DataStax this week at the GitHub Universe 2024 conference revealed it has extended its support for the GitHub Copilot generative artificial intelligence (AI) tool to now include the ability to write data to its Astra databases-as-service (DBaaS) platform based on the open-source Cassandra database.
Previously, DatsStax had provided developers using GitHub Copilot with read-only access to the database. However, it is now extending that capability to make it possible for application developers to interact with vector, tabular and streaming data right from their integrated development environment (IDE).
In addition, application developers can also now directly invoke DataStax Langflow, a visual tool for invoking open-source Langchain software to build retrieval-augmented generation (RAG) workflows for generative AI applications, that DataStax gained earlier this year with the acquisition of Logspace.
Greg Stachnick, vice president of product management for DataStax, said the integrations with GitHub Copilot make it simpler for application developers to, for example, use natural language to configure a database or generate Langchain application programming interface (API) calls from within their IDE. In effect, that integration makes it simpler for organizations to integrate DevOps and DataOps workflows, regardless of who on the IT team is managing a database.
Application developers can now use natural language to, for example, troubleshoot database queries on their own versus always having to rely on the expertise of a database administrator (DBA), noted Stachnick.
As organizations look to operationalize AI, many are discovering they need to integrate DevOps and DataOps workflows more closely than ever. The challenge is that DataOps workflows have largely been managed by separate teams that often have a separate culture. DataStax and GitHub are now moving to leverage generative AI tools to reduce the level of friction that many developers would have previously encountered, said Stachnick.
That’s especially critical as application developers look to invoke vector data within the context of RAG workflow that needs to be deeply embedded into an application, he noted. The integrations with GitHub Copilot will make it simpler to automatically generate the scaffolding for building those applications, noted Stachnick.
Less clear, is the degree to which that ability to more easily meld DevOps and DataOps workflows might drive further convergence across the way many IT teams are currently organized. At the very least, however, pressure to eliminate the friction that exists across many of the silos that exist within IT teams is only going to increase in the age of AI. One of the reasons that many application developers tend to opt for a document database, rather than a Cassandra key/value store database, is that the latter has been too challenging for them to configure in the absence of a tool such as GitHub Copilot.
The pace at which organizations are looking to build AI applications will naturally vary widely, however, going forward the bulk of new applications being built will embed one or more AI models. The goal now is to make it simpler for application developers and the DevOps teams that support them to build those applications at scale as rapidly as possible.
