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Saiku Cloud

Sign up & first cube

This page is the long-form walkthrough of the Quickstart, with the why behind each step. If you just want to ship a cube, Quickstart is faster.

Sign up

  1. Visit cloud.saiku.bi/signup.
  2. Authenticate via WorkOS (Google or SAML). We don’t manage passwords ourselves; identity is delegated.
  3. After signup we provision four things for you:
    • Your tenant with a generated slug (something like acme-corp). The slug appears in your dashboard URL.
    • A default workspace to hold your schemas and workbooks.
    • Your user account as the tenant owner.
    • An initial API key so the dashboard can talk to the backend on your behalf. You can mint additional keys for agents and integrations on the API keys page later.

Connect your first warehouse

Saiku Cloud supports five dialects in v1: PostgreSQL, Snowflake, BigQuery, ClickHouse, MotherDuck. Add more by filing a feature request — each new dialect is one JDBC driver + a small classifier.

Host:       db.acme.com
Port:       5432
Database:   analytics
Username:   saiku_reader
Password:   ••••••••
SSL:        require (recommended)

Credentials are encrypted at rest. See Tenant isolation for the full explanation of how we handle the credentials you give us.

Why a cube?

Saiku speaks MDX over Mondrian XML schemas. A cube is a declarative model that says “here are my facts (a sales table), here are the dimensions I want to slice by (date, region, product), and here are the measures I want to aggregate (revenue, units)”. Once you’ve described that once, any OLAP-style query — drilldowns, pivots, hierarchies — works against it without writing SQL.

Saiku Cloud’s job is to make cube authoring not-a-chore. The Schema designer samples your warehouse and drafts the cube with AI assistance. You review and tweak; you don’t start from a blank schema file.

Draft → Save → Query

The full chain:

  1. Schema designer → Pick a connection. We profile the warehouse schema cheaply — read its metadata, sample a few rows per column.

  2. Pick a fact table. Aggregate tables (agg_*, *_summary, *_rollup) are pushed to the bottom so the canonical base-grain fact wins.

  3. Describe the cube in plain English (optional but recommended). “Sales facts joined to customer and product dimensions, count of orders and sum of revenue.” This goes to Claude alongside the profile.

  4. Review the proposal. The cube card shows measures + dimensions as editable chips. The join graph below renders foreign-key joins between fact and dimension tables — click an edge to change the join column.

  5. Render XML. The cube card produces Mondrian XML.

  6. Save. The schema lands in your workspace.

  7. Analyze. Sidebar → Analyze → Open in Saiku on the cube card. You land in the Saiku UI with the cube selected.