Goodbye, January! New MongoDB Goodness

Lots of new things in MongoDB’s offerings as we get ready to say hello to February!

1) Public preview of MongoDB Atlas for Azure Data Studio extension | Partner Blog | Azure Release Notes | Azure Data Studio extension Docs
– Azure Data Studio is a downloadable data analytics tool that allows Azure customers to interact with their data on one or more data services, such as SQL Server or other Azure databases.
– Previously, Azure customers were unable to view or query their data on MongoDB Atlas from within Azure Data Studio.
– Now, customers can use a new extension for Azure Data Studio, available in preview, that allows them to seamlessly connect to and query data in MongoDB Atlas alongside other data services to provide a unified view of their data estate.

2) Ability to convert free and shared clusters to serverless instances in Atlas | Docs
– Customers with free or shared clusters (M0, M2, M5) can now directly convert their cluster to a serverless instance via the Atlas UI or Admin API.
– Previously, if a customer with a free or shared cluster wanted to move to a serverless instance, they needed to manually dump and restore their data to a newly created or existing serverless instance using command line tools or by exporting and importing their data via Compass.
– Now, customers can easily change their deployment type by editing their cluster configuration, in the same way that they would if they wanted to upgrade to a dedicated cluster.

3) Provenance and collection globbing in Atlas Data Federation | Docs: provenance, collection globbing
– Customers can now more easily query across complex topologies, such as multi-tenant systems, with two new features in Atlas Data Federation.
– Previously, customers had to manually configure each mapping to provide context on the data source or to combine collections across different databases. This made it difficult and time-consuming to manage federated database instances as data grew and changed over time, especially for customers with hundreds or thousands of sources.
– Now, customers can add the provenance of documents to the storage configuration, such as the Atlas cluster name or AWS region of the S3 bucket, to make it easy to understand where each piece of data came from at query time.
– Customers can also now glob collections together in the storage configuration, eliminating the extra step of first creating a new virtual collection.

4) Ability to set Atlas Data Federation storage configuration with the Atlas Admin API | Docs
 — Customers can now use the Atlas Admin API to configure federated database instances.
 — Previously, customers could only set the storage configuration to map data stores to collections for querying their federated database instance by using the Atlas UI with a Visual Editor or JSON Editor, or by using the MongoDB shell.
 — Customers who are accustomed to managing their infrastructure as code can now easily integrate Atlas Data Federation into their preferred workflows.

5) Filtered sync in Cluster-to-Cluster Sync | Docs
– Customers can now sync specific collections of data instead of an entire cluster with Cluster-to-Cluster Sync.
– Previously, customers had to first migrate a carbon copy of their data to the destination, then delete any unwanted collections as a second step.
– Now, customers can select which collections they want to sync prior to starting the sync process.
– With this added flexibility, customers can reduce the amount of time required for both ongoing and one-time sync use cases.

6) Support for unlike topologies in Cluster-to-Cluster Sync | Docs
– Customers can now use Cluster-to-Cluster sync with clusters with different sharding configurations.
– Previously, customers could only migrate from a source to a destination cluster with the same topology, forcing them to perform sharding operations either before or after the migration.
– Now, customers can pre-define the destination cluster with the desired topology, including sharded collections and the number of shards, and Cluster-to-Cluster Sync will facilitate both migrating the data and changing the topology in-flight.

7) Cloud provider authentication improvements and native AWS EKS support for Java Driver | Release notes
– Customers now have additional options for securely connecting between MongoDB and their cloud environments when building Java applications.
– Previously, customers running on Google Cloud and Azure had to manage a separate set of credentials for the client application, which introduced additional security concerns and was a barrier to the adoption of client-side encryption.
– Now, customers can automatically obtain credentials from their Google Cloud and Azure environments in the Java driver for in-use encryption.
– Previously, customers using Kubernetes running on AWS would need to supply a callback to the Java driver to allow it to supply credentials for any type of service, including Elastic Kubernetes Service (EKS).
– Now, customers using the Java driver can take advantage of native support for EKS, no application-supplied callback required.

8) Aggregation pipeline text editor in Compass | Release notes | Docs
– A new pipeline text editor makes it easier for customers to workshop aggregations in Compass.
– Previously, customers were limited to adding and editing aggregations in stages, making it difficult to modify long or complex queries.
– Now, customers can use a text editor to copy/paste text from other tools, such as an IDE, as well as write and edit free-form entire aggregation pipelines end-to-end.

9) Global user settings in Compass | Release notes | Docs
– Enterprise customers can now easily manage global settings across multiple users using a configuration file in Compass.
– Previously, customers were unable to set global settings for multiple users, such as blocking outgoing network connections, restricting access to read-only for certain users, or forcing a specific read preference. This lack of governance prevented certain customers from sanctioning the use of Compass among teams.
– Now, customers have more granular controls available for access and other settings and can ensure a consistent experience across users, making it easier for large companies to adopt and use Compass when developing with MongoDB.