June 2023 Summaries

Embedded analytics involves integrating data analytics and visualization capabilities directly into a user's workflow, enhancing user experience by eliminating the need for manual data processing in separate tools. This practice is becoming essential as employees reportedly spend significant time switching between applications to gather information. A modern embedded analytics stack typically includes a data source, a semantic layer, and a presentation layer, with the semantic layer playing a critical role in defining metrics, governing data access, and caching information for consistent performance. Tools like Cube, Snowflake, and Databricks exemplify components of this stack, offering scalable and efficient data solutions. For presentation layers, options range from traditional BI tools like Tableau and Power BI to more flexible interactive notebooks such as Hex and Deepnote, as well as customer-facing frameworks like React and D3.js. These tools enable the creation of tailored, native analytics experiences that are both flexible and efficient, illustrating a significant advancement from traditional BI approaches.

AI-powered data experiences are rapidly evolving, with large language models (LLMs) enhancing data consumption by enabling AI agents to interpret and act on business data. However, LLMs face limitations such as hallucinations and require a semantic layer to provide context, ensuring accurate data processing. This semantic layer organizes data into business definitions, allowing LLMs to query data effectively while avoiding direct interactions with complex database schemas. Cube plays a crucial role in this ecosystem, serving as an interface atop data warehouses, simplifying the querying process, and addressing performance and security concerns through caching and access control. By combining LLMs with semantic layers, Cube facilitates the development of AI-powered applications that can efficiently manage and interpret complex data queries.

Semantic Layer Sync is a feature that simplifies the use of Cube with BI tools by automatically synchronizing data models and enabling self-serve analytics. Data Graph provides a visual way to explore the semantic layer, representing data models as entity relationship diagrams. Single Sign-On with SAML 2.0 is available on the Enterprise tier, while Monitoring Integrations allow users to export logs and performance metrics to external tools. Custom Domains are now available on the Premium tier, enabling users to assign a custom domain to their deployment. These features are part of a week-long launch week that includes improvements to integration with other tools and simplification of complex data models.

Cube's semantic layer offers a powerful API that connects to BI tools through a Postgres-compatible protocol, allowing users to work with Cube without learning a new language or syntax. The company has introduced Semantic Layer Sync, which bridges the gap between semantic layers and BI tools, enabling instantaneous, zero-friction self-serve analytics. With this feature, users can develop their data model and surface metrics from Cube to one or many BI tools in seconds, while maintaining the benefits of never breaking dashboards in a BI tool due to untested changes. Semantic Layer Sync automatically synchronizes the data model between the semantic layer and BI tools, making it easy for data engineers to manage their data models and ensure consistency across different platforms. The feature is now available in Cube Cloud on all tiers, and users can try it today by joining the Slack community at slack.cube.dev.

The latest update from Cube enables the integration of their semantic layer with data orchestration tools, allowing for seamless blending into existing data pipelines. With the Orchestration API, users can now trigger pre-aggregation build jobs or retrieve statuses directly from their chosen data orchestration tool, such as Airflow, Dagster, or Prefect. This feature simplifies the integration process and enables efficient management of data workflows, making it easier to keep data warehouses and pre-aggregations in sync. The Orchestration API is now available to all Cube Core and Cube Cloud users, allowing them to explore its benefits and share their thoughts with the community.

Semantic layers centralize metric definitions and deliver consistently calculated metrics to data consumers, but without proper tooling, entropy can prevail inside the layer's data model. Cube Cloud aims to address this by providing a data model editor, version control support, and development mode with code branches. However, source code is not the only representation of the data model, and humans may have different needs than machines in understanding and working with it. To cater to both, Cube Cloud introduces Data Graph, an entity relationship diagram that uses crow's-foot notation to mark join relationships between cubes, allowing users to visually explore and reason about the data model in a human-friendly way.

The concept of embedded analytics refers to bringing rich data experiences to users within their natural workflow, without the need to toggle between applications. This technology has gained significant traction, driven by the rise of cloud data warehouses and advancements in front-end development tools. The modern data consumer is becoming increasingly diverse, with less technical expertise and varying expectations for usability, richness, and responsiveness. To meet these demands, companies are building customizable and performant embedded analytics features using modern front-end tools, such as React, Hex, Observable, and Streamlit. A cloud data warehouse-centric architecture is emerging, where the data warehouse serves as a backend for data analytics applications, including embedded analytics. This architecture simplifies security and streamlines software onboarding. The semantic layer plays a crucial role in supporting embedded analytics, requiring first-class support for cloud data warehouses, advanced caching, data modeling, diverse APIs, and a hybrid presentation layer to cater to different data consumers, use cases, and teams.

In a discussion about the longevity and impact of data tools in the evolving data landscape, Cube emphasizes the importance of integration and collaboration among data vendors, likening them to celestial bodies in a geocentric model where each believes the ecosystem revolves around them. Cube is dedicated to fostering an open, collaborative future by announcing a series of updates aimed at enhancing interoperability within the data stack. These updates include the introduction of Data Graph, which aims to facilitate better semantic layer integration, the Orchestration API for improved synchronization with data orchestration tools, Semantic Layer Sync to bridge gaps with BI tools, and new integrations with identity providers and monitoring tools. These initiatives are part of Cube's broader vision to create a seamless, collaborative data environment, which they will discuss further in events and social media engagements throughout the week, culminating in a community event on Friday.