The fast-growing startup VITAS.ai from Nuremberg, Germany builds an intelligent, AI-based phone assistant that helps companies to automate the handling of customer requests via phone calls. When they decided to build up business intelligence and analytics tooling, they faced the traditional make-or-buy decision.
VITAS.ai runs its SaaS application on top of a few operational MySQL databases and faced the challenge to integrate them at a central location for analytical purposes. If possible, the integration and data extraction should be performed without putting load on their production database systems, leaving the rest of their workloads unaffected. They compared six different providers, including DataCater, for handling their data integration needs. Early-stage startups need to iterate fast on their core product and cannot spend too much time on non-core tasks. That is why VITAS.ai was looking to buy an ETL product that delivers results fast.
After an extensive evaluation, VITAS.ai went for implementing their ETL pipelines with the data pipeline platform DataCater. The primary reason behind their decision was the outstanding time to production that DataCater provides: Their software engineers have been able to build and deploy the first streaming data pipelines, connecting their MySQL databases with their data warehouse solution Google Cloud BigQuery, in a couple of minutes. Another motivation for choosing DataCater was the self-managed installation of DataCater. VITAS.ai had prior experience with Kubernetes and has been able to install DataCater in a scalable way using the GCP-tailored Helm chart with minimal effort.
“After evaluating six providers, we chose DataCater and are extremely happy with the decision. We are able to build our pipelines with DataCater’s no-code pipeline designer in a tremendously short amount of time. Its support for Python transformations enables us to implement complex data transformations quickly and safely.”
Thomas Abend (CEO, VITAS.ai)
Fast time to production: DataCater's no-code pipeline designer allows users to integrate new data sources and build streaming data pipelines in minutes not weeks.
Powerful data transforms: DataCater's support for Python-based data transformations allows VITAS.ai to cope with even complex data preparation requirements.
Self-managed deployment for data sovereignty: VITAS.ai deals with sensitive medical information. They deploy DataCater on their own Kubernetes clusters such that their data do not leave their premises.
The fast-growing startup VITAS.ai builds an intelligent, AI-based phone assistant that helps businesses answer customer requests via phone calls.
DataCater is the low-code platform for streaming data pipelines, which makes Apache Kafka®-powered streaming data pipelines accessible to data teams. DataCater can be used to integrate a wide range of data systems, such as database systems and web APIs, in real-time.