MongoDB today announced a series of capabilities from the keynote stage of MongoDB’s annual conference, MongoDB World at the Javits Center in New York City. With these announcements, MongoDB is empowering development teams to innovate faster by addressing a wider set of use cases, servicing more of the data lifecycle, optimizing for modern architectures, and implementing the most sophisticated levels of data encryption, all within a single integrated developer data platform.

The new capabilities make it easier for developers to build in-app analytics and power richer application experiences. Column store indexing, available later this year, will enable users to create and maintain a purpose-built index that dramatically speeds up many common analytical queries without requiring any changes to the document structure or having to move data to another system. Furthermore, analytics nodes can now be scaled separately, allowing teams to independently tune the performance of their operational and analytical queries without over- or under-provisioning.

MongoDB time series collections make it easier, faster, and lower cost to build applications that monitor physical systems, track assets, or deal with financial data. In the upcoming MongoDB 6.0 release, time series collections will support secondary indexes on measurements, and feature read performance improvements and optimizations for sorting time-based data more quickly.

According to the company, Atlas Search is the fastest and easiest way to build relevance-based search capabilities into applications. Now, with Search Facets, developers are able to rapidly build search experiences that allow end users to more seamlessly browse, narrow down or refine their results by different dimensions.

MongoDB announced new products and capabilities that enable development teams to better analyze, transform, and move their data in Atlas while reducing reliance on batch processes and ETL jobs that can create delays, limit productivity, and increase costs. Atlas Data Lake will feature fully managed storage capabilities that provide the economics of cloud object storage while optimizing for high-performing analytical queries. Atlas Data Lake reformats, creates partition indexes, and partitions data as it is ingested from Atlas databases, creating a highly performant companion data lake.

Atlas’s Data Federation capabilities allow teams to create virtual databases so that they can work with data that resides in a range of different sources. Atlas SQL Interface provides a great experience for data analysts, who work mainly in SQL tools, to interact with Atlas data in a read-only interface.

In addition to supporting a wide range of workloads, organizations need to have the flexibility to deploy the right application architectures to serve their needs. Atlas Serverless is now generally available and allows users to support a wide range of application requirements with little to no initial configuration and ongoing capacity management. Users benefit from the ability to scale to zero and deploy in all three major cloud providers, and tiered pricing automatically reduces the cost for large workloads without upfront commitments.