March 2023 Summaries

The Superset Community Newsletter, authored by Evan Rusackas and dated March 30, 2023, briefly encourages readers to explore Preset, a platform designed for data visualization, by offering a free trial. The newsletter also invites subscribers to receive weekly updates on new blog posts, promoting engagement with the community's ongoing discussions and developments.

Time series charts, traditionally used to illustrate how variables change over time, now include the flexibility to explore trends and patterns across non-temporal dimensions with the latest updates in Preset and Apache Superset. These updates, which introduce non-temporal X-axes for general availability, provide users with the ability to conduct analyses using bar charts, line charts, and other chart types on categorical data, expanding their utility beyond temporal data. The enhancements involve renaming existing chart types to better reflect their capabilities and refining the chart creation process with improvements in the drag-and-drop "Chart Builder" interface, which now allows for intuitive configuration of X-axis dimensions and time-based filters. Users can also explore charts in dashboards more effectively with the option to use dashboard filters or cross-filters to examine different segments of their visualizations, ensuring a seamless analytical experience. The documentation has been updated to guide users through these new features, and Preset's Starter plan offers free access to the hosted version of the open-source Superset, encouraging users to take advantage of these versatile charting capabilities.

Pipe Technologies, an alternative financing platform founded in 2019, serves over 23,000 companies and has connected more than $7 billion of annual recurring revenue to its platform. Initially using Looker and Redash for business intelligence (BI), Pipe faced challenges with maintaining a consistent semantic layer and scalability issues. Transitioning to Preset, powered by open-source Apache Superset, provided solutions with its dataset-centric approach and diverse visualization options, allowing dashboard filtering to be optimized for performance. Preset's SQL Lab facilitates visualization creation without a steep learning curve, enabling broad access across the organization. Approximately 40-50% of Pipe's workforce utilizes Preset monthly, empowering teams in marketing, finance, business operations, and product to generate and analyze their own data insights. Marketing benefits from bi-weekly go-to-market reviews, finance uses it for visualizing financial metrics, business operations optimize returns on assets, and product teams track user engagement. Pipe anticipates further integration with dbt’s semantic layer to enhance metric consumption within Preset.

The text provides a comprehensive guide on customizing chart colors in Apache Superset and its hosted version, Preset, to enhance dashboard visualizations. It explains the two main types of color palettes available—categorical and sequential—and how they can be applied to charts and dashboards for better interpretability and aesthetic appeal. The guide details the process of creating custom color palettes in open-source Superset through code modifications and in Preset using a point-and-click interface, emphasizing the importance of color consistency and accessibility. Additionally, it addresses the feature of defining custom series-specific colors for specific semantic meanings and the significance of ensuring accessibility in data visualization. The text concludes by encouraging users to check for further updates on enhancing Superset's color and styling capabilities.

Enabling self-serve analytics is essential for companies aiming to enhance data-driven decision-making, as it allows data consumers to conduct personalized analyses beyond pre-prepared charts and dashboards by centralized data teams. Cross-filtering is a feature that facilitates this by enabling users to apply ad-hoc filters directly on chart elements within dashboards, without needing to set up new filters each time. This capability, now more widely accessible in Apache Superset and Preset, empowers users with any workspace role, such as Viewer, to interact with and explore data more deeply. Previously, only chart builders could enable cross-filtering, limiting the ability of users with restricted permissions to self-explore. The recent update now allows cross-filtering on various chart types like ECharts and time series charts, with the flexibility to scope filters to specific visualizations or disable them when necessary. This feature is included in all Preset subscription plans, encouraging users to take advantage of self-serve analytics by creating and interacting with dashboards.

Superset 6.0 introduces a comprehensive theming system that enhances dashboard styling capabilities, offering features like design tokens and dark mode, which surpass previous CSS-only methods. While CSS remains an option for specific customizations, theming is recommended for most use cases to improve the effectiveness of dashboard consumption. The text navigates through various techniques for applying CSS to Superset dashboards, including specific selector strategies for styling individual charts, chart types, and dashboard components. It emphasizes the flexibility and risks involved in customizing dashboards with CSS, noting the potential for changes in Superset's structure to impact CSS selectors. The post also explores creative possibilities such as modifying chart backgrounds, fonts, and UI elements, while cautioning users about the fragility and limitations of such customizations. It highlights the open-source nature of Superset, encouraging community contributions to enhance styling capabilities and address common requests like implementing a dark mode and improving mobile responsiveness.

Data visualization is a crucial tool for transforming raw data into comprehensible and actionable insights, making it indispensable in the era of big data across various industries, including STEM, government, and business sectors. Through a variety of formats such as charts, graphs, and interactive dashboards, data visualization highlights patterns, trends, and outliers, facilitating quicker and more effective data comprehension and decision-making. This process not only enhances data accessibility and storytelling but also aids in strategic decision-making by providing a clearer understanding of complex datasets. Businesses, for instance, can leverage data visualization to improve productivity, understand customer behavior, and refine marketing strategies. Tools like Preset, built on Apache Superset™, allow users without extensive technical expertise to create intuitive and interactive visualizations, enabling deeper data insights through an easy-to-use interface without the necessity of coding. Effective data visualization practices emphasize audience consideration, appropriate visual selection, pattern highlighting, engaging design, and strategic text placement to ensure the most impactful presentation of data.