insider said that Snowflake can handle eight concurrent users per cluster, which means that as the system adds more users, the costs go up.
In Teradata, “Although the current system is known finite capacity in terms of throughput, compute and storage, it is a guaranteed known cost. It is the unpredictability of the costs that is a big shock to people. [using Snowflake] because not only can they not explain this month’s bill, but they have no idea what next month’s bill will be, “he said.
In addition, technical teams in the financial services industry were skeptical about the novelty of Snowflake, which is based on a relational database. “There is no more leeway to advance the state of the art,” the insider said.
While Snowflake helped with the integration and arguably the data sharing, that didn’t solve a real problem. “What is the technology catalyst in this announcement, what is the new IP that is involved? ” He asked.
An analyst from a global technology research firm, who asked not to be named, agreed that Snowflake is struggling to break into major corporate data warehouse systems in financial services.
While they can perform tactical analysis and data processing in Snowflake, banks and insurers rely on the functionality Teradata has built around its on-premises data warehouse appliances for 40 years.
“Teradata has integrated machine learning [for performance] and the integration of several types of data: things that Snowflake has not yet designed. If you have a large bank that depends on these features, migrating to Snowflake in the cloud is quite difficult, and from a cost-benefit perspective, it would be hard to argue now that Teradata has a cloud system as well. . I don’t think they will deal a fatal blow to Teradata anytime soon, ”he said.
Meanwhile, Teradata announced new features to deploy machine learning models for platforms like the Databricks data lake spinner directly into its own.
Scott Toborg, Director of Product Management at Teradata, said The register that, for example, a logistic regression model that a data scientist had built in Apache Spark could be exported to the Predictive Model Mark-Up Language (PMML) interchange format, with coefficients, biases, and any other parameters used for describe this model.
“Then we import this file into Teradata, extract the data, and then create the Java code needed to rerun this model,” he said. The industry analyst said the combination of Databricks (Spark) to create machine learning models and Teradata to deploy them to enterprise data could be sophisticated.
Despite the investor spotlight on Snowflake over the past two years, insiders say there is still a long way to go to convince major enterprise data warehouse users to migrate their core systems, although ‘It supports peripheral and tactical workloads.
Legacy vendors like Teradata have their own cloud history. For example, Unilever has migrated its on-premises Teradata system to Azure in the past 18 months, according to a customer testimonial.
All the while, Teradata continues to add functionality, which means the battle in enterprise data warehousing is far from over. ®