MongoDB on Thursday introduced new language support, easier installation of Atlas’ Kubernetes Operator, and a new Kotlin driver for its NoSQL Atlas database-as-a-service — all designed to streamline developer tasks, including work related to infrastructure management.
Typically, MongoDB developers have managed infrastructure-as-code (IaC) on AWS via the public cloud provider’s CloudFormation Public Registry, Partner Solution Deployments, and its Cloud Development Kit (CDK).
The company has also added support for Kotlin for developers building server-side applications. Previously, developers could use the MongoDB Realm Kotlin software development kit (SDK) for client-side development, but server-side developers relied on a community-created driver without official MongoDB support, or had to write extensive custom code, the company said.
“As a result, developers faced longer software development cycles to build server-side Kotlin applications on MongoDB and risked application reliability without a fully supported MongoDB Kotlin driver,” it added.
Easier way to install Atlas Kubernetes Operator
MongoDB is also providing an easier way to install the Atlas Kubernetes Operator — a tool that developers use to manage projects and database clusters.
“Using the MongoDB Atlas command line interface (CLI), developers can now install the MongoDB Atlas Kubernetes Operator and generate security credentials quickly in order to reduce operational overhead,” the company said, adding that developers will now have the option to import existing MongoDB Atlas projects and deployments with a single command.
The update, according to the company, is expected to provide greater agility for developers while working with containers.
While the company did not immediately provide information on the availability of the new features, it said that it was making the open source PyMongoArrow library generally available.
The library, according to the company, can be used to convert data stored on MongoDB using popular frameworks such as Apache Arrow Tables, Pandas, DataFrames and Numpy Arrays.
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