Cloud automations rarely live in isolation. One flow detects an issue, another enriches context, a third notifies the right team. Without a shared source of truth, you end up duplicating logic or hardcoding mappings that drift over time.

The Datastore node provides your CloudFlow automations with a shared, managed place to store and query structured data, enabling multiple flows to reference the same tables for enrichment, routing, and state. Think of Datastore as a lightweight database you can use directly from flows.

A common pattern is a tag ownership table:

• You maintain a table that maps tags (or tag patterns) to an owner, team, cost center, escalation channel, and metadata.
• Any flow can query it to resolve “who owns this?” consistently.

Example: an anomaly flow spots a sudden spike on an untagged workload. It looks up the resource’s tags, queries the ownership table, and then routes the alert to the correct Slack channel. A separate remediation flow uses the same table to decide who can approve a change and where to open a ticket.

In the Datastore node, you can:

• Get records using filters (including values from previous nodes)
• Insert records for new rows, including batch inserts
• Upsert records to keep a table current without duplicates by using a unique key column

Supported column types include Text, Integer, Numeric, Boolean, Date, Timestamp, and JSON.

How to get started

Create a Datastore table for shared mappings (for example, tag-to-owner), then reference it from any flow via a Datastore node query. Use Upsert when you want scheduled flows to keep the table continuously in sync from an authoritative source.