February 2021 Summaries

Apache Superset, a rapidly growing open-source project, has adopted semantic versioning to support its continued development and maintain quality standards set by its recent 1.0 release. Semantic versioning is a widely used scheme in open-source software that uses a three-part version number (X.Y.Z) to denote major, minor, and patch updates, ensuring clarity regarding compatibility of newer versions with existing deployments. The decision to adopt this versioning system resulted from a collaborative process initiated by Preset's Elizabeth Thompson, who proposed a Superset Improvement Proposal (SIP) to facilitate community discussion and voting. This SIP process exemplifies the open and collaborative nature of Superset's development, enabling contributors to propose changes, discuss them on platforms like GitHub, and ultimately vote on them within the Apache Superset mailing list. The adoption of semantic versioning not only discourages breaking changes but also benefits users by providing clear information on version compatibility, encouraging community involvement in the project's evolution.

The article explores how Superset's geospatial data visualization capabilities can enhance understanding of the 2020 Presidential election results by analyzing popular vote distribution at the county level for Democrats and Republicans. Using publicly available data, the process involves importing election data into a Postgres database, enriching it with GeoJSON data corresponding to county FIPS codes, and visualizing it in Superset with deck.gl. The visualization highlights trends and patterns, showing that while a majority of the land appears red, blue areas represent more densely populated regions, offering an edge to the Democratic party. The methodology includes creating a lookup table for county FIPS and GeoJSON data and customizing visualization features to convey meaningful insights, such as displaying the number of votes as polygon height and color-coding to indicate winning parties. The article concludes by demonstrating the map's ability to provide a more nuanced view of election results, emphasizing the importance of population density in understanding electoral outcomes.

Data visualization is a crucial tool in various fields, including products, news, and politics, with creators having access to a wide range of tools and techniques. However, the abundance of choices can lead to decision fatigue, making it essential to select the right chart type for specific use cases. The blog post categorizes potential charts into time series, composition, distribution, relationship, and geospatial charts, each answering different data-related questions, such as how variables change over time, how parts fit into a whole, the common values and their proportions, the relationship between variables, and the geographical distribution of values. These categories, exemplified using Apache Superset, help users and viewers draw accurate conclusions from data by internalizing trends, patterns, variability, proportions, correlations, and geographic trends. The post emphasizes the importance of choosing the right visualization to enhance understanding and suggests that hands-on experience in crafting dashboards is the best way to improve visualization skills.

Preset Cloud Beta is now available as a fully managed, secure service for Apache Superset, offering an easy way to leverage its features without the complexity of self-managing an enterprise-ready cluster. Built on the open-source Superset platform, which is known for its robust data exploration and visualization capabilities, Preset Cloud simplifies deployment, scaling, and maintenance while providing enterprise-level security and compliance, such as role-based access control and SOC2 compliance. Users benefit from a hassle-free experience with automatic updates and configurations, alongside in-product tutorials and extensive documentation to facilitate onboarding and usage. Preset Cloud ensures freedom from vendor lock-in by maintaining compatibility with the open-source version, allowing seamless migration of workloads. Additionally, dedicated support and training from Preset's Superset experts enhance the user experience, making it a practical choice for organizations seeking a scalable, high-performance BI solution without the burdens of traditional BI software.