Core concepts - Summand

Summand’s data model is small. Most of the product makes sense once you’ve internalized seven terms.

Connector

A pointer to where your data lives — a CSV upload, a database, a Fivetran-managed source, a Delta Sharing endpoint.

Dataset

A specific table within a connector, with a curated Parquet copy that everything else queries.

View

A saved SQL transformation over one or more datasets, with a visual builder and live Athena preview.

Experiment

A scheduled run of one or more components against a dataset or view. Cron-driven, versioned outputs.

Component

A unit of analysis — predictors, surprise finding, column stats, custom — with typed inputs and outputs.

Semantic layer

The bundle of artifacts each experiment run writes: column stats, model graphs, embeddings.

Sharing

Per-user grants plus visibility settings, enforced through OpenFGA on every read.

How they fit together

Connector (CSV / Postgres / MySQL / Snowflake / Azure SQL / Delta Sharing / Fivetran)
   └── Dataset (one per table, curated Parquet copy)
         │
         ├── Chat — Summand, grounded in the dataset
         │
         ├── View(s) — SQL transformations, optionally cross-dataset
         │      └── Chat or experiments can target a view instead of the dataset
         │
         └── Experiment(s) — scheduled component runs
                └── Components produce semantic-layer artifacts per run
                      └── Visible to chat, queryable downstream

A connector can have many datasets — one per table for databases. CSV connectors are always single-dataset. Datasets are the unit you chat about, query in views, and target with experiments.

Identifiers

Every resource has a stable, URL-safe ID:

Resource	Prefix	Example
Connector	`con_`	`con_01J5R...`
Dataset	`ds_`	`ds_01J5R...`
View	—	UUID-keyed under the user
Experiment	—	UUID-keyed
Run (component execution)	`run_`	`run_01J5R...`

IDs are immutable. Display names can change without invalidating shared links.

Lifecycle

Create connector — upload a CSV or point Summand at a data source.
Datasets land automatically — for CSVs, one dataset on upload; for databases, one per table you enable.
Pick an action:
- Chat with Summand for ad-hoc questions.
- Create a view to save a SQL transformation you’ll re-use.
- Create an experiment to schedule recurring analysis.
Read results — through chat, in the global Surprises page, or in a view that pulls experiment outputs.
Share — invite teammates with explicit grants, or change visibility.
Refresh — for live sources, click Refresh to re-read; experiments will re-run on their next schedule against the fresh data.

Documentation Index