TECHNOLOGY PARTNERS

EMBEDDABLE & RITTMAN ANALYTICS

The developer toolkit for building remarkable analytics experiences in 10% of the time

DEEP DIVE

EMBEDDABLE & RITTMAN ANALYTICS

Build Remarkable Analytics Experiences

Embeddable is a developer toolkit for building fast, interactive, fully-custom analytics experiences directly into your app.

It lets you create beautiful, pixel-perfect dashboards that look, feel, and behave exactly like the rest of your platform, in 10% of the time.

Scalable data products powered by Cube semantic models

Embeddable leverages the power of Cube, an open-source semantic model and caching service that converts your app data structures into a more accessible format for Embeddable to interpret and visualise.

This supports data consistency across your analytics and enabling you to achieve lightning-fast loading speeds whilst keeping database costs low

There are multiple options on how to structure your data stack for use with Embeddable, depending on your needs. In some cases, the data volume is relatively small and the database can handle the additional load from analytical queries without affecting app performance.

However it might be more advisable to stage your app data in a separate data warehouse, transformed and structured using a tool such as dbt for reasons of performance, data transformation, data integration, data quality or scalability:

  • Application databases are typically optimised for transactional operations, not analytical queries. Directly running complex analytical queries on the application database can slow down the application's performance. By staging the data in a separate data warehouse, you can offload the analytical processing from the application database, ensuring that the application's performance is not affected.

  • Application databases often store data in a format that is optimised for the application's needs, not for analytics. Tools like dbt can be used to transform and structure the data in a way that is more suitable for analytics. For example, dbt can be used to create a semantic layer that simplifies complex data structures into a more accessible format for Cube to interpret and visualise

  • If you have data from multiple sources that you want to analyse together, it can be beneficial to stage all this data in a single data warehouse. Tools like dbt can be used to integrate this data, ensuring that it is consistent and can be analysed together

  • dbt provides a framework to test assumptions about the results generated by a model, which helps provide assurance that your SQL is transforming data in the way you expect, and your source data contains the values you expect. This can be crucial for ensuring the accuracy and reliability of your analytics

  • As your data volume grows, it can become increasingly difficult to manage and analyse the data directly in the application database. A separate data warehouse can provide more scalability, allowing you to handle larger data volumes more effectively

SERVICES

EMBEDDABLE QUICKSTART PACKAGES

Rittman Analytics partners with Embeddable and Cube to help your team get your data into shape, modelled and transformed and ready for use as the datasets for your Embeddable data visualizations.

To find-out more about our Embeddable Quickstart Package or other consulting services for the modern data stack, either contact us at info@rittmananalytics.com, download our services brochure or press the button below to arrange a free, no-obligation initial Discovery call.

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