Overview: Data Science Studio
An end-to-end cloud data science studio helps you build the automated long-term data foundation required for your analyses.
Last updated
An end-to-end cloud data science studio helps you build the automated long-term data foundation required for your analyses.
Last updated
From data acquisition to analytics and data visualization, the platform delivers an uninterrupted data journey within a customizable data science environment:
You start with a data import from a variety of sources and formats such as IoT routers and the web.
Data science workbooks and data visualization
You visualize the data in custom infographics, perform complex analytics and train machine learning models in data science workbooks.
You get a live view of your processes and automate package loads all the way up to reporting.
The platform emphasizes the following virtues:
End-to-end collaboration: Encourages the free and open exchange of know-how. In bringing your teams on a single platform, you speed up development processes and share insights instantly.
Open platform: Serves as an open, enabling interface for custom analytics. The platform facilitates integrations with a variety of tools and services on top of the data warehouse toolchain.
Scales up or down: You freely modify your resource consumption as your needs change.
Implementation: You deploy the platform in the cloud or locally, as part of your on-premises ecosystem. The Record Evolution platform is consumable as a cloud service by default. Get in touch if seeking to deploy only locally, on on-premises servers.
Data storage and data processing are carried out in data layers. Each layer comes with a certain purpose and special features designed to simplify your process.
Within this layered architecture, you can freely decide how to organize your data and processes. You can even bypass layers to create the results you want.
The first layer is called Sources. This is where you establish a connection to raw data sources.
The raw data is imported, in table format, into entities called Raw Tables.
Traditionally called the Operational Data Store (ODS), this stage is where raw data is stored in a structured way. At this stage, data has a fixed row-and-column structure with defined data types. Applying data types to raw data is a sophisticated technical process that should be implemented before more complex logic is applied.
The Results layer represents your analytical output in the form of data science workbooks and infographics. Results can be utilized by external tools via the REST API or by creating live infographics within the platform.
The Results are recomputed in full on each execution. That is to say, the Results are completely described by the query generating the result set. This also means that there is no additional result versioning in this layer.
Feel free to contact the Record Evolution support team with any questions you might have.
