Embedded analytics offers the seamless integration of rich data experiences into users' natural workflows. Let me walk you through the technical requirements of a modern embedded analytics data stack, highlighting the role of a data warehouse-centric architecture and a robust semantic layer. The evolution towards decoupling BI from the frontend not only ensures total consistency of data across applications but also opens the door to exciting developments and cost-savings.

What is embedded analytics?

Embedded analytics means much more than using iframes to place charts in a dashboard. The real promise of embedded analytics is to bring rich data experiences to users where they already are. As Gartner puts it, “data analysis occurs within a user’s natural workflow, without the need to toggle to another application.”

With embedded analytics, financial analysis can be built right into a personal finance application. Personal health metrics can be displayed alongside one’s medical history. Every member of a team can see the business’s KPIs and trends without logging into a BI tool.

For businesses, embedded analytics affords full control over what data is available and how it’s presented. For data consumers, embedded analytics means finding useful insights within the sites or apps they already use—without toggling between unfriendly, third-party dashboards. A Harvard Business Review study found that switching between applications costs employees time equivalent of up to five weeks a year.

At Cube, we’ve been on the front lines of embedded analytics for years. Here are some recent technological and behavioral shifts we’ve observed.

The rise of the cloud data warehouse

In the past, collecting and processing massive data sets was so technically complex and expensive that only a few companies did so. Now, cloud data warehouses make it possible for organizations in every industry and of every size to easily and affordably collect and process more data—which is often the most uniquely competitive asset a company has. This newfound accessibility has led to the explosion in adoption of cloud data warehouses.

But it's not enough to just collect lots of data. Businesses need to use the data, too—which has increased the need for customizable, powerful data experiences. As a result, companies’ adoption of cloud data warehouses has led to greater interest and investment in embedded analytics, too.

Data consumers are more diverse

As more and more businesses invest in embedded analytics, there is also a quickly growing variety of data consumers. Business intelligence was once the purview of specialized engineers and analysts—but now, every member of an organization is expected to use and understand data to perform their job.

So, who is the modern data consumer? For starters, they may be less technical. The modern data consumer may not be familiar with data engineering concepts like OLAP—and they shouldn’t have to be. Moreover, the modern data consumer may not even be familiar with legacy BI tools—and, again, they shouldn’t need to be.

In order to make data accessible and actionable for these data consumers, companies need to build customized user interfaces that move beyond mere iframed graphs. And so, to match the pace of increasing diversity in data consumers, embedded analytics applications have grown more polished and interactive.

Front-end development is easier

A major change in the world of embedded analytics is the rise of modern front-end development tools. In the past decade, there have been incredible advances in powerful, simple front-end technologies—particularly React. It’s now easier than ever to design tailored, streamlined user experiences.

This innovation hasn’t just made it easier to develop custom data experiences; it also has altered users’ expectations to make such experiences essentially mandatory. Both internal data consumers and end users have new, higher standards for usability, richness, and responsiveness.

Since these expectations can’t be met with generic dashboards or out-of-the-box BI platforms, more companies are building customizable and performant embedded analytics features using modern front-end tools.

There are new tools

It’s become easier for front-end development teams to build highly custom native UIs, but these aren’t necessary for every project. Sometimes, standard visualizations are enough and development speed is key.

A new generation of collaborative data tools, including Hex, Observable, and Streamlit, make it possible for data analysts to quickly select the appropriate chart for a dataset and share reports both throughout a company and to end customers.

Organizations need the freedom to choose the right tool for each job—whether it’s a custom application built by front-end teams or shareable dashboards built by data teams.

Rethinking the data stack


So, as we think about these developments, what are the technical requirements of a modern embedded analytics data stack?

Data warehouse-centric architecture

As others have noticed, the cloud data warehouse is a place not only store and process data, but also make it interactive. To actually be a backend for data analytics applications, including embedded analytics, the data warehouse needs to be at the core of your data stack.

Having data in a single place significantly reduces operational overhead. On one end, ELT is replacing ETL, on the other end, we see a trend away from moving data into “serving” databases and towards powering applications with data directly from warehouses. This architecture is easier to orchestrate and less error-prone because it removes additional pipelines and storage layers.

Warehouses still predominantly work with batch data, but there are a lot of recent exciting developments in supporting real-time data. Firebolt, Clickhouse and Materialize are actively innovating in the space.

Finally, this architecture also opens an opportunity for bring-your-own-data-warehouse applications. This may be beneficial for both vendors and consumers because it simplifies the security architecture and streamlines new software onboarding.

Semantic Layer in the middle

We’ve previously described the four essential components of a semantic layer. To specifically support embedded analytics, a semantic layer solution must have these attributes:

First-class support for cloud data warehouses

A major piece of a semantic layer is access control integrated with the data warehouse’s security controls, because embedded analytics always require multitenancy. A second piece is advanced caching, because although the data warehouse is a great candidate for a backend, it doesn't does not support high-concurrency small queries with the low latency that modern data consumers expect.

Data modeling

Across the wide variety of new data consumers, everyone should consume the same data: “net sales” in your dashboard should mean the same thing as “net sales” in my mobile CRM. To achieve this consistency, data modeling and defining metrics should be handled once, and this must be up-stack from every application or dashboard.

Diverse APIs

Different data consumers have different expectations and requirements, so it’s inevitable that a company will end up supporting a whole class of data apps. For all of these applications to share the same metrics, a semantic layer must be accessible via multiple APIs, e.g., SQL, GraphQL, and REST.

A hybrid presentation layer

To support different data consumers, use cases, and teams, an embedded analytics presentation layer should be universally open and compatible with every frontend architecture; organizations should be able to granularly curate their stacks.

When high customization is required and front-end teams are looped in, different charting libraries can be used, ranging from D3 to Chart.js and Highcharts. These most likely will be natively integrated with frontend application frameworks like React or Angular.

When requirements are less custom, tools like Observable or Dash come into the picture. These tools can be natively integrated into an app and combined with a more custom interface, while making it faster and cheaper to build tailored applications.

And on the least customizable—but lowest barrier to entry end—data analysts and engineers can quickly build appealing data interfaces with modern BI tools like Superset and Metabase. These tools don't require front-end knowledge, which usually enables even faster building and iteration. They too can be combined with other presentation layers within a single application.

Finally, there is the emerging category of no-code/low-code tools for internal tooling. This segment includes Appsmith and Retool which can also be used to build analytics interfaces.


We’ve moved far past the era when embedded analytics was considered a part of a single integrated BI solution. The latest generation of BI vendors have already innovated to run “live” queries directly on top of data warehouses, which allows for cloud data warehouse-centric architectures.

The next evolutionary step—one that’s already rapidly taking place—is decoupling BI from the frontend. By doing so, organizations are empowered to facilitate total consistency of the same data across all data applications, as well as AI agents and chatbots.

This architecture will be enabled by several products, including Cube. And, as the category matures, and more enterprises adopt this architecture, we’ll see exciting, exponentially compounding developments. So, buckle up.

Get started with Cube for free today, or get in touch to discuss your next embedded analytics project!