The webinar provided insights into Cube, Explo and their roles in embedded analytics. Here's a summary of the key takeaways:
- Cube's Functionality as a Semantic Layer: Cube plays a significant role as a universal semantic layer by streamlining the accessibility and consistency of data across multiple platforms. These data-driven use cases allow companies to transform chaotic many-to-many data relationships into a usable model. Cube offers four key functionalities:
- Data Modeling: To bring consistency across multiple databases and BI tools
- Access Control: Provides advanced security measures that can be customized as needed
- Caching: Facilitates improved performance for OLAP queries compared to relational databases
- APIs: Avails various APIs (REST, GraphQL, SQL) for data access
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Introduction To Explo: Explo, a customer-facing platform, offers dashboard and reporting software for SaaS companies and B2B solutions. It provides complex customer permissions, data security, full white-labeling, and offers customization for the appearance of its solutions. Explo connects to CUBE via their SQL BI connector and offers diverse ways to share metrics and reports.
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Data Modeling With Cube: The webinar highlights the differences between cubes and views in data modeling with Cube, where cubes are basic objects used for joins, optimizing dimensions, and creating measures. On the other hand, views simplify the relationships between cubes and present finalized, abstract datasets for downstream tools.
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Creating Dashboards With Explo: Explo is designed to allow easy dashboard building and manipulation. The platform provides controls to manipulate data and create a wide variety of visualizations. It allows for version control, embedding the dashboards, and customizing the styles to suit specific needs.
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Cube Store And Materialized Views: The webinar clarifies the difference between using the Cube Store cache or materialized views in data warehouses. The Cube Store cache is designed for real-time access to pre-aggregated data for analytics and reporting and is optimal for read-heavy workloads. Materialized views in a data warehouse are more suitable for data transformations and pre-calculations.
These are some of the highlights from the webinar, offering actionable insights into Cube, Explo, and their implications in the world of data warehousing, analytics, and reporting. To gather further insights and explore these offerings in more depth, visit Cube's blog or Explo's platform.