Introducing Standalone Instant Datasets: Build and Test with Realistic Production-like Data with Ease

Erik Landerholm
July 26, 2023
photo credit: vox.athena
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At Release, we are strong advocates of using production-like data for all feature development. We build and test with production-like data daily and think you should too, that’s why we built the dataset replication capability directly into our platform.

Understanding how your database's data integrity impacts your application's behavior and performance, as well as challenging your UI assumptions against production data, can significantly enhance every aspect of software development. This approach not only streamlines the development process but also brings potential issues and defects to light as early as possible, fostering high performance within your team and company.

Today, we are introducing Instant Datasets as a standalone product. Now everyone can build and test with production-like data instantaneously, no exceptions! Whether you already use the Release platform for ephemeral environments, share environments, manage complex applications with multiple data stores, or run a simpler setup, Instant Datasets is now accessible to you. Anyone in need of realistic, up-to-date data for building and testing applications can take advantage of Instant Datasets within their own cloud accounts.

Now, you may wonder why you would need Instant Datasets when you can manually create a copy of your snapshot and add it to your application. While that is a viable solution for obtaining production-like data, Instant Datasets offers a more comprehensive and efficient approach by managing the entire process for you. It generates a pool of instances that can be easily checked out and utilized by individual developers, product managers, test environments, staging environments, and any other user or use case you can imagine. With Instant Datasets, you can effortlessly create and manage multiple datasets based on various cloud databases.

But what about the potential cost of maintaining all those available instances on your cloud account? Fear not! Instant Datasets has implemented several measures to protect you from the dreaded "surprise cloud bill" we've all experienced:

  • Each database comes with a user-defined default Time-to-Live (TTL) when created.
  • Databases can be paused manually or on a schedule, helping you control costs effectively.
  • The "check-in/check-out" process ensures that Instant Datasets cleans up after itself when you no longer need the data, further managing costs.

Although the ability to instantly use production-like data comes with some associated costs, the benefits in terms of application performance and experience make it well worth it.

Now, you might wonder if you can share the databases with the rest of your team and whether they will see the changes you've made to your working copy. Rest assured, every checked-out database is exclusively yours to use and modify for as long as you need it. The moment you check one out, a replacement copy is instantly generated; and once you check your database back in, it is deleted to help you manage costs and maintain integrity of the data. Note that currently Instant Datasets works with AWS RDS and Aurora and we are adding new services soon.

But what if you don't have a budget for another subscription? We've got you covered! For our initial user cohort, we are making standalone Instant Datasets absolutely free. We understand the pain of dealing with inaccurate seed data, and our aim is to make access to production-like data as easy as possible for everyone.

Now, let's address the crucial matter of security. Your data is YOURS, and it remains securely stored within your cloud account. Release orchestrates and manages access to instant replicas without interacting with the actual data itself, ensuring a safe choice even for the most stringent security environments. Datasets are password-protected and you can further limit access based on your specific needs. Additionally, we integrate with services like Tonic to obfuscate data and create "fake" datasets that closely mimic production data without risking exposure to sensitive values.

Finally, we built Instant Datasets for ourselves, using it as a scratchpad of sorts to gain early insights into real applications. By doing so, we've saved countless hours of rework and bug hunting, making our entire team happier and more productive. Now, we invite you to join us on our mission to embrace production-like data and give Instant Datasets a try for yourself. Check out the Quickstart Guide in our documentation and snag a free account while they last, and let's revolutionize the way we build and test applications with realistic data!

Sign up here

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