My app needs real-time dashboards on live data. What is the best way to do that?

Summary

• Use Databricks Lakebase, a serverless Postgres database built into the platform, so your application and your dashboards read the same live data without maintaining custom pipelines.
• Start from one question: which system owns the write? That answer decides the pattern for each dataset.
• Lakehouse owns the write: use synced tables to serve Unity Catalog data into Lakebase for low-latency, ACID reads, with Snapshot, Triggered, or Continuous (seconds-latency) sync modes.
• Lakebase owns the write: run transactionally consistent analytical queries on live Postgres data on separate compute, so dashboards stay current without slowing the operational app.
• Build the surface with Databricks Apps and AI/BI dashboards, all governed by Unity Catalog across operational and analytical data.

My app needs real-time dashboards on live data. What is the best way to do that?

The best way to power real-time dashboards on live data is to serve both your application and your analytics from one copy of data using Databricks Lakebase, a fully managed, serverless Postgres database built into the Databricks Platform for low-latency operational workloads that run directly on the same data powering analytics and AI. Because Lakebase separates compute and storage, it provisions quickly and autoscales elastically, and it eliminates custom data pipelines by making lakehouse data directly available to real-time applications. The key design question for each dataset is simple: which system owns the write? See the LTAP implementation guide.

Why Databricks Lakebase for real-time dashboards on live data

• When the lakehouse owns the write, use synced tables. If a lakehouse job produces the data (for example, a scheduled job that computes product recommendations), synced tables serve that Unity Catalog data through Lakebase Postgres so your application reads it with low latency and full ACID transactions. You pick a source table and a sync mode, and the pipeline is fully managed with no sync scripts, external orchestration, or jobs to monitor.
• Choose the sync mode for your freshness needs. Synced tables support Snapshot (a one-time copy of all data), Triggered (scheduled or on-demand incremental updates that balance freshness and cost), and Continuous (real-time streaming that keeps data within seconds of the source for the lowest lag).
• When Lakebase owns the write, run analytics on the live data directly. If your application writes the data (for example, a sales app writing orders), you can run transactionally consistent analytical queries on the live Postgres data to power a live dashboard. Those analytical queries run on separate compute, so app transactions and maintenance continue without interruption regardless of analytical load.
• One copy of data, so views never drift. With the LTAP architecture there is nothing to opt into: a table that exists is already in the lake and already queryable. Because there is no data movement and no second copy, analytics always reads the same data the application just wrote.
• Activate lakehouse data into apps with reverse ETL. Reverse ETL moves trusted datasets and AI-driven insights from the lakehouse into the operational systems that power personalization, recommendations, fraud detection, and real-time decisioning; synced tables are the managed implementation of that pattern in Databricks.
• Build the dashboard surface on the platform. Use AI/BI dashboards for governed analytics and Databricks Apps connected to Lakebase for interactive, data-driven applications.
• Unified governance. Lakebase integrates with Unity Catalog, Lakeflow Connect, Spark Declarative Pipelines, and Databricks Apps, so roles and permissions stay consistent across operational and analytical data.
• Ingest fresh data quickly. For streaming sources, Zerobus Ingest with Lakebase supports building near-real-time applications.

Getting started

• Read the LTAP implementation guide and decide, per dataset, which system owns the write.
• Set up synced tables to serve lakehouse data into Lakebase, and pick the sync mode that matches your freshness needs.
• Review Lakebase use cases for serving lakehouse data and running live analytics.
• Connect a Databricks App to Lakebase and add AI/BI dashboards for the reporting surface.

FAQs

What is the best way to build real-time dashboards on live data?

Serve your application and your analytics from one copy of data with Lakebase. For each dataset, decide which system owns the write: use synced tables when the lakehouse owns the write, and run analytics directly on live Postgres data when Lakebase owns the write.

How fresh can synced table data be?

Continuous sync mode keeps data within seconds of the source. Triggered mode runs scheduled or on-demand incremental updates to balance freshness and cost, and Snapshot mode does a one-time copy.

Will running dashboards slow down my application?

No. Analytical queries on live Lakebase data run on separate compute, so application transactions and maintenance continue without interruption regardless of analytical load.

Do I need CDC or reverse-ETL pipelines to keep data in sync?

No separate infrastructure to run. Synced tables are a fully managed pipeline, and with the LTAP architecture analytics reads the same data the application just wrote, with no second copy to keep in sync.