I have previously written about the impact of intelligent operational applications on the requirements for data platforms. Intelligent applications are used to run the business but also deliver personalization, recommendations and other features generated by machine learning and artificial intelligence. As such, they require a combination of operational and analytic processing functionality. The emergence of these intelligent applications does not eradicate the need for separate analysis of data in an analytic data platform (such as a data warehouse or data lake), but it does increase the requirement for real-time analytics on operational data. This has prompted many operational data platform providers, such as Couchbase, to evolve products to better support analytic processing.
Couchbase was founded in 2011 amid the boom of NoSQL database open-source projects and software providers. Couchbase resulted from the merger of two companies (CouchOne and Membase) founded to build businesses based on the Apache CouchDB document database and the Memcached distributed in-memory caching system, respectively. The combination provided Couchbase with its distributed document database architecture and an ethos of combining multiple concepts to deliver a flexible and adaptable data platform that can be used to support a variety of data workloads, both on-premises and in the cloud.
The company’s success was initially driven predominantly by the development of new applications, with application developers taking advantage of Couchbase Server’s flexible
To further accelerate this adoption, Couchbase has also turned its attention to replacing existing relational database workloads by adding concepts and features that make its database more familiar to developers and database administrators who are more used to working with relational databases. A prime example is the company’s development of the SQL++ specification–essentially an extension to SQL that enables users to apply their SQL skills to query and manage data stored in the JSON document format. SQL++ also enables support for distributed multi-document atomic, consistent, isolated, and durable database transactions. Couchbase had attracted approximately 715 customers as of the end of its most recent fiscal quarter (ended October 31, 2023) and generated revenue of $154.8 million in its most recent fiscal year, ended January 31, 2023, up 25% on the previous year.
Couchbase’s product portfolio includes the Couchbase Capella managed database-as-a-service offering, Couchbase Server for self-managed deployment on-premises or in the cloud, and Couchbase Mobile, which provides an embedded database for edge and local applications as well as a gateway for synchronization to Couchbase Server or Couchbase Capella. New functionality recently added to the Couchbase Server core database includes support for time-series data and change data capture integration via Apache Kafka.
Like many other data platform providers, Couchbase is increasingly delivering its most innovative new capabilities via the Capella cloud managed service. A prime example is the
While Capella iQ delivers AI capabilities to accelerate development, Couchbase is also taking steps to encourage the development of AI-driven intelligent applications using its database-as-a-service offering. In August, the company announced the Couchbase AI Accelerate Partner Program to facilitate the development of AI-powered applications by customers using Couchbase Capella. That was followed in November by the private preview launch of Capella columnar services, which enables real-time analysis of data generated by operational applications by providing a massively parallel processing query engine and column-oriented storage for analytic workloads, along with zero-ETL (extract, transform, and load) ingestion of JSON from operational Couchbase nodes.
Traditionally, operational data platforms are designed to store, manage and process data to support worker-, customer- and partner-facing operational applications. Data is then extracted into a separate analytic data platform designed to store, manage, process and analyze data. The emergence of intelligent applications does not remove the need for analysis of data in a data warehouse or data lake separate from operational data platforms, but it does increase the requirement for operational data platforms to support analytic processes, including ML inferencing. Almost one-half (49%) of participants in ISG’s 2023 Application Development and Maintenance Study are planning to embed AI/ML models into current applications.
Initially available only on Capella running on Amazon Web Services, Capella columnar services also provides the ability to ingest data from external relational and non-relational data stores via Apache Kafka as well as data stored in Amazon Simple Storage Service (Amazon S3). Capella columnar services also features cost-based optimization to improve SQL++ query performance and provides native support for Salesforce’s Tableau and Microsoft’s Power BI for visualization and analysis as well as integration with Capella iQ.
Capella columnar services is currently only in private preview but expands on existing Couchbase capabilities, including SQL++, to enhance support for analyzing JSON data. While further support for generative AI and analytics can be anticipated, Capella columnar services combined with Capella iQ strengthens Couchbase’s value proposition for supporting intelligent applications enhanced by AI. I recommend that enterprises considering potential database providers for intelligent operational applications and real-time analytics include Couchbase in evaluations.
Regards,
Matt Aslett