Real-Time Operational Analytics with VeloDB
When an order is stuck, a payment changes state, or a device starts failing, the useful answer is the one an operations team can act on now. VeloDB brings operational changes into an analytical warehouse, keeps current-state records queryable, and lets teams filter, join, and aggregate fresh data with SQL.
Which problem are you trying to solve?
Is your dashboard showing stale data?
The source system has already changed, but the analytical copy still shows the old value. By the time the dashboard catches up, the moment to act may have passed.
Start with a full copy of the selected tables, then keep later inserts and updates synchronized. Analysts can work with fresh operational data without running reporting queries on the application database.
Do records keep changing while people query them?
Orders, accounts, and shipments are updated throughout the day. Teams still need to query the latest state while new versions continue to arrive.
The Unique Key model identifies a changing record by its business key. Merge-on-Write resolves new versions as data is written, so queries can work with the latest visible state.
Are the records you need buried in a much larger table?
An operations team may need only the orders in one region, one status, and one time window. Scanning a large table to return a small result wastes time and resources.
For selective predicates on large datasets, an inverted index can narrow the search to matching rows and reduce unnecessary scanning.
Does one answer depend on several tables?
Knowing that an order is late is not enough. The operations team may also need the merchant, customer, region, or support history before it can take action.
Synchronized tables can be filtered, joined, and aggregated with SQL in the same analytical warehouse. The result combines current operational state with the context needed to investigate it.
From a source change to an operational action
Each tutorial follows the same repeatable path. Existing rows are loaded once, later changes continue to arrive, and SQL turns the current data into an answer an operations team can use.
An order, payment, or device changes state.
Start with a full load, then capture later inserts and updates.
Fresh operational data remains available for analysis.
Filter, join, and aggregate the records that need attention.
Alert, follow up, or resolve the issue.
See the workflow in action
Order Fulfillment Monitoring
Find paid orders that have remained unshipped for too long, add the merchant details needed for follow-up, and see the result change after an order ships.
Use MySQL or PostgreSQL as the source database.
This is the first scenario in the collection. Additional scenarios will follow the same problem-to-solution format.
VeloDB is powered by Apache Doris, so some technical articles use the Apache Doris name when they explain the underlying engine. Benchmark results apply to the workloads described in those articles. These tutorials use synthetic data and are not performance benchmarks.