Data Visualization

5 posts about Data Visualization. Every summary links to the original.

Databricks · Jun 17, 2026
Design Beautiful Dashboards in AI/BI
This guide presents dashboard design as a way to apply brand identity consistently while helping viewers interpret business data and trust the result. It recommends choosing a 3-, 4-, or 6-column subdivision of AI/BI’s 12-column grid according to audience needs, then using F- or Z-pattern hierarchy and unequal widget sizes to prioritize information. The visual system combines readable sans-serif or local fonts, high-contrast text, neutral backgrounds, a 60-30-10 color allocation, and distinct interactive and visualization palettes. For the Wanderbricks example, the final five-color palette is #15AFDD, #2375A8, #52A870, #C85070, and #C89930, tested in light and dark modes and with color-blindness simulations; workspace themes extend the resulting styling across dashboards.
Maggie Li, Mohit Hingorani
Databricks · Jun 11, 2026
Geospatial Unbounded: Spatial SQL GA with AI/BI Maps, Delta Sharing, and Iceberg v3
Databricks announces Spatial SQL as Generally Available, positioning its platform for geospatial analysis without separate spatial databases, warehouses, and mapping tools. It supports native GEOMETRY columns in Delta or Iceberg, more than 90 OGC-compliant ST_* functions, spatial joins, and boolean set operations. AI/BI dashboards can render Geometry and Geography columns as maps, while Genie can generate spatial queries and dashboards and respect Unity Catalog row filters. Geo columns are supported by Delta Sharing, and Databricks can read and write managed Iceberg tables or read externally written Iceberg tables with geospatial types in Iceberg v3. Benchmarks show eight of twelve SpatialBench queries improved since Public Preview, with gains from 20% to 15X, while areal boolean operations are twice as fast on average versus prior versions.
Kent Marten
Databricks · Apr 23, 2026
Guide to BI Reporting and Maximizing Intelligence Effectiveness
Business intelligence (BI) reporting turns data from operational systems and analytical repositories into structured reports, dashboards, and visualizations for organizational decision-making. The guide explains managed and ad hoc reporting, then traces a typical workflow through data sources, ETL, deduplication, normalization, validation, data modeling, visualization, and automated distribution. It compares established platforms such as Microsoft Power BI, Tableau, SAP BusinessObjects, Qlik, and Looker with newer AI-native tools, while noting evaluation criteria including connectivity, scale, governance, usability, and cost. It also addresses dashboard overload, stale semantic models, and specialist bottlenecks, and cites reports of 10x faster query creation from AI-assisted natural-language querying alongside recommendations to start with one decision, one primary source, and three to five KPIs.
Databricks Staff
Shopify · Apr 28, 2021
A Five-Step Guide for Conducting Exploratory Data Analysis
Exploratory data analysis (EDA) is presented as a practical way to describe a dataset, uncover patterns such as missing data, correlations, and outliers, and generate hypotheses that inform business questions and later data products. The guide recommends first clarifying the intended task with leadership and product, then checking dataset size, sample rows, and the unique row identifier; its synthetic merchant-behaviour example has 1,500 samples, 13 features, and one row per shop per day. It then advises examining missingness by feature, investigating why values are absent before deleting or imputing them, and classifying features as continuous, discrete, or categorical to guide analysis. Visualizing distributions and relationships helps reveal changes across samples and time, while box plots and percentile thresholds support systematic outlier review. The process is framed as a starting point whose findings can support regression, classification, or KPI-dashboard decisions.
2023-10-18
Shopify · Jan 28, 2021
How to Make Dashboards Using a Product Thinking Approach
The guide presents dashboards as automatically updated collections of visualisations or metrics for monitoring business questions and argues they should be built with a product-thinking approach. It first recommends deciding whether a dashboard is appropriate, then defining one clear goal, a specific audience, and metrics that reflect their needs before sketching the layout with stakeholders. Implementation guidance covers readable and efficient queries, version control, peer review, visual hierarchy, restrained content, business and data context, refresh frequency, technical reviews, and user sign-off. After launch, teams should market the dashboard, make it discoverable, monitor usage and outcomes, iterate when it falls short, and assign ownership for maintenance. The intended outcome is a dashboard that stays useful and delivers business impact by keeping users front and centre.
2023-10-18

This guide presents dashboard design as a way to apply brand identity consistently while helping viewers interpret business data and trust the result. It recommends choosing a 3-, 4-, or 6-column subdivision of AI/BI’s 12-column grid according to audience needs, then using F- or Z-pattern hierarchy and unequal widget sizes to prioritize information. The visual system combines readable sans-serif or local fonts, high-contrast text, neutral backgrounds, a 60-30-10 color allocation, and distinct interactive and visualization palettes. For the Wanderbricks example, the final five-color palette is #15AFDD, #2375A8, #52A870, #C85070, and #C89930, tested in light and dark modes and with color-blindness simulations; workspace themes extend the resulting styling across dashboards.

Maggie Li, Mohit Hingorani

Geospatial Unbounded: Spatial SQL GA with AI/BI Maps, Delta Sharing, and Iceberg v3

Databricks announces Spatial SQL as Generally Available, positioning its platform for geospatial analysis without separate spatial databases, warehouses, and mapping tools. It supports native GEOMETRY columns in Delta or Iceberg, more than 90 OGC-compliant ST_* functions, spatial joins, and boolean set operations. AI/BI dashboards can render Geometry and Geography columns as maps, while Genie can generate spatial queries and dashboards and respect Unity Catalog row filters. Geo columns are supported by Delta Sharing, and Databricks can read and write managed Iceberg tables or read externally written Iceberg tables with geospatial types in Iceberg v3. Benchmarks show eight of twelve SpatialBench queries improved since Public Preview, with gains from 20% to 15X, while areal boolean operations are twice as fast on average versus prior versions.

Kent Marten

Guide to BI Reporting and Maximizing Intelligence Effectiveness

Business intelligence (BI) reporting turns data from operational systems and analytical repositories into structured reports, dashboards, and visualizations for organizational decision-making. The guide explains managed and ad hoc reporting, then traces a typical workflow through data sources, ETL, deduplication, normalization, validation, data modeling, visualization, and automated distribution. It compares established platforms such as Microsoft Power BI, Tableau, SAP BusinessObjects, Qlik, and Looker with newer AI-native tools, while noting evaluation criteria including connectivity, scale, governance, usability, and cost. It also addresses dashboard overload, stale semantic models, and specialist bottlenecks, and cites reports of 10x faster query creation from AI-assisted natural-language querying alongside recommendations to start with one decision, one primary source, and three to five KPIs.

Databricks Staff

A Five-Step Guide for Conducting Exploratory Data Analysis

Exploratory data analysis (EDA) is presented as a practical way to describe a dataset, uncover patterns such as missing data, correlations, and outliers, and generate hypotheses that inform business questions and later data products. The guide recommends first clarifying the intended task with leadership and product, then checking dataset size, sample rows, and the unique row identifier; its synthetic merchant-behaviour example has 1,500 samples, 13 features, and one row per shop per day. It then advises examining missingness by feature, investigating why values are absent before deleting or imputing them, and classifying features as continuous, discrete, or categorical to guide analysis. Visualizing distributions and relationships helps reveal changes across samples and time, while box plots and percentile thresholds support systematic outlier review. The process is framed as a starting point whose findings can support regression, classification, or KPI-dashboard decisions.

2023-10-18

How to Make Dashboards Using a Product Thinking Approach

The guide presents dashboards as automatically updated collections of visualisations or metrics for monitoring business questions and argues they should be built with a product-thinking approach. It first recommends deciding whether a dashboard is appropriate, then defining one clear goal, a specific audience, and metrics that reflect their needs before sketching the layout with stakeholders. Implementation guidance covers readable and efficient queries, version control, peer review, visual hierarchy, restrained content, business and data context, refresh frequency, technical reviews, and user sign-off. After launch, teams should market the dashboard, make it discoverable, monitor usage and outcomes, iterate when it falls short, and assign ownership for maintenance. The intended outcome is a dashboard that stays useful and delivers business impact by keeping users front and centre.

2023-10-18