Announcing MongoDB Relational Migrator
We’re thrilled to announce a new tool: MongoDB Relational Migrator . Relational Migrator simplifies the process of moving workloads from relational databases to MongoDB. We’ve heard it from more of our customers than we can count: organizations want to replatform existing applications from relational databases to MongoDB. MongoDB is more intuitive, more flexible, and more scalable than relational databases. Customers tell us that they need to move away from a relational backend in order to build new functionality into existing applications with increased agility, to make new and better use of enterprise data, or to scale existing services to volumes or usage patterns that they were never designed to handle. While some customers have successfully migrated some of their relational workloads to MongoDB, many have struggled with how to approach this challenge. Requirements vary. Can we decommission the old database, or does it need to stay running? Is this a wholesale replatforming, or are we carving out pieces of functionality to move to MongoDB? Some customers end up using a variety of ETL, CDC, message queue, streaming, pub/sub, or other technology to move data into MongoDB, but others have decided it’s just too difficult. It’s also important to think carefully about data modeling as part of a migration. Though it’s possible to naively move a relational schema into MongoDB without any changes, that won’t deliver many of MongoDB’s benefits. A better practice is to design a new and better MongoDB schema that’s more denormalized and potentially to take the opportunity to revise the architecture of the application as well. We want to make this process easier, which is why we’re developing MongoDB Relational Migrator. Relational Migrator streamlines the process of moving to MongoDB from a relational database and is compatible with Oracle, Microsoft SQL Server, MySQL, and PostgreSQL. Migrator connects to a relational database to analyze its existing schema, then helps architects design and map to a new MongoDB schema. When you’re ready, Migrator will perform the data migration from the source RDBMS to MongoDB. Migration can be a one-shot migration if you’re prepared for a hard cutover; soon, we will also support a continuous sync if you need to leave the source system running and continue pushing changes into MongoDB. We know that moving long-running systems to MongoDB still isn’t as simple as pushing a button, which is why Relational Migrator is designed to be used with assistance from our Field Engineering teams. For example, as part of a consulting engagement with MongoDB, a consulting engineer can help you evaluate which applications are the best candidates for migration, design and implement a new MongoDB backend, and execute the migration. Relational Migrator will significantly lower the effort and risk in transforming and replicating your data, leaving more time to focus on other aspects of application modernization. If you’ve been trying to figure out how to get off of a relational database, get in touch to learn more about MongoDB Relational Migrator.
Import and Export Your Charts Dashboards
With the latest release of MongoDB Charts, we’ve added the ability to export any dashboard to a file, as well as import those files back into a Charts project. To export a dashboard, simply choose Export Dashboard from the dashboard’s tile on the main Dashboards page. To Import a dashboard, choose the command from the menu next to Add Dashboard. Let’s look at some things you can do with this new capability. Copy dashboards between projects MongoDB Cloud allows you to create multiple projects, each of which has its own Atlas cluster. There are a bunch of reasons to use multiple projects, but one common example is to use them for different environments of an application, such as Development, QA or Production. Each Charts dashboard also lives within a project, and up until now there was no way of moving or copying a dashboard between projects. This could be problematic if a dashboard that was created in the Development project needed to be promoted to QA or Production. WIth the new Import/Export feature, you can simply export a dashboard from one project and import it into another. Version control your dashboards Taking this example one step further, now that you can export your dashboards to a file, you can treat them as code. That allows you to store the dashboard definitions in a source control system, making it easy to track changes, go back to specific versions, and keep the dashboards stored safely alongside other code artefacts used in your solution. Share dashboards with the community While some dashboards only make sense when connected to your own private data, others may be built on a commonly-available schema, whether that’s the Atlas sample data , some open data from the web, or data created by a reusable script . Once you’ve built a great dashboard using this generally available data, why not export it and share it with the world? Copy dashboards and change their data sources Whenever you import a dashboard from a file, Charts will give you the opportunity to “remap” the data sources used on the dashboard. This is important because the data in the new project might not match what was in the original project. You can use this feature to your advantage if you want to quickly change the data sources used on a dashboard, even if you are importing back into the same project. As an example, suppose you are a multinational company and used a different collection to track sales in each country you operate in. You could build a dashboard with a bunch of great charts, all linked to your “US Sales” collection. If you wanted to easily build an equivalent dashboard for your Australian sales, you could simply export the US dashboard, reimport it and remap your data sources on import to the “Australian Sales” collection. Migrate from Charts on-prem Finally, this feature provides a great option for Charts on-prem users who want to move to the cloud and take advantage of all of the new features only available to cloud users. While the on-prem version of Charts does not have the Export feature, on-prem users can contact MongoDB Support to obtain a script that will generate export files for on-prem dashboards. Those files can then be imported into your MongoDB Cloud projects using the new Import feature. We hope you’re as excited about this feature as we are! Remember, if you haven’t used Charts before, you can get started for free by signing up for MongoDB Cloud , deploying an Atlas cluster and activating Charts.
New Ways to Customize Your Charts
When it comes to building charts, we know that details matter. Small differences in layout, styling or composition can make a big difference in how well your chart communicates the story behind your data. That’s why we’ve just released a whole bunch of new capabilities in MongoDB Charts , giving you more control than ever. Here’s what’s new: Secondary Y Axis: Charts can be a great way to show correlation between two different datasets, but when their scales differ greatly it can be hard to see the correlation. By choosing to plot one more series on a secondary Y Axis, you can allow them to make the most of the available space and highlight any interesting relationships. Secondary Y Axis can be enabled on Grouped Column, Discrete Line, Continuous Line and Continuous Area charts. Legend Position: Chart legends can now be moved to the top, right or bottom of your chart, or hidden altogether. “All Others” Group: Charts has long allowed you to limit a chart to show, say, just the top 10 values. The new “All Others” option allows you to add an additional bar or donut segment that shows the value of all other categories not included in the limit. “Count by Value” aggregation: Building multi-series charts is now easier than ever, with the new “Count by Value” aggregation option. This will automatically create series from each distinct value found in a field. String binning with Regular Expressions: Last month we introduced binning of string values, allowing you to choose the exact values to go into each bin. This month we’ve extended this further by allowing you to use Regular Expressions to assign values to a bin based on powerful patterns. Scatter Mark formatting: We’ve ramped up the customization options available on Scatter charts, allowing you to control the size, border thickness and opacity of each plotted mark. Line Dash Styles: A new option on Discrete and Continuous Line charts results in a different dash style for each series, making it easier to differentiate the series and improve the accessibility of your charts. Here’s one example of a chart that shows off the secondary Y axis, custom legend position and line dash styles: And here’s another, showing the effect you can get by customizing your scatter chart’s mark style: We hope you enjoy these new charting capabilities, but we’re not done yet! Over the next couple of months, we’ll be moving our focus to Table charts, adding options like conditional formatting, text wrapping and column pinning. If you have any other ideas for new customization features, please let us know using the MongoDB Feedback Engine . If you haven’t tried Charts yet, you can get started for free by signing up for MongoDB Atlas and deploying a free tier cluster.
Build Geospatial Visualizations with MongoDB Charts
MongoDB Charts now supports a range of geospatial chart types, allowing you to render data on a map, making it easy to expose trends, gain insights and tell stories using your geographic data. Let us introduce you to the different map chart types and when it makes sense to use them.