With enterprises looking to scale generative AI adoption, databases are going to play an important role. However, they need to evolve to handle increasingly complex data structures and queries efficiently, supporting the diverse needs of AI algorithms and applications.
Jihad Dannawi, regional vice president for APAC at DataStax, believes while 2024 was the year of production in terms of generative AI, 2025 will be the year of transformation.
He said that currently, around 50% of all companies in the world are leveraging generative AI, of which 10% are in production and the other 40% will move into production this year or next.
“Databases will be central in solving generative AI agents or other generative AI use cases. You will need a lot of data because you are trying to solve multiple use cases, and you need data in real-time,” Dannawi told AIM.
Based in Santa Clara, California, DataStax is a real-time database company that specialises in providing solutions for AI-powered applications and enterprise-scale databases.
Its core products include Astra DB, which is a cloud database-as-a-service based on Apache Cassandra, offering vector search capabilities for generative AI applications.
However, their offerings are not limited to the cloud. They have on-prem solutions, too, and according to Dannawi, a large bank in India is already leveraging DataStax’s on-prem database solutions.
Earlier this year, the company introduced Data API, which offers a complete stack for production generative AI and retrieval-augmented generation (RAG) applications, ensuring high relevancy and low latency in data retrieval.
Cassandra Can Scale While Others Can’t
Today, many companies provide cloud database services, each bringing in their unique proposition. MongoDB, a dominant player in this space, has over 3000 customers in India. Others like CockroachDB, PostgreSQL, TiDB too have presence in India.
However, Dannawi believes Indian startups and enterprises will soon realise that they can’t scale with monolithic databases. The general trend in the industry is also towards distributed systems for large-scale, global applications needing scalability and high availability.
“MongoDB will run out of gas; PostgresDB will run out of gas when it has to handle real-time processing or high volumes of data. To effectively manage large-scale operations, you require a robust infrastructure that can handle substantial demands without running out of capacity.
“So if you want a system that has no problem in scaling, no problem of performance and that can give you a great total cost of ownership (TCO), there’s no other system than Cassandra and we are the number one provider of Cassandra,” he added.
Apache Cassandra, initially developed by Facebook (now Meta) for its own internal needs to scale, is an open-source distributed NoSQL database designed to handle large amounts of structured data across many commodity servers, providing high availability with no single point of failure.
Dannawi further claims that no other database can claim 99.999% availability. “Others can claim 99.8% but nobody can claim 99.999%. We take the open-source Cassandra and make it enterprise-ready. We can put it on-premise or on the cloud,” he added.
MongoDB of the GenAI Era
India is the hottest pie and everyone wants a piece. Last year, Cockroach Labs’ first-ever distributed SQL mixer was held in Bengaluru.
MongoDB, which entered the country a bit earlier, managed to grab a significant portion of the pie. PingCap, which built TiDB, is also expanding in India and looking to acquire new customers. DataStax too has similar plans. It wants to be the ‘Mongo of the generative AI era,’ according to Dannawi.
“MongoDB is not the best database, but it has become the best database for mobile developers. They had a lot of free versions and the mobile wave happened and they rode that wave. It’s a good engine, but it’s not the best. We want to do what MongoDB has done on mobile, but on generative AI applications,” he said.
DataStax is Expanding in India
Currently, DataStax serves a diverse customer base in India that includes banks, public sector organisations, and startups. Among its notable Indian customers are Physics Wallah, Chingari and VerSe, while globally, companies such as Netflix, FedEx, and PlayStation are also among its clients.
The company is also hiring and expanding its team to land more customers and better serve existing customers.
Dannawi also adds that the APAC market for DataStax is as big as the US market. In fact, APAC is leading in terms of innovation, and India is leading in terms of the number of generative AI projects.
“We have more generative AI projects in India than the rest of the APAC regions combined. Indians have the mass; they are entrepreneurs at the core, and they love to use technology to solve problems and for competitive advantage as well,” concluded Dannawi.
