![]() ![]() Since Snowflake operates on a consumption-led revenue model, customers are likely to increase their usage over time if the data cloud workloads, data sharing, and analytics continue to provide value for them. Rising Net Revenue Retention: During the Q1 FY23 earnings call, Snowflake indicated that their net revenue retention rate is expected to remain well above 130% for an extended period. Snowflake Quarterly Result, Author's calculation The chart below illustrates the increasing number of customers with sales over $1 million. In other words, Snowflake has significant growth potential among these large accounts. Among their current $1 million customers, only 45% are from the Global 2000, 55% are enterprise accounts, and 1% are corporate accounts. Their revenue depends on the amount of consumption that occurs in a certain period. ![]() It is important to note that Snowflake does not operate on a SaaS pricing model, but instead follows a consumption-led model. Large Customer Growth Potential: Snowflake is focusing on customers who have the potential to generate more than $1 million in sales over a trailing 12-month period. These features are critical for data analytics and machine learning. On the contrary, Snowflake's platform powers data, whether structured or unstructured, disrupts the siloed data, and supports all these workloads within their data cloud. In other words, the data cloud and workloads are not fully integrated, and the data is siloed. In some cases, customers use spreadsheets to download and share data across platforms/divisions. Why? Oracle and Teradata provide data warehouses, but the data sets are siloed and structured. I believe Oracle and Teradata are losing the game. ![]() To deploy machine learning and data analytics, you need to migrate all the data to the cloud, design the workloads and applications for the cloud, and run these applications on the data cloud. Snowflake Investor Day 2022 How much Growth can Snowflake Capture?ĭata Analytics and Machine Learning are still in the early stages of adoption: I believe that data analytics and machine learning are enduring trends regardless of the macro environment, and they are still at the early stage of industry adoption. Undoubtedly, this is a huge market for Snowflake. According to Snowflake, the total addressable market for the entire cloud data platform is estimated to be $248 billion in 2026. The data cloud encompasses all workload executions, data sharing, and the data marketplace. The ideal approach is to run all these workloads in the data cloud. With structured and unstructured data, enterprises need to share data, perform data analytics, and utilize machine learning for their AI capabilities. Enterprises require cloud data infrastructure for storing and aggregating data from various sources, including CRM, cybersecurity, HRM, etc. In conjunction with workload migration, the cloud's share of the database market is expanding and expected to reach 71% in 2025.ĭata infrastructure and the data cloud are two key elements. One clear reason for this growth is the migration of workloads to the cloud. How Big is the market?Īccording to Gartner, database management spend will outpace the broader software market, growing from 13% of software spend in 2021 to 17% in 2026. From my perspective, Snowflake will become the most relevant cloud platform for machine learning and data analytics. In addition, Snowflake Marketplace enables enterprise customers to combine internal data with external third-party data products for improved data analytics and decision making. I believe Snowflake is disrupting the siloed and structured data, combined with database management, data analysis, machine learning, cyber security, and data sharing. They are taking market share from legacy incumbents like Teradata ( TDC ), Oracle ( ORCL ), IBM ( IBM ), and EMC. ![]() Over the past few years, Snowflake has experienced whopping growth, with sales growing from $100 million in FY19 to more than $2 billion in FY23. Snowflake ( NYSE: SNOW) originally built their cloud-based warehouse on AWS in 2014, then started to build their platform and applications to operate workloads beyond data warehousing. ![]()
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