Here’s the difference between Databricks and PipeRider. The comparison is based on pricing, deployment, business model, and other important factors.
Databricks provides a data lakehouse that unifies your data warehousing and AI use cases on a single platform. With Databricks, you can implement a common approach to data governance across all data types and assets, and execute all of your workloads across data engineering, data warehousing, data streaming, data science, and machine learning on a single copy of the data. Built on open source and open standards, with hundreds of active partnerships, Databricks easily integrates with your modern data stack. Additionally, Databricks uses an open standards approach to data sharing to eliminate ecosystem restrictions. Finally, Databricks provides a consistent data platform across clouds to reduce the friction of multicloud environments. Today, Databricks has over 7000 customers, including Amgen, Walmart, Disney, HSBC, Shell, Grab, and Instacart.
With PipeRider you can profile your data sources, create highly customizable data quality assertions, and generate insightful reports. It allows you to define the shape of your data once, and then use the data checking functionality to alert you to changes in your data quality.
|Categories||Data Warehouses, Data Lakes||Data Quality Monitoring|
|Stage||Late Stage||Early Stage|
|Target Segment||Enterprise, Mid size||SMB's, Mid-Size|
|Business Model||Commercial||Open Source|
|Pricing||Freemium, Contact Sales||Freemium|
|Location||San Francisco, US||Taipei City, Taiwan|
|Companies using it|