Fullstory helps companies record and analyze their customer communications by recording user sessions, providing detailed step-by-step logs of everything customers did during their sessions and storing that information for later retrieval and analysis. Fullstory can then be searched for specific events, including link usage, rage clicks or dead clicks. In addition to data on individual sessions, Fullstory can also retrieve analytics on aggregate customer behavior, showing the most clicked items, the most rage clicked areas, the most navigated to sites, etc.
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.
Bring all your Fullstory data to Amazon Redshift
Load your Fullstory data to Google BigQuery
ETL all your Fullstory data to Snowflake
Move your Fullstory data to MySQL
Provide a user ID and/or email address to get a list of every session associated with a specific user up to the defined limit. Entries in that list include the user ID and email address, the time the session occurred, and the Fullstory session URL, all of which can be used to access the data from that session if desired.
Retrieve a list of 20 available data bundles from a specific timestamp onward. Then, export the most valuable data bundles from that list so that they can be integrated with other relevant data sources to give you a deeper overall view of your customer’s experience.
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 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 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.
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 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.
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 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 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.