Singer is an open source tool that helps bring order to the messy world of custom ETL scripts. Singer allows users to mix and match inputs and outputs, making their ETL processes much more modular–and therefore easier to run and maintain. Singer uses JSON to move all data between sources and destinations, so you won’t have to worry about incompatible formats once you’ve set your ETL tasks up to run. Panoply’s integration with Singer makes it easy to move data from anywhere with a supported tap–Singer’s term for a data collection script–into an automated cloud warehouse. And since Singer is open source and supported by a broad community of developers, if you can’t find the tap you need, you can just write your own, without having to worry about building loading functions for Panoply.
Databricks Unified Analytics was designed by the original creators of Apache Spark. This paid BI tool combines data science and engineering to perform massive-scale ML data operations. It’s an integrated platform that prepares data, runs experiments, and continuously trains and builds ML models. Then Databricks deploys the AI apps you create across multiple platforms.
Expand Databricks capabilities by integrating it with Panoply with one click. Panoply is the only cloud service that combines an automated ETL with a data warehouse. With Panoply’s seamless Databricks integration, all types of source data are uploaded, sorted, simplified and managed in one place. The Panoply pipeline continuously streams the data to your Databricks output. So your models and apps are always delivering real-time analytics.