Tutorials on Query Builder

Analytics dashboards display different data visualizations to represent and convey data in ways that allow users to quickly digest and analyze information. Most multivariate datasets consumed by dashboards include a spatial field/s, such as an observation's set of coordinates (latitude and longitude). Plotting this data on a map visualization contextualizes the data within a real-world setting and sheds light on spatial patterns that would otherwise be hidden in the data. Particularly, seeing the distribution of your data across an area connects it to geographical features and area-specific data (i.e., neighborhood/community demographics) available from open data portals. The earliest example of this is the 1854 cholera visualization by John Snow , who marked cholera cases on a map of London's Soho and uncovered the source of the cholera outbreak by noticing a cluster of cases around a water pump. This discovery helped to correctly identify cholera as a waterborne disease and not as an airbourne disease. Ultimately, it changed how we think about disease transmission and the impact our surroundings and environment have on our health. If your data consists of spatial field/s, then you too can apply the simple technique of plotting markers on a map to extrapolate valuable insight from your own data. Map visualizations are eye-catching and take on many forms: heatmaps, choropleth maps, flow maps, spider maps, etc. Although colorful and aesthetically pleasing, these visualizations provide intuitive controls for users to navigate through their data with little effort. To create a map visualization, many popular libraries (e.g., Google Maps API and deck.gl ) support drawing shapes, adding markers and overlaying geospatial visualization layers on top of a set of base map tiles. Each layer generates a pre-defined visualization based on a collection of data. It associates each data point with certain attributes (color, size, etc.) and renders them on to a map.

From businesses looking to optimize their operations, data influences the decisions being made. For scientists looking to validate their hypotheses, data influences the conclusions being arrived at. Regardless, the sheer amount of data collected and harnessed from various sources presents the challenge of identifying rising trends and interesting patterns hidden within this data. If the data is stored within an SQL database, such as PostgreSQL , querying data with the expressive power of the SQL language unlocks the data's underlying value. Creating interfaces to fully leverage the constructs of SQL in analytics dashboards can be difficult if done from scratch. With a library like React Query Builder , which contains a query builder component for fetching and exploring rows of data with the exact same query and filter rules provided by the SQL language, we can develop flexible, customizable interfaces for users to easily access data from their databases. Although there are open source, administrative tools like pgAdmin , these tools cannot be integrated directly into a custom analytics dashboard (unless embedded within an iframe). Additionally, you would need to manage more user credentials and permissions, and these tools may be considered too overwhelming or technical for users who aren't concerned with advanced features, such as a procedural language debugger, and intricate back-end and database configurations. By default, the <QueryBuilder /> component from the React Query Builder library contains a minimal set of controls only for querying data with pre-defined rules. Once the requested data is queried, this data can then be summarized by rendering it within a data visualization, such as a table or a line graph.