A bilingual wordsmith dedicated to the art of engineering with words, Chiara has over a decade of experience supporting corporate communications at multi-national companies. She once translated for the Pope; it has more overlap with translating for developers than you might think.
At Tonic, we’ve been mimicking structured data since day 1. But let’s face it: not all data belongs in columns and rows. Which is why we’re very excited to announce our newest database integration: Tonic can now mimic your document-based data in MongoDB.
This latest integration is joined by a full parade of additional databases Tonic now natively connects with, including Amazon Redshift, Databricks, BigQuery, Spark on Amazon EMR, and Db2. The list represents an expansion of our technology into the realm of data warehouses. We’re enabling companies to securely de-identify their sensitive data the moment it comes into their database, for long-term, compliant storage, without running up against the limits dictated by privacy regulations.
From Postgres to Redshift to MongoDB, our customers work in multi-database environments, and our technology equips them to work across those databases, regardless of the type, to create a true mimic of their entire data ecosystem.
Let’s talk details.
Customer profiles, webforms, financial transactions, medical records—so much sensitive data is stored in Mongo’s document-oriented databases. De-identifying it is not an easy task. Here’s a quick look at the challenges and how we solve for each:
We’re proud to be leading the industry in offering de-identification of semi-structured data in MongoDB, for which the landscape of available tools is currently very limited.
The market has spoken: big data needs a big warehouse, and we're here to help keep it safe. Demand for data anonymization in Redshift, Databricks, and BigQuery is skyrocketing, thanks to the ever-increasing amounts of data companies need to store and the ever-stricter regulations around how long sensitive data can be kept in a database.
We’ve built database connectors for today’s leading data warehouses to enable organizations to store their data compliantly. And if developers need to access that data for QA and testing, they can do so without infringing on anyone’s privacy, while still getting data they can use. In addition to prioritizing privacy, our tools are designed to ensure data utility, no matter the scale of the dataset you’re working with.
Here are a few of the features in Tonic tailored to the specific needs of de-identifying data stored in data warehouses:
Yes, we did! Not to be overshadowed by NoSQL documents and large-looming warehouses, Db2 is the latest addition to our list of relational database integrations, and we’re excited to support it. We can connect to both Db2 LUW and iSeries, and offer our full suite of tools for each. Similar to with MongoDB, Tonic is one of the few data generation tools available that currently offers a Db2 integration.
The database-agnostic nature of Tonic and the ability to connect directly to your database, as opposed to a data-upload approach, are two of our key differentiators as compared to other data anonymization and synthesis tools on the market today. Add to this the rare ability to work with document-based data in MongoDB, and we’re excited to be leading the charge in getting developers and data engineers the safe, realistic data they need.
What database are you connecting to? Or better yet, what new database would you like to see? Drop us a line and let us know; it may just make it into our next launch event.