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Watch On-Demand: Build an explorable and accessible semantic layer with Cube and Hashboard

Join Cube and Hashboard to learn about building governed metrics your business can trust and making those metrics easy to discover and explore with beautiful visualizations.

  • Hashboard
  • Cube

Register to watch on-demand and learn how you can build an explorable and accessible semantic layer with Cube and Hashboard.

In a recent webinar, Brian Bickell, VP of Strategy & Alliances at Cube, Carlos Aguilar, Founder & CEO of Hashboard, and Tony Kau, Partner Solutions Architect at Cube, introduced and discussed strategies for leveraging technology to help your business model and deliver data. Here are the top 5 takeaways:

The Purpose and Functionality of a Semantic Layer

Cube's universal semantic layer is a mediator between data sources and users. It offers various functionalities such as data modeling, access control, caching, and APIs. Ultimately, its task is to provide a single source of truth for data modeling and delivery, empowering organizations to easily manage and access their data.

Making Data Accessible

Hashboard responds to the call to make data differently accessible. Carlos Aguilar put the primary focus on self-service, identifying the necessity of integrating both analyst-friendly and governed workflows in data accessibility. This approach offers the ability to toggle between point-and-click and code-based workflows seamlessly, according to the needs of the user, thus filling gaps left by traditional BI tools.

Cube Cloud for Scalable Data Analysis

Tony Kau presented the ability of Cube Cloud to perform measures and aggregates across multiple dimensions without the need for precomputing all results. This flexibility and efficiency extend to leveraging data caching capabilities that can improve performance and reduce costs. Cube Cloud demonstrates an advanced capacity for managing large data models, which is integral for handling scalable data analysis.

Ease and Optimization with Hashboard

Hashboard boasts a lightweight semantic layer called the data library, which synchronizes with data models in Cube, providing an integrated solution for data management. With features such as resource lineage, version history, and an activity stream for auditability and visibility, professional data engineers can expect more control over their data layer, enhancing their capacity to deliver accurate and timely analytics.

Closing the Gap between Data Models and Goal Setting

Highlighting the necessity of metrics for successful data modeling, Carlos Aguilar emphasized how defining clear measures is essential for relating data to business outcomes. The end goal being to use insights gained from data analysis to better drive key business operations. Hashboard supports this through its feature to set specific goals and create a centralized metrics page with essential definitions.

The seamless integration between Cube and Hashboard presents an exciting development for the world of data modeling, streamlining this process. With easier governance, increased accessibility, and a more streamlined workflow, these two solutions enhances data management. Regardless of your organization's size, understanding and properly managing your data is surely the key to success in today's technology-dependent world.