Cost optimization compared to traditional ETL
Engineering hours saved per month
Data pipelines with 15+ tables across internal systems
Nauto delivers AI-powered fleet management software that coaches drivers to reduce collisions and save lives. For years, Nauto relied on fragile point-to-point integrations as it took new orders, processed payments, shipped hardware to customers and managed customer subscriptions to its cloud data processing services. One broken integration could leave its business users unable to serve customers for days — and different business systems rarely shared the same version of the truth. Seeking to provide seamless customer service, Nauto ultimately looked for a way to establish a single data repository that it could manage in-house using flexible modern tools.
Today, all the data that matters to Nauto’s business is readily available in Databricks Lakehouse. Now that Nauto has moved its data out of proprietary systems and into Amazon Web Services, the company has total control over access and formats.
“We knew we wanted a lakehouse architecture, as it is cost-effective and open. It helps us bring all our data in one place, regardless of the format or type, and use it in any format we need,” said Ernest Prabhakar, Business Data Lead, at Nauto.
Nauto uses Hightouch to sync data automatically from Databricks to Salesforce and from Databricks to NetSuite, eliminating the complex series of scripts and spreadsheets it previously used to track data changes.
The company also followed many recommendations from peers by implementing Fivetran, which enables Nauto to centralize its business data in Databricks with just a few clicks. This newfound integration has helped Nauto streamline tasks such as coordinating device returns — rather than spending weeks comparing spreadsheets, the company now generates the necessary reports and workflows automatically. In addition, customer dashboards and billing statements display the same accurate billing information, eliminating time-consuming disputes. The IT team has seen 75% cost optimization compared to traditional ETL approaches such as Informatica.
Since its transformation, Nauto has gone from having a limited data strategy to implementing a modern data stack, running three major Fivetran data pipelines with at least 15 tables. Within one month, everyone aligned around a common set of metrics, and IT no longer spends three days (and 80 engineering hours) per month debugging data inconsistencies. Everyone across teams can contribute their own data in their own format to support major strategic decisions. Nauto now has total confidence that its business systems are in sync — which gives decision-makers the perspective they need to lead the company forward.