Amazon Redshift IntegrationYour Redshift data is already there. KPI Tree turns it into understanding, ownership, and proven impact.
You chose Redshift because the economics made sense. KPI Tree keeps them that way. One query per metric, everything else runs off-warehouse. Fifty users or five hundred, your Redshift bill stays flat. What you get on top: cause-and-effect metric trees that map how your business actually works, RACI ownership at every node, personalised action plans for every team member, and statistical verification that actions moved the needle. Connect in under an hour.
Connected in under an hour
A guided setup wizard generates the exact SQL you need. No agents to install, no data extraction, and no changes to your VPC unless you want them.
Create a read-only user
The wizard generates copy-paste SQL that creates a dedicated Redshift user with SELECT-only access on the schemas you choose. By default the username is KPITREE and the wizard auto-generates a 25-character password. Grant access to exactly the tables your metrics need and nothing more.
Authenticate and connect
Provide your cluster endpoint, port (default 5439), and database name. The region is detected automatically from your endpoint. Choose password authentication with the credentials the wizard generated, or switch to IAM authentication for temporary credentials via get_cluster_credentials. The connection is validated in real time so you know it works before you leave the setup screen.
Import metrics and start acting on them
Running dbt on Redshift? One-click sync imports every metric, dimension, and time grain. Otherwise, define metrics directly with SQL against any table or view. From there, map how metrics drive each other, assign ownership, and start closing the gap between knowing what happened and doing something about it.
Deep integration with Redshift
KPI Tree connects to your provisioned cluster with the authentication method your team prefers. Your security model stays exactly as configured.
Two authentication paths, one setup wizard
Password authentication stores encrypted credentials with SSL set to "require" by default. IAM authentication uses get_cluster_credentials to issue temporary database credentials from your existing AWS identity setup. Optionally provide an IAM role, or supply access keys directly. Both paths go through the same guided wizard.
One query per metric, off-warehouse analytics
Each metric syncs with one query on a configurable schedule. Comparison periods, correlations, regressions, and outlier detection all run in KPI Tree's compute engine. User interactions like filtering, comparing, and drilling down never hit your warehouse. Your Redshift costs stay flat as your team grows.
Sync your dbt semantic layer in one click
If you run dbt on Redshift, every metric definition, dimension, and time grain syncs automatically. No re-definition, no configuration drift. Your dbt models stay the source of truth while KPI Tree adds ownership, causation, and action on top.
Cause-and-effect metric trees, not another dashboard.
Your Redshift warehouse holds every metric your business tracks. The problem was never data access. The problem is that nobody knows which metrics drive which outcomes, who owns what, or what to do when something moves. KPI Tree turns warehouse data into causal metric trees where every node has an owner, every shift triggers an alert to the right person, and every team member gets a personalised action plan. That is the difference between a data warehouse and a Data Engagement platform.
- Causal metric trees map how every metric drives your top-level outcomes
- RACI ownership at every node so accountability is never ambiguous
- Statistical monitoring alerts the right person when a metric shifts
- Personalised action plans reach team members who can influence the outcome
Password or IAM. Your security team picks, the wizard handles the rest.
The setup wizard supports two authentication methods. Password authentication creates a dedicated user (default username KPITREE) with a wizard-generated 25-character password, stored encrypted with SSL mode set to "require". IAM authentication calls get_cluster_credentials to issue temporary database credentials from your existing AWS identity configuration. You can authenticate with an IAM role, or provide an access key and secret directly. Both methods connect over port 5439 by default, and the region is auto-detected from your cluster endpoint.
- Password auth with auto-generated 25-character credentials and SSL "require"
- IAM auth via get_cluster_credentials for temporary credentials
- Optional IAM role or direct access key and secret
- Region auto-detected from your cluster endpoint
One query per metric. No additional queries for comparisons or aggregations.
Traditional BI tools fire a warehouse query for every dashboard load, filter change, and date shift. Your costs scale with your headcount. KPI Tree runs one scheduled sync per metric, regardless of how many people are looking at it. All analytics run off-warehouse. You do not need to add concurrency scaling or increase your cluster size to support more users. The typical result: more people engaging with data, lower compute spend.
- One query per metric on a configurable schedule
- All analytics computation runs off-warehouse in KPI Tree's engine
- No concurrency scaling needed to support additional users
- No additional warehouse queries for comparisons, aggregations, or user interactions
Your dbt investment keeps paying dividends.
If you have built a semantic layer in dbt on Redshift, that work carries straight into KPI Tree. One-click sync imports every metric, dimension, and time grain. Changes in dbt flow through automatically. KPI Tree adds the layers dbt does not cover: visual cause-and-effect trees, RACI ownership, statistical correlations, and personalised action plans delivered to every team member. Your dbt models stay the source of truth. KPI Tree makes sure people act on them.
- One-click sync imports all metrics, dimensions, and time grains
- Supports both dbt Cloud and dbt Core
- Changes in dbt sync automatically with no manual updates
- Works with any dbt project targeting Redshift
How KPI Tree uses Redshift differently
Most tools treat your warehouse as a compute engine that gets hammered with every user interaction. KPI Tree treats it as a data source: one query per metric on a schedule, everything else runs off-warehouse, so you can focus on what no dashboard can deliver.
From warehouse to Data Engagement platform
Other tools query Redshift every time someone opens a dashboard or changes a filter. KPI Tree syncs each metric once and computes correlations, comparisons, causality, and outlier detection in its own engine. Your warehouse bill stays flat while the number of people engaging with data grows.
Understand, act, prove impact
Dashboards answer "what happened?" and stop there. KPI Tree answers the harder questions: why did it happen, who should act, and did the action work? Causal trees, ownership, statistical monitoring, and proof-of-impact analysis turn your Redshift data into a system that drives behaviour, not just reports.
Redshift-native security, not workarounds
Password or IAM authentication. SSL required by default. Read-only grants on specific schemas. KPI Tree works with the security model you have already configured rather than asking you to create exceptions.
Related integrations
Other data sources that work with KPI Tree.
Common questions
- Your cluster endpoint (hostname), port (default 5439), and database name. The region is auto-detected from the endpoint. You then choose either password authentication or IAM authentication. SSL mode defaults to "require" on every connection.
- The setup wizard creates a dedicated Redshift user with a default username of KPITREE and auto-generates a 25-character password. Credentials are stored encrypted. You can customise the username and password if you prefer.
- KPI Tree uses the AWS get_cluster_credentials API to obtain temporary database credentials. This is for provisioned clusters. You can authenticate with an IAM role, or provide an AWS access key ID and secret access key directly. No long-lived database password is required.
- KPI Tree runs one scheduled SQL query per metric, regardless of how many users are active. All downstream analytics run off-warehouse in KPI Tree's engine. You do not need to add concurrency scaling or increase your cluster size to support KPI Tree users. Most teams see their per-user data cost drop because KPI Tree replaces dashboard queries with a single scheduled sync.
- A read-only user with SELECT access on the schemas containing your metric data. The setup wizard generates the exact SQL to create this user and apply the grants.
- Yes. One-click sync imports every metric definition, dimension, and time grain from your dbt semantic layer. Both dbt Cloud and dbt Core are supported. Changes in dbt flow through to KPI Tree automatically. See the dedicated dbt Cloud and dbt Core integration pages for details.
- BI tools give your team dashboards. KPI Tree gives them cause-and-effect metric trees with ownership at every node, statistical drivers identified automatically, and personalised action plans. Instead of asking "what happened?", your team starts asking "what should I do about it?" and "did my action work?". That is the difference between data access and Data Engagement.
- KPI Tree queries your warehouse on a schedule and processes aggregated results in its own engine. Raw row-level data is not persisted outside your environment. All Redshift security controls, including IAM policies and schema-level grants, remain fully enforced.
Related guides
Deep dives into the frameworks and metrics that work with Amazon Redshift.
Your Redshift data is ready. Make sure your team acts on it.
Connect Redshift to KPI Tree in under an hour. One query per metric, off-warehouse analytics, and a Data Engagement layer that turns your warehouse investment into action across every team.
