We’re building a data engineering platform focused on developer experience. Our goal is to empower data scientists to quickly turn ad-hoc data science into dependable data services.
There is a chasm between data science and data engineering. Data science is creative, focused on understanding data and finding insights and use cases. Data engineering is operational: integrating infrastructure and data sources, deploying pipelines, managing permissions, and tracking data quality and lineage.
Too much good data science stays ad-hoc because the engineering overhead is too great. To trigger real-time alerts, respond to user input, or integrate analytics in your app, you quickly wind up with Postgres, BigQuery, Fivetran, DBT, maybe Kafka, an API gateway, and homebaked Python data pipelines. Sound familiar?
We’re pursuing a unified approach that combines data storage, processing, and visualization with data quality management and governance in one platform. We’re inspired by services like Gitlab and Netlify that make it remarkable easy for developers to build and run web apps. We want to give data scientists a platform to deploy, monitor, derive, visualize, integrate, and share analytics.
Getting there is a journey and we’re starting with data storage in the form of streams you can replay, subscribe to, query with SQL, monitor, and share. Streams are the primitive the rest of our roadmap builds upon.
We hope you’re excited about our journey. We love to get feedback, so if you’re up for it, reach out. If you just want to stay updated on our progress, follow us on Twitter or sign up for our newsletter:
Data visualization and exploration
Data quality and governance