Using Prisma? Snaplet simplifies seeding your database with getting production-like data.

Learn about database seeding in development: from manual inputs and seed scripts to the power of automated data generation with Snaplet seed. Dive into the world of realistic data and see how it can enhance your development productivity and accuracy.

Seeding your local development database? We explore traditional methods for data seeding and show how Snaplet is safer, easier, and more accurate at getting data to code against. We review seed scripts, manual insertion, and database dumps like pg_dump, and learn about Snaplet's features for data realism, security, and team collaboration.

When you don't use data that looks like production data in your end-to-end tests, you're setting yourself up for problems. We investigate how production-realistic data is often the part of the E2E testing puzzle that's hardest to solve, and how Snaplet can help.

A recent Twitter thread where developers shared their perspectives on the merits of reactive and proactive approaches towards solving bugs in production wasn't just a great collection of war stories, but a great way of framing the problem we're solving at Snaplet.

We're launching pricing for Snaplet. Here's how we thought about it, what we decided on, and what it means for you.

Accidentally deleting data in production is often seen as a rite of passage in the development community. Downtime is the war story of our industry. I'm collecting these stories in a series called "This Is Fine". In this post, I recount my own experience as a young developer.

Snaplet simplified the process of handling large amounts of data, made collaboration and sharing easier, accelerated onboarding and ultimately improved Trunk’s development workflow.

Snaplet has always played well with Supabase, but we’ve taken the next step and have now integrated directly with Supabase via the Supabase API.

Seamlessly transition your database and data between git branches as easily as switching between files in your editor. With the new Snaplet VS Code extension, it's never been easier or more intuitive to code against realistic data all the time, with none of the hassle.

Tired of wrestling with elusive bugs or building features without the right user data at hand? Snaplet's filtered snapshots are here to help you power through your development challenges! Get your hands on the perfect slice of production data, perfectly tailored to your needs.

With preview databases, you can now use a Snaplet snapshot to get a database you can code against in seconds. It's never been easier to safely code against production-like data.

Ever wished you could sneak a peek inside a snapshot without restoring it first? Our latest addition, the Snapshot Data Browser, allows you to double-check that your transformations worked as intended, or that there’s the right kind and amount of data to work with.

Do you feel like you're treading a tightrope when handling production data for development? Let Snaplet's new transformation modes be your safety net. Choose from Unsafe, Strict, or Auto modes to find the perfect balance for your data anonymization journey.

Ever found yourself in a debugging deadlock, unable to reproduce that bug that's wreaking havoc in production? Discover the importance of reproducibility and how Snaplet provides developers with production-like data while keeping compliance in check. Get ready to level up your debugging game!

If you think copying data from your live production database is the way to go, it's time for a reality check! Uncover the risks and pitfalls of the old 'postgres pg_dump', including potential compliance violations and performance issues and see why you should use Snaplet instead of copying sensitive data right out of your production database.

Do you ever think about why seed scripts suck? No? Is it because you're too busy writing actual code instead of fiddling with janky scripts? We're making it a thing of the past and in this post, we'll show you why exactly they're so bad, and why Snaplet is such a better alternative.

We’re closing the loop and bringing stateful databases bootstrapped with realistic data into your Netlify workflow.

When we saw Crunchydata’s post on HackerNews about Postgres running in a browser, we were inspired to replicate the feat and open-source the work to make it available to everyone. In this post, we briefly describe how we collaborated with Supabase to make it happen.

When it comes to securing sensitive data (also referred to as PII) in your database, we tend to err on the side of caution, which is why we're implementing encryption for Snaplet snapshots.

How do you like your data scrambled? Soft? Firm? How about exactly the same as the source data? It's now possible with Snaplet Copycat!

Stop doing data dumps like you used to. Snaplet makes it easy for you to get production-like data into your local development database so you can code against it.

Snappy and the rest of the Snaplet team are just thrilled to be part of the Netlify Jamstack Innovation Fund!

Imagine the ability to add data to your database and “live-reload” it from within your CLI with a simple command, and immediately code against it. Snaplet Proxy allows validation of transformations on the fly. This means you can stay in your editor and hence, stay in the flow.

Obfuscate sensitive data with Typescript and validate your transformations before restoring with Snaplet's data browser (Updated 22Nov 2023)

There are several mock data libraries and fake data generator tools out there. So how is Copycat different from the initial faker library we used in the past? The libraries out there for generating synthetic data (in most cases) generate sequences of data. What we were looking for in our data masking and data generation tool, is a mapping of values, instead of a sequence. This is why we decided to write Copycat.

Our founder Peter had the opportunity to demo how to use Snaplet to safely copy and de-identify your production database. Here's a short round-up of what he had to share.

Ever restored your entire production database to your local environment when you're only changing data in a few specific tables? With Snaplet's subsetting feature, you can be more granular about the data you want to use, making it quicker and easier than ever before to Snappify your workflow!

Writing company documents and policies is no easy undertaking, but getting your engineering team to actually read them is a bit of a challenge. In this post, we share how we got our devs to literally consume our employee value proposition (EVP)

As a Supabase user, you may have struggled with setting up multiple environments on Supabase, and populating each of those with data. We've created a step-by-step guide to show you how Snaplet solves that for you.

We dive into the rabbit hole of coding against anonymized production-like data and provide six reasons why you should be using Snaplet to copy and de-identify your production database for local development and testing.

Snaplet cofounder, Scott Chacon, presented a talk on Prisma Day to demo what Snaplet is and how easy it is to use our database snapshot tool to safely copy and de-identify your production data.

What is PII? I'm not talking about the lobster pie that Snappy (our cute cat mascot) loves so much. We discuss how PII, also known as personally identifiable information is relevant for developers working with production data