HomeToGo is a vacation rental marketplace headquartered in Berlin, Germany. In June of 2021, their team requested a demo of the Anomalo data quality monitoring platform. Their ~35 data analysts, data engineers, and data scientists needed a platform to help them further increase their confidence in their data stored in their data warehouse.
Five months later, HomeToGo has completed a trial, signed on as a customer, scaled their deployment in Kubernetes, and published a case study of their journey. They have found and fixed multiple important data quality issues and democratized access to data quality information.
They have been one of the fastest and most sophisticated teams we have worked with, which bodes very well for the future success of their data-driven marketplace!
“Maintaining trust in our datasets is a top priority for our data teams. Anomalo helps us deliver on this commitment by providing tons of valuable alerts right out of the box. Their intuitive and comprehensive UI enables stakeholders to create alerts on their own. Using Anomalo, we have improved end-to-end visibility and collaboration between engineering and business team.”
Stephan Claus, Head of Data Analytics
The HomeToGo team chose Anomalo because it is:
- <bolderbold>Secure<bolderbold> - Customers can deploy Anomalo fully in-VPC; no data or metadata leaves an environment the customer controls.
- <bolderbold>Easy<bolderbold> - Setting up checks in Anomalo is an easy one-click experience, and our well-designed slack integration allows notification channels per table.
- <bolderbold>Comprehensive<bolderbold> - Our UI has an extensive range of rules, time series, and ML-based data checks, plus a fully automated ML anomaly detection layer.
- <bolderbold>Programmable<bolderbold> - Our API first mentality and pip installable Python SDK enable a programmatic approach to configuring monitoring and alerts.
- <bolderbold>Self-Service<bolderbold> - Anomalo allows data and business users to use their no-code editor to set up their alerts, and our pricing model is per-table rather than per-user.
You can read their full article here.
If your team would like to build the same confidence in your data as HomeToGo, be sure to request a demo.