Want to see your data come to life?
Begin building your dashboards now, and unleash your creativity!
Integration
Databricks
Turn Databricks Data into High-Performance Dashboards
Databricks brings data engineering, analytics, AI, and lakehouse workloads together on one platform. Dashtera helps you turn Databricks query results into fast, interactive dashboards built for large datasets, operational analytics, and high-density visual exploration.
Connect Dashtera to Databricks, query your lakehouse data, and visualize the results with high-performance charts, KPIs, tables, maps, time-series views, and dashboard panels — without building a custom visualization frontend.
Give Databricks Data a Fast Visual Frontend
Build dashboards from Databricks query results
Use Databricks SQL outputs as the foundation for interactive charts, KPIs, tables, maps, gauges, time-series views, and dashboard panels.
Visualize lakehouse data without manual exports
Move from query results to dashboards without repeatedly exporting data into CSV files, spreadsheets, or static reporting workflows.
Explore large analytical datasets visually
Use Dashtera’s high-performance visualization layer to work with dense data, large result sets, time-series records, and complex analytical views.
Connect data teams and business users
Let data engineers and analysts prepare the right Databricks queries while business, product, finance, and operations teams consume the results visually.
Build dashboards for real business workflows
Create dashboards for product analytics, operations, finance, SaaS metrics, customer behavior, infrastructure, IoT, and large-scale reporting.
Create a visual layer for governed data
Use Databricks as the trusted data platform, while Dashtera provides the frontend for visual analysis, communication, and decision-making.
How the integration works.
Dashtera’s Databricks connector lets users connect to Databricks, run SQL-based queries, and use the results inside dashboards. Databricks SQL supports warehouse-style analytics on lakehouse data, and Databricks documentation describes SQL Warehouses as compute resources used for running SQL commands on data objects in Databricks.
A typical Databricks connection may require details such as the workspace server hostname, SQL Warehouse HTTP path, authentication credentials or token, catalog, schema, and the required permissions for tables, views, or query access.
Once connected, Databricks query results can be mapped into Dashtera visualizations such as line charts, bar charts, KPI panels, tables, gauges, maps, and advanced analytical charts.
Teams can use Databricks tables directly, create reusable SQL views, or prepare dashboard-specific queries depending on the analytics workflow.
What users can do
Connect Databricks as a data source
Use Databricks as the backend for Dashtera dashboards.
Visualize SQL Warehouse query outputs
Turn Databricks SQL query results into interactive charts, KPIs, tables, maps, and dashboard panels.
Build dashboards from lakehouse data
Create visual analytics on top of Delta tables, curated datasets, reporting views, and analytical query results.
Support governed analytics workflows
Keep Databricks as the central platform for data access and governance, while Dashtera provides the visual experience for end users.
Share Databricks-backed dashboards
Make lakehouse insights easier to consume across business, analytics, product, operations, and technical teams.
Example Use Cases
A data team can use Databricks dashboards to turn curated lakehouse tables into visual analytics for the wider organization. Instead of serving every stakeholder through custom SQL exports or static reports, teams can prepare reusable queries and expose the results through interactive Dashtera dashboards.
A product or growth team can use Databricks-backed dashboards to analyze user behavior, activation, retention, subscriptions, cohorts, funnel steps, feature usage, or customer journeys. Databricks remains the analytical backend, while Dashtera provides the visual layer for exploring product performance and business impact.
An operations team can build dashboards around large-scale processes, service activity, logistics, workflow status, IoT-style data, monitoring signals, or internal platform metrics. Dashtera helps translate Databricks query results into operational views that are easier to review, filter, and act on.
For finance, BI, and leadership teams, Dashtera can turn Databricks data into executive dashboards for revenue, costs, customer segments, business KPIs, forecast inputs, regional performance, and reporting workflows. The lakehouse remains the source of analytical truth, while Dashtera makes the results easier to communicate.
For engineering and technical teams, Dashtera provides a faster way to create visualization-heavy internal tools on top of Databricks data. Teams can work with high-volume datasets, time-based records, telemetry, logs, or analytical models and turn them into smooth, interactive dashboards.
Real interactive demo projects buit in Dashtera with Databricks:Â Smart Grid Electricity Consumption and Load Forecasting Dashboard
Technical Details
Dashtera’s Databricks integration is built for teams that want to turn lakehouse query results into dashboard-ready visualizations.
Depending on your Databricks setup, users may need connection information such as workspace hostname, SQL Warehouse HTTP path, authentication token or credentials, catalog, schema, table access, query permissions, and network access from Dashtera to the Databricks environment [read more].
For Databricks dashboards, teams can use SQL queries, views, curated tables, or analytics-ready datasets depending on the use case. Views are especially useful when teams want to centralize joins, calculations, filters, business logic, or governed metric definitions before visualizing the data in Dashtera.
Dashtera is built to handle serious data volumes. Send your query results into Dashtera and let its high-performance visualization engine do the work — rendering large, complex datasets into smooth, interactive dashboards without the lag you expect from traditional BI tools.