February 2026 Summaries

Enterprise teams utilizing Red Hat OpenShift can now incorporate Preset Certified Superset (PCS), a robust, QA-approved distribution of Apache Superset, to enhance their business intelligence capabilities within a secure and compliant container environment. OpenShift is favored by industries like finance, healthcare, and government, prioritizing security and compliance, making it crucial for BI tools to integrate seamlessly into existing infrastructures. PCS supports this need by offering validated container images, secured by non-root operation and strict Security Context Constraints, and is designed for easy deployment on OpenShift with resources like PostgreSQL and Redis for metadata storage and caching. Superset's extensive database connectivity, including integration with IBM Db2, allows teams to leverage existing data investments while providing a modern open-source alternative to legacy BI platforms such as Cognos. Through OpenShift's networking capabilities, PCS ensures secure and efficient BI operations, and its observability features offer comprehensive monitoring and logging. Moreover, PCS facilitates smooth migration from legacy systems, providing expert support and validated upgrade paths, making it an attractive option for organizations seeking a contemporary BI solution.

The Apache Superset Newsletter by Preset, dated February 2026, is a brief communication from the Preset Team inviting readers to explore visualization tools by trying Preset for free. It encourages subscriptions to their blog for weekly updates, aiming to keep the audience informed about new blog posts and developments related to Apache Superset.

Open source solutions like Apache Superset present a compelling case for embedded analytics, offering ownership, flexibility, and cost-effectiveness compared to proprietary BI tools. Superset's interoperability allows seamless integration with various databases and modern data stack tools, avoiding vendor lock-in and ensuring long-term data strategy control. Managed services like Preset combine open source benefits with operational ease, providing scalable, secure, and customizable analytics without the burden of infrastructure management. With transparent pricing and community support, open source ensures rapid innovation, especially as AI integrates more effectively with open codebases. As technology evolves, open source stands out as the foundation for modern embedded analytics, offering the best of both worlds through initiatives like Preset.

Healthcare analytics require unique considerations due to highly regulated, sensitive data critical to patient care, with open-source solutions like Apache Superset offering advantages over proprietary BI tools by ensuring transparency, flexibility, and compliance with regulations such as HIPAA. Open-source platforms like Preset provide auditability and security by allowing healthcare security teams to inspect code, and offer deployment flexibility to meet diverse data residency requirements, avoiding vendor lock-in. Preset's workspace architecture supports complex multi-tenancy needs in healthcare, ensuring isolated workspaces for different entities like hospitals and research groups, while offering predictable pricing and eliminating the risk of unexpected costs associated with proprietary tools. Additionally, Superset supports various healthcare use cases, including real-time patient monitoring and population health analytics, through its dataset-centric architecture and SQL compatibility, ensuring best-practice governance and security compliance with features like row-level security and audit logs. The open-source approach, supported by the Apache Software Foundation and Preset's managed services, provides healthcare organizations with the ability to maintain control over their analytics infrastructure, supporting long-term technology investments and accommodating changes in regulatory and organizational requirements.

Apache Superset™ is a popular open-source business intelligence platform that can be effectively managed in-house, but for organizations with high security and continuity needs, Preset Certified Superset (PCS) offers a compelling alternative. PCS is a refined distribution of Superset, backed by a team experienced in large-scale enterprise operations, designed to enhance security, reliability, and feature updates while allowing organizations to maintain control over their infrastructure. It addresses the challenges of open-source management, such as tracking vulnerabilities and managing updates, by providing regular, tested, and security-focused builds, which reduces operational risk and the burden on internal teams. PCS also offers deployment tools, expert support, and a structured path for feature development, enabling organizations to influence the Superset roadmap and quickly deliver critical features. While PCS introduces a commercial cost, it offsets hidden costs and risks inherent in managing open-source software by offering enterprise-grade backing and stability, making it an attractive option for large organizations using Apache Superset.

Apache Superset 6.0 introduces a comprehensive overhaul of its theming capabilities, transforming it into a versatile and adaptive platform for analytics that can seamlessly integrate into various environments. The update leverages Ant Design v5's advanced dynamic theming system, which allows for real-time theme switching, component-level customization, and algorithm-based theme generation, all of which enhance the user experience by enabling analytics to blend naturally into any application. This new system supports multiple theme strategies, including light/dark modes and dashboard-specific themes, and provides granular control over components using design tokens. The migration to this sophisticated theming framework simplifies what was previously a complex process, allowing for greater visual control and customization without deep technical knowledge. The update also introduces features like dynamic theme switching and a system-level theme architecture, enabling developers to deliver polished, on-brand analytics experiences that feel native rather than third-party.

FOSDEM, Europe's largest open-source conference, offers a unique volunteer-driven format that gathers developers and enthusiasts in Brussels to engage in open-source collaboration and innovation without the commercial aspects typical of such events. The author attended FOSDEM with the goals of promoting Apache Superset, learning from other communities, and networking with potential collaborators, presenting on self-maintaining documentation in the Tool the Docs developer room. The talk highlighted a shift towards using AI to automate documentation processes, ensuring that documentation evolves directly from the codebase and stays current without manual upkeep. Real-world applications included automating country mapping visualizations, feature flags, API documentation, database connections, and more, reducing maintenance burdens and improving accuracy. The broader implications for open-source projects were discussed, emphasizing the role of comprehensive documentation in enhancing AI training. The event fostered community connections, with discussions at the Maintainer Unconference focusing on reducing contributor burdens and the future of open source. Alongside attending meetups and connecting with the data community, the author left FOSDEM inspired by the momentum and potential of open-source innovation.

Metabase provides a comparison between itself and Superset, highlighting that Superset is aimed at technically savvy teams needing a wide range of visualization types. The main distinction is in scalability; Superset's dataset-centric architecture supports growth by centralizing business logic in a transformation layer, ensuring consistent metrics and seamless dashboard filters. This contrasts with Metabase's query-centric approach, which can lead to fragmented sources of truth and maintenance challenges as user numbers and dashboards increase. The text argues that while Metabase suits small-scale operations, Superset accommodates complex data analysis and scalability, offering features like a no-code Explore view and extensive API for broader adoption and programmatic control. Ultimately, the choice between the two tools hinges on whether an organization prioritizes immediate ease of use or long-term scalability and data access needs, with Superset positioned as a more sustainable solution for teams anticipating growth.

Apache Superset™ is a rapidly growing open-source analytics project known for its modern, lightweight, and extensible data exploration and visualization capabilities. It integrates well with various SQL engines and offers a wide range of visualization options, making it popular among startups, research teams, and data-savvy business units. However, as organizations scale their use of Superset, they encounter challenges related to security, scalability, and integration into enterprise systems. Preset addresses these challenges by providing a managed platform that enhances Superset with enterprise-grade features such as managed hosting, robust authentication, fine-grained security controls, and compliance certifications like SOC 2 and HIPAA. Preset offers flexible deployment models, including a fully hosted SaaS offering and a dedicated managed private cloud, allowing teams to evolve their analytics infrastructure as their requirements grow. It also provides centralized member and role management, audit logging, and enhanced API integrations to support governance and operational planning. Companies like Flywire have benefited from Preset's managed solutions, achieving cost savings and reducing engineering maintenance loads while ensuring scalable and secure analytics delivery. Preset transforms Superset from an open-source starting point into a comprehensive enterprise-ready analytics platform, offering the necessary scalability, security, and support for mission-critical workloads.

Apache Superset 6.0 marks a significant milestone with its release, showcasing a major visual and architectural transformation, highlighted by the migration to Ant Design v5, which introduces a token-based theming system and first-class dark mode support. This update, made possible by contributions from 155 contributors, including 101 first-time contributors, includes over 1,000 pull requests that enhance data exploration capabilities, improve performance, and introduce new features such as URL prefix support for flexible deployments, a new folder system for datasets, and advanced visualization options like an enhanced Table Chart powered by AG Grid. The release also features upgrades to Deck.gl visualizations, a new Gantt chart plugin, and improved filter experiences, providing a more intuitive and efficient user experience. Additionally, security and infrastructure updates include Python 3.12 support, expanded database support, and a transition from the sqlparse library to sqlglot, enhancing SQL analysis and manipulation capabilities. The Superset community is encouraged to participate in future developments and testing through various channels, as the platform continues to evolve with upcoming releases like Superset 6.0.1 and 6.1.0, focusing on bug fixes and new features, respectively.

January 2026 marked a highly productive month for Apache Superset, with significant advancements across various aspects of the platform. The month saw the merging of 344 pull requests from 72 contributors, including 31 first-time contributors, highlighting a vibrant and expanding community. Key updates included the launch of a revamped documentation system that automates the maintenance of documentation using built-in codebase metadata, and a new Developer Portal aimed at extension developers. Enhancements in SQL Lab included a redesigned UX with a virtualized treeview sidebar and a multi-stage progress bar for query tracking. Apache Superset expanded its database support to include MongoDB, offering new SQL query execution capabilities via the PyMongoSQL dialect. Additional features like dynamic currency formatting, improved chart timestamping, and a comprehensive overhaul of component documentation were introduced. Several performance improvements, security updates, and user experience enhancements, such as a redesigned dashboard filtering system and new chart customization options, were also implemented. The release underscores Apache Superset's commitment to evolving its open-source data visualization platform through community collaboration and innovation.

Preset is an analytics platform that emphasizes openness and flexibility, allowing users to integrate their existing tools and systems without forcing them into a specific data model or metrics layer. It supports a wide range of data warehouses and semantic layers, including Snowflake, BigQuery, and dbt, and offers deployment options that range from fully managed to self-hosted. Preset's design philosophy is centered around integration rather than isolation, enabling users to connect AI tools, orchestrate analytics workflows, and embed visualizations seamlessly into applications. As a central hub for modern analytics ecosystems, Preset facilitates coordination across fragmented stacks through APIs, automation, and intelligence, ensuring portability, transparency, and freedom in analytics processes. Built on open standards and community collaboration, Preset enhances the power of data stacks by providing the tools necessary for innovation without locking users into a single vendor's ecosystem.