Build, Query, Share: The New Era of Dashboards in Databricks

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Build, Query, Share: The New Era of Dashboards in Databricks

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Source: https://www.linkedin.com/pulse/build-query-share-new-era-dashboards-databricks-arabinda-mohapatra-knn6c?trackingId=xP9nN9ecSTuF1gxQ9dl6jQ%3D%3D

Build, Query, Share: The New Era of Dashboards in Databricks

Arabinda Mohapatra

Arabinda Mohapatra

Running Kafka streams after dark, diving into genetic code by daylight, and wrestling with Databricks and Tableflow in every spare moment—sleep is optional

Databricks just dropped something big: Databricks One—a unified workspace that finally bridges the gap between data engineers, analysts, and business users. No more toggling between notebooks, dashboards, and SQL endpoints. This is a single pane of glass for everyone.

🔍 What’s inside Databricks One?

• Genie (AI Assistant): Ask questions in plain English. “Show me Q3 sales by region” → done.

• Dashboards & Metrics: Track KPIs, trends, and anomalies without touching code.

• Databricks Apps: Prebuilt, customizable apps for everything from forecasting to churn analysis.

• Role-based UX: Consumers get simplicity, creators get power. No clutter, no confusion.

• Workspace Search: Find notebooks, dashboards, and assets instantly—finally, search that works.

• Your pipelines now have a front-end that business users can actually use.

• Genie reduces the load on analysts and engineers for ad hoc queries.

• Apps let you package logic + UI in one deployable unit—no more duct-taped dashboards.

• Role-based UX means fewer onboarding headaches and cleaner governance.


BNYC-Taxi-Dashboard:

  • We can create a dashboard by writting sql code
  • We can create the dashboard from uploading files(Supported file formats: .csv, .tsv, .tab, .json, .jsonl, .avro, .parquet, .txt, or .xml)
  • we can insert the parameter into sql when :param_rank_key = 'workspace' then 'rank_metadata'(:param_rank_key is the parameter)
  • Add the filter is possible
  • Add the title & small description is possible
  • You can share the dashboard is much more simpler
  • You can schuke to update the dashboard in frequency
  • You can add multiple dataset to the dashboard and write sql script to join and put into a cte within select statement
  • Can we

💡 Here’s what makes it powerful (and practical):

  • ✅ SQL-first dashboarding: Write pure SQL to build visualizations. No drag-and-drop fluff—just logic, control, and precision.
  • 📁 Upload & visualize files directly: Supports .csv, .tsv, .json, .parquet, .xml, .avro, and more. Instant ingestion → instant insights.
  • 🧠 Parameterized SQL: Use dynamic filters like :param_rank_key to drive logic. Example:

CASE WHEN :param_rank_key = 'workspace' THEN 'rank_metadata'

🔍 Filters, titles, and descriptions: Add context, interactivity, and clarity—without leaving SQL.

🔄 Scheduled refreshes: Automate updates at your preferred frequency. No manual reruns.

🔗 Easy sharing: One-click sharing with role-based access. No BI license drama. 🔗 Multi-dataset joins: Use CTEs to combine datasets, write modular SQL, and build layered insights.

📊 Log tables & validation views: Yes, you can store log/validation data in Delta tables and visualize them directly. Great for pipeline health, anomaly tracking, and audit trails.

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📊 Benchmarking implications:

• Faster feedback loops from business users → better pipeline tuning.

• Polars ETL + Databricks Apps = lightweight, high-performance delivery stack.

I’ll be sharing more insights as I explore further—stay tuned for deeper dives and real-world use cases!

Refer:https://docs.databricks.com/aws/en/dashboards/



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