Securely integrate ChartMogul with MongoDB Atlas
ChartMogul can turn new and existing business intelligence data into valuable analytics that companies can use to improve their market performance. ChartMogul can take subscriber data - both created within ChartMogul and imported from other data sources - and generate visualized analytics for a variety of metrics that SaaS companies care about.
About MongoDB Atlas
MongoDB Atlas is a cloud database service for applications that works with Amazon AWS, Microsoft Azure, and Google Cloud Platform. The database service seeks to comply with the most stringent data security and privacy standards while offering a reliable suite of drivers, tools, and integrations. By automating numerous database management tasks, MongoDB Atlas helps developers build apps faster with less human error.
Popular Use Cases
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Integrate ChartMogul With MongoDB Atlas Today
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ChartMogul's End Points
Gather data about your subscription plans - like the subscription IDs, names, billing intervals, and the number of intervals that are charged at once - to evaluate the performance of each plan. This will help you better understand the effectiveness of your plans so that you can determine which ones are more or less successful as a whole.
Create, retrieve, or update data for new or imported customers in ChartMogul. This allows you to see important customer contact details, customer IDs, and valuable performance data including a customer’s MRR, ARR, and industry sector. You can then use that data to better segment your customers, which can provide more accurate and specific information about your business performance.
Import invoice data for customers that you are tracking through ChartMogul, including customer IDs, dates of purchase, transactions, and any relevant line items. Then, use ChartMogul to create subscription data for those customers and use that data to track more specific revenue data, both in ChartMogul and in your other data sources.
Track payments or refunds made on an invoice to see the transaction ID, type of transaction, transaction date, and whether or not the transaction was successful. This can help you get more accurate analytics from your invoice data. It can also indicate when there is an unusually high number of refunds, which could signal a problem worth addressing.
Get a list of subscriptions that ChartMogul has automatically generated from invoice data. This endpoint returns several IDs - including subscription IDs, customer IDs, plan IDs, and data source IDs - that will help you to more easily track and integrate data between any of those parameters to create deeper, more accurate business analytics.
Use tags to track terms that are associated with a customer so that you can segment or monitor them more specifically. For example, you could tag a particular customer as “high priority,” “returning” or anything else that is relevant to your business, and then retrieve a list of customers who have been tagged with those attributes in order to analyze them as a segment.
ChartMogul Custom Attributes
Update customer data with ChartMogul custom attributes that are more specific to the needs of your company. This can include both tags as well as more complex custom attributes. Then, track those attributes in ChartMogul to get analytics that are focused on your particular business concerns.
MongoDB Atlas's End Points
MongoDB Atlas Automated Features
The automated security features included in MongoDB Atlas let you monitor who has access to your data while securing your information against unwanted intrusions. Also, due to the platform's automation of mundane operational tasks — like provisioning and configuration, patching and upgrades, monitoring and alerts, advanced security automation, and disaster recovery — you don't have to be a data science expert to set up and run your databases.
MongoDB Advanced Security Automation
MongoDB Atlas provides a variety of database security layers including advanced access control, IP whitelists, in-flight data encryption through TLS/SSL, optional encryption of your file system, and network isolation through Amazon VPCs and VPC Peering.
MongoDB Atlas Built-In Replication
MongoDB Atlas offers multiple servers to provide 'always-on' availability. Even if your primary master goes down, multiple backups ensure that your system is always up and running.
MongoDB Atlas Backups and Time-Machine Recovery
The advanced backup and recovery features for MongoDB Atlas guard against data corruption. Whether the threat is from hackers or a team member's innocent mistake, you can rest easy knowing that, after a catastrophic, event you'll have a backup copy to recover your system from a specific time in the past.
MongoDB Atlas Detailed Statistics and Monitoring
MongoDB Atlas provides detailed information and statistics about your database systems. By organizing this information in numerous ways, the platform helps you understand when important changes or upgrades to your system may be necessary. Moreover, if it's time to make changes, you can provision new server instances in a flash.
MongoDB Atlas Automated Patches and Upgrades
Whether it's a new technology upgrade to improve database efficiency or a security patch to protect against a new security threat, MongoDB Atlas automatically upgrades or lets you upgrade with a single click, so you can take advantage of features as soon as they're available. Upgrades happen in a matter of minutes without any downtime required.
MongoDB Atlas Customizable Database Tools
MongoDB Atlas includes a suite of tools that allow you to select your regions, billing options, and more — allowing you to customize server instances to your desired specifications.
MongoDB Atlas Scalability
MongoDB Atlas scales up and down — or scales out horizontally through automatic sharding — according to the needs of your company with zero application downtime. This allows you to grow beyond the limitations of one server without making your application too complex. Moreover, the platform's automatic balancing keeps information equally distributed across multiple replica sets as your data volumes grow, or as your cluster increases or decreases.