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Model-aware analysis for analytics teams

PostgreSQL preview

You built the models.
Now ask better questions.

Bring model definitions and dependencies into your analysis. Query the models you already build, ask Kole for help, and keep production dbt jobs where they are.

Explore dbt in SyneHQSee the preview

Syne Pro or an eligible trial. Keep your existing dbt builds.

PostgreSQL preview for Syne Pro or an eligible trial. GitHub and compilation runners are optional.

A closer look at dbt.

Product preview · Sample data
acme / analytics mainInteractive preview · GitHub example

Your GitHub project

Isolated compilation

Review in Syne

customer_revenue Compiled
target / compiled / customer_revenue.sql
select
  customer_id,
  sum(amount) as lifetime_revenue
from "analytics"."stg_orders"
group by customer_id

Source revision retained with this compilation

GitHub source. Model context. Human review.See the workflow

Why dbt

Business answers with model context.

01

Use what you already build.

Connect dbt Cloud metadata or import a manifest. Query built models in your PostgreSQL warehouse.

Keep your existing dbt environment

02

Give Kole the definitions.

Bring model descriptions, declared columns, and dependencies into the analysis with your team.

Answers grounded in model context

03

Inspect a change when needed.

Optionally connect GitHub and compile SQL on a hosted or team runner before reviewing the result.

Optional compilation, without deployment

Practice the underlying skill

Work through a SQL example.

Reliable models need an explicit grain. Use a small SQL exercise to see how pre-aggregation prevents a join from multiplying revenue.

Runs locally in DuckDB. Fictional data. No signup required.

Start with the models you already have

Connect dbt Cloud metadata or import a manifest, then select the PostgreSQL warehouse where your models are built. Explore their definitions and dependencies beside the rows you query. Your current builds and schedules stay in place.

Your models. Your warehouse.

Latest successful run

Read model metadata on refresh. Keep builds in dbt Cloud.

Model context + Kole

Understand dependencies. Write an aggregate. Inspect the SQL.

Your PostgreSQL data

Query built tables and views through your governed connection.

Read-only queries · masking · auditGitHub and runners optional for analysis

Go further when the project needs it

Optionally connect GitHub to inspect model changes and compile SQL on a hosted or team-managed runner. Compilation keeps the source revision with the artifact; it does not deploy models or start production builds.

Projects that need warehouse introspection or external packages can import a manifest from their existing dbt environment.

Read the connection guide or the team runner guide.

Before you get started

A few useful answers.

Talk through your use case
Do I need GitHub or a runner to get started?

No. Connect dbt Cloud metadata or import a manifest and select the PostgreSQL warehouse where your models are already built. GitHub and compilation runners are optional.

Does SyneHQ replace dbt Cloud or run my builds?

No. Builds and production schedules stay in your existing dbt environment. SyneHQ brings model metadata, warehouse queries, and Kole into the same workspace. A metadata refresh does not start a dbt job.

What does compilation include?

Offline PostgreSQL compilation produces SQL and model context for inspection. It does not build models, perform warehouse introspection, download packages, or certify tests. Import a manifest from your existing environment when your project needs those capabilities.

Which plan includes dbt?

dbt projects require Syne Pro or an eligible trial. The current preview supports PostgreSQL.

Your models already hold the context. Put it to use.

Connect dbt Cloud metadata or import a manifest to start analyzing your built models.

Explore dbt in SyneHQOr book a product walkthrough