Watch On-Demand: Get Started with Embedded Analytics

Key Takeaways from the Embedded Analytics with Cube Webinar

During this workshop, Rory Gatto, a Customer Experience Engineer at Cube, shared invaluable insights on embedded analytics using Cube. This tool's unified data modeling potential offers a unique opportunity to comprehensively understand multiple data sources. Among the numerous points raised during the tutorial by Rory, five emerged as particularly critical for optimum utilization of Cube Cloud.

Cube for Unified Data Modeling: Cube's hallmark feature is its ability to unify disparate data sources, offering a single “source of truth” for data modeling. Its compatibility with multiple data sources alleviates the hassle of constant referrals to various databases, thereby fostering efficient data analysis.
Cube Deployment and Connection to Snowflake: Rory highlighted the simplicity of creating a Cube deployment and establishing a link to a data source like Snowflake. The connection to Snowflake further expands Cube's interoperability, allowing data to be moved, analyzed, and processed smoothly.
Data Modeling Wizard: This feature simplifies the creation of data models. Rory showed how to utilize this wizard, thereby enhancing understanding among users who may be new to data modeling or wish to improve their modeling strategies.
Chart Prototyping Feature: This feature allows users to generate downloadable code for stand-alone apps to visualize the data from Cube. The chart prototyping not only aids in the convenient inspection of data but also fuels creativity among users, encouraging them to create unique data visualization tools.
Charting Libraries and API Connections: The company supplements its number of charting libraries, paving the way for enhanced data visualization. Furthermore, users can conveniently connect to Cube's API and exploit third-party charting libraries for enriched, graphical presentations of their data. The inclusion of more libraries will serve to diversify visualization options and create a more enriching user experience overall.

A significant aspect of utilizing Cube involves building out the models necessarily for a multi-tenancy application. According to Rory, while this task may initially seem daunting, it becomes more manageable once the initial models are complete and the infrastructure is prepared for querying. He also recommended using Cube for modeling in multi-tenant situations.

In conclusion, convenient data modeling, seamless connections with platforms such as Snowflake, simplified chart prototyping and an intriguing prospect of enriched charting libraries, all combine to create a powerful tool for data management and visualization.