Snowflake marketshare

Muji has done a great job in Snowflake deep drive, thanks.

However, there are a lot of competitors… Although SNOW has been gaining new customers, it will be good to know if anyone has the market share data on the cloud data sector?

SNOW, MDB could be market leaders. Like to learn more about market share ownership.


Did a bit of googling this morning …

MarketsandMarkets forecasts the global cloud database and DBaaS market size to grow from USD 12.0 billion in 2020 to USD 24.8 billion by 2025.….

However we need to consider how the database market is further segmented:

  • 40%/60% for no-SQL vs. SQL, though Snowflake indicates it can handle unstructured data, so the entire market may be up for grabs. Let’s say Snowflake can target 80% of that.……

  • OLTP vs OLAP. Snowflake appears to be designed for OLAP, though I can’t find anything on market share for these segments. My guess would be that since OLTP workloads are the most mission-critical, that they would be the last to move to the cloud, especially for large enterprises. Also, OLAP data is massive compared to OLTP. So let’s say OLAP is 80% of the market.

So the market share becomes USD 7.68 billion in 2020 to USD 15.8 billion by 2025.

Fyi, for those who don’t know the difference between OLTP and OLAP:…

OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).
Online transactional processing (OLTP) is designed to efficiently process high volumes of transactions, instantly recording business events (such as a sales invoice payment) and reflecting changes as they occur.

OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).