Database biz Redis Labs has secured $100m in a funding round that sets its valuation at an estimated $1bn.
The company is keen to see its open-source in-memory, key-value store tech move beyond use cases in infrastructure and caching, and be seen as a real-time analytics database for anything from online gaming to credit card transaction approvals.
Speaking to The Register, Redis Labs co-founder and CTO Yiftach Shoolman said: "What we are pushing more with Redis is to use it as more than a caching system. We want people to see it as a real-time database for practically every everything that you need. If latency is important, you should think twice before you use a database other than Redis."
The new funding round was led by Bain Capital Ventures and TCV, and included the existing investors Francisco Partners, Goldman Sachs Growth, Viola Ventures, and Dell Technologies Capital. It takes the total funding to date to $246m.
More than 7,500 customers including MasterCard, Dell, Fiserv, Home Depot, Microsoft, Costco, Gap, and Groupon have bought Redis tech, it said.
Redis Enterprise, the paid-for commercial platform built on the database, will soon perch on the top tier of Microsoft's Azure Cache for Redis, with a public preview expected in autumn. Redis Enterprise Cloud is available as a "native service" on Google Cloud, and has grown 300 per cent since its launch in October last year, though that figure is meaningless without a hard revenue number.
Shoolman told us that Redis also supports a secondary graph database model, which game developers were finding useful in building and managing gamer communities.
"One of the top gaming companies is using Redis for its community – here graph is a great use case," he added.
Earlier this year, the company launched RedisAI, which it claimed had the ability to deploy machine-learning models and run inferencing and performance monitoring within the database, the idea being to bring analytics closer to the data and improve performance.
Redis is not alone in gunning for real-time analytics, though: SAP launched its in-memory HANA database 10 years ago, the latest version of Exasol's in-memory database has touted support for machine learning and semi-structured data, and Oracle has an in-memory column store database. Meanwhile, products such as MariaDB are being adapted to accommodate real-time analytics.
But Shoolman said these were relational databases and thus not suited to the semi-structured and graph-like workloads Redis is designed for. "We are focusing on the non-relational use case and the new data models."
Evidence from stats-gatherers such as DB-Engines has suggested that the trend for relational databases is practically flat, while growth hs been coming from data models, key values, JSON, and graph.
"If you're looking for those types of data models and speed is important, Redis is the only solution," claimed Shoolman. "If speed is not important, there are 300 other solutions."
Perhaps tougher competition may come from RockSet, which bases its technology on RocksDB, a document store database with RDBMS built as a secondary model. Although not in-memory, its approach to indexing makes it practical for real-time analytical use cases, the company has claimed. ®