Snowflake’s listing on the Stock Exchange in September 2020 allowed the company to accelerate its expansion and demonstrate its ambition: transforming the BI market in the medium term and becoming the undisputed leader thereof.
So here’s an overview of Snowflake’s data platform, in which Solution BI is a partner. With its strengths in terms of agility and costs, and a few points of attention on BI market developments.
Snowflake: a success story culminating in an IPO
Snowflake is a Franco-American company founded in 2012 that provides a Cloud data management and analysis platform. The key to its success was its ability to anticipate the growing data storage needs of businesses. With an agile, pay-as-you-go, “turnkey” Cloud solution.
Snowflake had already raised $1.4 billion in expansion capital since its creation, tripling its R&D investments in 2020 (source Nasdaq.com). This is why Snowflake’s listing on the New York Stock Exchange (NYSE) on 16 September 2020 made the headlines. Its market capitalization almost reached $70 billion after the first trading day, i.e. twice its initial valuation. As we write this article, the interest has not waned.
Snowflake’s turnover amounted to $190.5 million in 2021, with more than 2,000 employees in 12 countries. The vendor has more than 4,500 clients (Q1 2021), whose loyalty they build via their subscription-based offer. The top 100 clients each generate recurring revenue of more than $1 million over 12 months (Source: Snowflake financial results). As the pandemic accelerated businesses’ Cloud migration, Snowflake is aiming for $10 billion in product revenue by 2029. Their offer is already deployed on the world’s leading Cloud infrastructures, at Amazon, Microsoft, Google, etc., who are also their competitors with their own Cloud data warehouse offers. Snowflake has also forged alliances with more than 1,300 partners. For example with Salesforce, in the field of CRM, who connected their “Tableau” data analysis tool to Snowflake’s data Cloud.
Snowflake’s offer: “Cloud-native”
Snowflake provides a Cloud service allowing businesses to store and analyze their own data. This “Cloud-native” offer features an interface which makes the everyday life of their clients easier while widening access to data. It includes all necessary components in an Saas subscription: infrastructure, storage, computing resources, software updates. Snowflake is also extending its offer to provide massive data sets in the Cloud, where businesses may pick external data to enhance their analyses. Snowflake's “data marketplace” already features more than 500 monetized data sets, with a try-before-you-buy option.
Historically, two approaches to data storage in the Cloud coexisted on the market. “Data lakes” (dedicated to the storage of large volumes of heterogeneous data, structured or otherwise) were distinguished from “data warehouses” (which does not store “raw” data but qualified, cleaned or processed data) (to find out more, read our dedicated article). Snowflake provides a solution that supports both approaches, and simply prefers to speak of “data cloud”.
It is an effective business approach for democratizing data collection and analysis
Two points of attention in light of market developments
Snowflake therefore has serious advantages but the market is very competitive. There are currently more than 60 major players who claim to provide data lake or data warehouse solutions, Cloud native or otherwise. It features many new entrants likely to be eventually taken over by bigger players, … and leading Cloud providers (Amazon, Google, Azure), long-standing leaders in the storage of high volumes of data (IBM, Teradata, Oracle, …Microsoft Sql) or businesses that build on a NoSql revival (Mongo, Cassandra, Couch, Apache Hadoop/Hive, etc.)
This plethora of offers, which are difficult to compare both in terms of technique, price and sustainability, could give the impression that all players are capable of doing everything. There is however no “do-it-all” solution, and businesses must be prepared to select a number of players to develop a data storage and analysis solution that truly meets their needs… and resources. This entails a risk that the smaller players may disappear or be taken over by bigger players.
Consequently, here are two major points of attention
1/ Give thought to the integration of solutions and Clouds. It is important to choose open suppliers engaged in technological partnerships so that their solutions are compatible, by anticipating market trends and changing business requirements. A data Cloud must be able to connect to “on-premises” environments and other Clouds as part of a multi-Cloud strategy. Without creating data silos, with an appropriate level of performance and security for data exchange.
2/ Retain control over data governance. Irrespective of the solutions selected, the company must manage data access rights and keep auditable records, in particular for personal data protection purposes with the GDPR. Snowflake’s data Cloud allows for the implementation of this data governance. The tool is only a means of implementing this governance; it must be accompanied by the development of a data culture within the company and the transformation of its data organization (roles).
Solution BI’s expertise on the Snowflake platform
Solution BI has been Snowflake’s certified partner since 2019, and gained Premier partner status in 2021. With several significant references each year, we assist our clients in their data warehouse Cloud migration projects. Our Advanced Analytics consultants are certified by the vendor and undergo regular training to maintain their level of excellence.
We remain however “platform agnostic” by constantly assessing the technological solutions available on the market, as a BI approach must be suited to the existing infrastructure of our clients, to their current and anticipated needs as well as their resource constraints. Find out more: https://www.solution-bi.com/expert-data-snowflake/
Suggested reading :
Business Intelligence : 5 reasons to securely switch to full cloud
Questions to ask before creating your data lake
