You can get the control and flexibility of a DIY approach without the hassle.
You are right to want to own your data and data infrastructure, yet businesses often underestimate the resources and ongoing maintenance required to set up and manage their own data pipeline.
Snowplow Insights gives you all the benefits of a build-it-yourself pipeline without any of the heavy lifting. A strategic data asset fully managed for you in your AWS or GCP cloud environment giving you more time and resources to drive value from your data.
Setting up a reliable data pipeline from scratch is a complex and time-intensive process. Snowplow Insights ensures you have the behavioral data management platform you need to power any data project, generate valuable insights and drive a competitive advantage with your data – without sapping your most important resources.
It can take months to build, test and evolve your own data pipelines. We save you time and resources by deploying a complete pipeline in one day, and you can start collecting rich, granular data right away by leveraging our suite of trackers and webhooks (and out-of-the-box tracking).
With Snowplow, your team spends less time debugging issues, and more time deriving value from complete and accurate data. Resolve data quality problems fast with automatic alerting and generate behavioral data your end users can work with straight away instead of spending hours on cleaning and preparation.
Leave the installation, upgrades, and ongoing maintenance of your data pipeline to us, and enjoy guaranteed SLAs on uptime, latency and support response time so your data professionals can focus on moving your business forward with data.
From custom onboarding to tailored training sessions and 24/7 support from expert engineers, we make sure Snowplow Insights customers maximize the value of their behavioral data asset.
Your business won’t stand still, so you need your pipeline to be flexible too. Snowplow empowers you to easily scale your pipeline, and evolve your data structures as your requirements or products change, while maintaining data meaning, data quality and data governance.