March 2026 Summaries

Apache Superset's Model Context Protocol (MCP) service is a library-first architecture that integrates with Superset as a Python library, allowing independent scaling and clean asynchronous support without the need for a full web context. This architecture facilitates the exploration and invocation of Superset's analytics capabilities by AI applications through the MCP, an open standard that enables seamless communication between compliant LLM clients and tool servers. Key features of the MCP service include a preview-first workflow for non-destructive data exploration, zero privilege escalation to maintain security integrity, and token budget management to prevent overwhelming LLM context windows. The service exposes Superset's analytics tools, such as chart and dashboard management, dataset discovery, and SQL execution, in a structured manner that respects existing role-based access control (RBAC) and row-level security (RLS) rules. Additionally, the middleware architecture and response caching mechanisms enhance performance and observability, while extensions allow for the registration of custom tools.

Preset MCP enhances the open-source Apache Superset MCP service by adding features tailored for multi-tenant environments, enterprise authentication, and improved user interfaces. By adopting a "wrap, don't fork" approach, Preset maintains compatibility with the open-source code while integrating middleware layers, authentication providers, and infrastructure components to support multi-tenancy and enterprise-grade security without altering the original architecture. The service includes JWT and OAuth 2.0 authentication modes, a built-in Chatbot for user interaction within the Superset UI, and a dedicated Kubernetes deployment for scalability. Preset's modifications ensure that open-source improvements directly benefit its customers, offering additional enterprise capabilities like workspace isolation, observability through Datadog, and per-workspace feature gating, all while maintaining the original security guarantees and tool functions.

The emergence of the role of AI Enablement Engineer reflects a shift in organizations aiming to harness AI's potential by enabling team-wide AI adoption rather than individual productivity boosts. This role involves practical, technical work to facilitate AI integration across teams, helping organizations climb the AI Enablement Pyramid, which progresses from access and policy to special-purpose agents. Unlike traditional roles, AI Enablement Engineers focus on building infrastructure, connecting agents to internal tools, and ensuring AI serves the entire team. The role has become critical as AI capabilities have grown and the gap between AI-native and AI-absent teams has widened. Tools like Agor have been developed to support these engineers by providing a collaborative platform for orchestrating AI agents, enabling visibility and coordination across teams while managing data access and security concerns. The role is seen as a key driver for scalable productivity gains in tech companies, and organizations are beginning to formalize it to stay competitive in AI adoption.

Tableau, a leading platform in business intelligence, revolutionized visual analytics with its desktop-first model, but faces challenges adapting to modern data practices dominated by cloud-native warehouses and browser-based collaboration. Acquired by Salesforce in 2019, Tableau continues to offer a robust visualization library but struggles with friction in integrating with cloud data environments and managing costs through per-seat licensing. In contrast, Apache Superset, developed by Airbnb, addresses these modern needs by being an open-source, browser-based platform that seamlessly integrates with cloud warehouses, supports a code-centric workflow, and eliminates per-user costs, making it an attractive option for teams seeking scalable self-serve analytics. Superset's architecture facilitates easier deployment and management of analytics assets, offering more flexibility and cost efficiency, especially through the managed service Preset Cloud. The choice between Tableau and Superset hinges on organizational needs: Tableau remains suitable for those with entrenched desktop workflows, while Superset appeals to teams prioritizing cloud-native, open-source solutions with broad access and lower operational costs.

Databricks is a robust platform that, when combined with the Lakehouse, Unity Catalog, and Genie, offers significant advantages, particularly for exploratory and ad-hoc analysis through natural language querying. However, Genie may struggle with complex, organization-specific business logic that requires ongoing curation to maintain accuracy. Preset, which integrates natively with Databricks, provides a complementary solution by leveraging validated business logic and existing datasets, offering a portable analytics layer that can connect to multiple data sources and semantic layers. This approach enhances cost predictability and flexibility, especially as organizations scale and diversify their data stacks. Both platforms support the Model Context Protocol (MCP), but Preset's open standard allows for broader compatibility with various AI tools, offering a flexible alternative for teams looking to maintain a consistent analytics experience across different environments.

DatAgor is an AI-driven Agor Assistant at Preset, designed to streamline and enhance data operations by answering questions, writing dbt models, executing SQL queries, building dashboards, and monitoring data infrastructure within the company. Unlike traditional chatbots, DatAgor retains memory and skills, coordinates with other agents, and operates autonomously on a schedule, providing transparency and efficiency in managing tasks. It operates within a framework that includes file-based memory and executable skills, allowing it to evolve and adapt to new tasks over time. DatAgor is part of a growing roster of Agor Assistants at Preset, each with specialized roles such as legal expertise or release management, aiming to make expertise accessible and improve workflow efficiency. The system is designed to function as a persistent team member, providing continuity and proactive monitoring, which traditional hiring patterns may not accommodate, thus redefining the role of AI in organizational data management.

In the digital era, marketing dashboards have become essential tools for businesses to transform vast amounts of raw data into actionable insights efficiently. These dashboards consolidate key metrics and data from various sources such as website analytics, CRM systems, and social media platforms into a unified workspace, enabling faster decision-making and reducing reporting times. A well-designed dashboard not only simplifies data analysis by providing real-time insights but also facilitates informed decision-making, enhances ROI tracking, and fosters improved communication and collaboration within teams. The use of visualization tools helps present data in an easily interpretable format, making patterns and trends visible at a glance. Preset, a platform for creating customizable and interactive dashboards, offers powerful visualization capabilities and user-friendly interfaces, allowing even non-experts to build effective dashboards tailored to specific business goals. With the integration of predictive analytics and AI, the future of marketing dashboards is set to offer even deeper insights, further streamlining marketing strategies and enhancing business outcomes.

The newsletter titled "What's New in Preset?" from the Preset Team, dated March 11, 2026, encourages readers to explore their platform by offering a free trial and stay updated by subscribing to their blog updates. While the specific content of the newsletter is not detailed, it emphasizes visual engagement and provides options for readers to receive weekly digests of new blog posts, suggesting a focus on keeping the audience informed and connected with the latest developments and features of Preset.

Preset has successfully raised $7.27 million in a Series C funding round led by Andreessen Horowitz, with additional investment from Redpoint Ventures and Datatech.fund, as it seeks to enhance the capabilities of Apache Superset, an open-source analytics platform. With over 400 customers and significant growth, Preset aims to overcome infrastructure bottlenecks for AI by providing robust, open-source analytics solutions that improve decision-making through interactive visualizations. Apache Superset's widespread adoption is fueled by the organic growth within organizations, where its open-source nature allows it to be deeply integrated into AI systems. This integration enhances the platform's capabilities and drives further adoption, creating a virtuous cycle of improvement and use. Preset's role is to make Superset enterprise-ready by adding management, governance, collaboration, and AI features, positioning it as a comprehensive analytics solution. The recent funding will be used to expand AI functionalities and strengthen the Superset ecosystem, supporting both users and AI agents in data consumption and analytics.

Healthcare business intelligence (BI) addresses the challenge of integrating disparate clinical, operational, and financial data from systems like Epic, Workday, and ERP to provide actionable insights for healthcare organizations. As the industry generates a substantial portion of the world's data, the demand for effective BI solutions is increasing, with the global market projected to grow significantly by 2035. Healthcare BI transforms this vast data into practical use cases such as clinical, operational, and financial analytics, enabling organizations to improve decision-making, enhance patient care, and manage costs. The complexity of healthcare BI stems from stringent regulatory requirements, data governance, and the need for seamless integration across diverse systems. Successful implementation requires overcoming challenges related to HIPAA compliance, data silos, and clinician adoption. Open-source tools like Apache Superset are gaining traction due to their flexibility and reduced vendor lock-in. The choice of a BI platform hinges on factors such as integration capabilities, access control, deployment flexibility, and user-friendliness, ultimately facilitating a shift from intuition-based to evidence-based healthcare management.

Businesses are increasingly challenged by the overwhelming influx of data generated from various interactions, necessitating effective tools to harness this information for strategic decision-making. Business Intelligence (BI) reporting emerges as a crucial solution, transforming raw data into actionable insights through systematic data analytics, offering a competitive edge in the marketplace. BI reporting includes various forms such as self-service BI, dashboards, embedded reports, mobile BI, and static reports, each catering to specific business needs and enhancing operational efficiency, customer insights, and predictive analysis. By enabling improved data utilization, informed strategic decisions, and cost management, BI reporting becomes integral to modern business operations across diverse sectors like HR, marketing, and finance. Effective BI reporting requires understanding the audience, preparing clean data, ensuring flexibility and scalability, and promoting data literacy, while tools like Preset offer a user-friendly, cost-effective platform to democratize data analytics, fostering an inclusive approach to business strategy.

February 2026 was a significant month for the Apache Superset™ community, with 48 contributors merging 330 pull requests, impacting various components of the platform. Key enhancements included the introduction of Progressive Web App support, allowing Superset to function as a native desktop application, and the Global Task Framework, which standardized background task management. The dashboard experience was improved with an advanced auto-refresh system and chart error transparency. MCP AI integration was enhanced with safety and capability upgrades, while the Dataset Folders editor saw refinements in drag-and-drop management. Security was bolstered through a comprehensive audit addressing SQL injection and other vulnerabilities. The SQL Lab and data exploration tools received bug fixes and performance enhancements, and new database engine support was added for Apache Phoenix and IoTDB. February's updates also included significant test coverage improvements, dependency updates, and developer experience enhancements, with a notable contribution from first-time contributors.