How Helvetia accelerates cloud-native modernization by 90% with MongoDB Atlas

“Insurance policies are composed of many diverse attributes, while customer data is hierarchically structured. Both map naturally to rich JSON documents. Trying to force fit these flexible structures into the rigid rows and columns of a relational database would add a lot of friction to our developers. It would slow down our release velocity and limit how quickly we can respond to new demands from the business.”

“Atlas Search is blazing fast! We can surface relevant results from data sets with tens of millions of documents in around 15ms.”

“Coordinating the inter-team dependencies and executing the reindex process meant it would take us 3 to 5 days to roll out new application features with Elasticsearch. Because Atlas Search is embedded right alongside the database, everything is automated for us. Now we’ve got feature releases down to 3 hours, representing a time saving of over 90%.”

“Atlas Search has given us a cleaner and simpler architecture. We can create new indexes in a single API call. Data is automatically synced between the database and search index, giving consistent, high quality query results. Developer and operational overhead is reduced, creating cost and time savings which we can reinvest back into building products for our customers.”