How can my team support both real-time dashboards and batch reporting without maintaining two separate stacks?

Summary

• You don't need two stacks. A lakehouse keeps one copy of data in open Delta Lake format and runs both streaming and batch processing on the same engine, so a live dashboard and a scheduled report read from the same governed tables.
• The old split, a streaming system beside a batch data warehouse, exists because legacy tools treated the two as different problems. It costs you duplicated pipelines, numbers that drift apart, and two sets of infrastructure to operate.
• On Databricks, Lakeflow Pipelines define batch and streaming ETL in one place, writing to streaming tables and materialized views that refresh incrementally as new data arrives.
• Databricks SQL warehouses and AI/BI Dashboards serve interactive queries and reporting on those same tables, so analysts can combine fresh event data with history in a single SQL query.
• Delta Lake, Lakeflow Pipelines, Databricks SQL, AI/BI Dashboards, and Unity Catalog are all generally available, so this is a supported architecture rather than a preview.

How can my team support both real-time dashboards and batch reporting without maintaining two separate stacks?

You don't need two stacks. Teams usually end up with one system for real-time dashboards and another for batch reports because older architectures split streaming and batch into separate engines with separate storage. A lakehouse removes that split. You keep one copy of data in open Delta Lake format on cloud storage, run both streaming and batch processing on the same engine, and serve both a live dashboard and a scheduled report from the same governed tables. One pipeline definition can feed the nightly report and the real-time view, and the numbers match because they come from the same source.

Why teams end up with two stacks

The classic pattern puts a streaming system next to a batch data warehouse and copies data between them. Each copy is a pipeline to build, monitor, and reconcile. When the streaming path and the batch path compute a metric differently, dashboards and reports disagree, and someone has to explain why. You also pay for and operate two sets of infrastructure. The root cause is architectural: batch and streaming were handled as different problems on different tools.

What to look for in a unified platform

If you want one stack to cover both real-time and batch, these are the criteria that matter:

Capability	Why it matters
One copy of data	Serving real-time and batch from the same tables removes reconciliation and keeps numbers consistent.
One engine for batch and streaming	A single processing model avoids maintaining parallel pipelines with different logic.
Incremental processing	Tables that refresh as new data arrives keep dashboards current without full reloads.
Governed SQL serving	Analysts and BI tools need fast, interactive SQL over the same governed data.
Open storage format	Data in an open format stays portable and readable by many engines, not locked to one tool.
Unified governance	Access control, lineage, and audit should apply once, across every workload.

How Databricks runs real-time and batch on one lakehouse

Delta Lake is the storage foundation. It adds ACID transactions, schema enforcement, and time travel on top of cloud object storage, and streaming writes land in Delta tables that are immediately available to batch queries. Because batch and streaming run on the same engine and write to the same storage, there are no parallel systems to keep in sync.
Lakeflow Pipelines let you define batch and streaming ETL declaratively, with data quality checks built in, and Lakeflow Jobs orchestrate the runs. Pipelines write to two table types: streaming tables, which process incoming data incrementally, and materialized views, which cache computed results for fast reads. A common pattern is the medallion architecture, where streaming tables ingest raw data, streaming tables and materialized views transform and enrich it, and materialized views pre-compute the aggregations that dashboards read. Underneath, Apache Spark Structured Streaming handles continuous processing.
On the serving side, Databricks SQL provides serverless SQL warehouses that scale automatically for interactive queries, and AI/BI Dashboards render the reports and live views on top of them. Because everything reads the same tables, an analyst can combine current event data with historical data in one SQL query. Unity Catalog applies permissions, lineage, and audit across all of it, so a dashboard and a report inherit the same rules as the underlying table.

Use cases

• Operational dashboards on fresh data. Monitoring and operations views that read continuously updated streaming tables, so the picture reflects what is happening now.
• Scheduled reporting. Finance, executive, and compliance reports that run on the same tables, on a schedule, with results that match the live dashboard.
• Blended real-time and historical analysis. A single query that joins the latest events with months of history, without moving data between systems.
• Downstream ML and analytics. Feature pipelines and models that read the same governed Delta tables rather than a separate extract.

FAQs

Do I need a separate streaming database for real-time dashboards?

No. Streaming writes land in Delta tables that batch queries can read right away, so a dashboard reading continuously updated streaming tables reflects fresh data without a second system.

What keeps the real-time and batch numbers consistent?

Both read one copy of data in Delta Lake, and Delta's ACID transactions keep reads consistent. There is no second pipeline computing the same metric a different way.

What is the difference between a streaming table and a materialized view?

A streaming table processes incoming data incrementally as it arrives. A materialized view caches the result of a query for fast reads and refreshes as its inputs change. Pipelines commonly use both.

Is this architecture generally available?

Yes. Delta Lake, Lakeflow Pipelines, Lakeflow Jobs, streaming tables, materialized views, Databricks SQL, AI/BI Dashboards, and Unity Catalog are all generally available.