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Connectors are where you set up everything Summation reads from. There are two kinds, on two tabs of the same page:
  • Data — warehouses, databases, object storage, and repositories. You pick which tables or files to bring in as datasets, and those datasets become available to Addison, artifacts, and your tables and data catalog.
  • Apps — third-party tools like Shopify, PostHog, or Tableau. Connecting one lets Addison work with that app directly in a chat.
The Connectors page titled 'Connectors — Create and manage data connections and app integrations', with a New connection button in the top-right and Data / Apps tabs below. A table lists connections by Name and Status, each with its source type beneath the name — bq_test (BigQuery), DEMO_WH (Snowflake), shortify-url (GitHub), Salesforce connection fresh (Salesforce), Shopify connection (Shopify), and NetSuite connection (NetSuite) — each with a 'Last updated' date and a status dot.

The Connectors page — your data connections and their status

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Terminology

Three terms get used throughout this section. They’re easy to mix up: You can have multiple connections of the same connector type — for example one Snowflake connection for production and another for staging.

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Add a data connection

Click New connection (top-right of /connectors) to start a three-step wizard.
1

Choose a data source

Pick a connector type. Snowflake, Postgres, and BigQuery are surfaced under Popular; everything else is under Other, and app integrations appear under Apps.
Step 1 of 3 of the New connection wizard, titled 'Choose a data source' with a progress indicator reading 1/3. A Popular row offers Snowflake (Data warehouse), Postgres (Database), and BigQuery (Data warehouse). An Other section lists S3 (File storage), GitHub (Repository), Databricks (Lakehouse), ClickHouse, MySQL, Oracle, MotherDuck, Redshift, GCS, SQL Server, REST API (HTTP endpoint), MongoDB (Document database), and Apache Iceberg (Lakehouse catalog). An Apps section below starts with Shopify, NetSuite, and Salesforce.

Step 1 — choose a data source

2

Enter connection details

Give the connection a Name — it’s the identifier used in SQL and the API, so it must start with a letter and contain only letters, numbers, hyphens, and underscores — plus an optional Description. Then fill in the connector’s own fields; View the setup guide links to that connector’s page below.Optionally turn on Scheduled snapshots here. Datasets inherit the connection’s schedule unless they override or disable it.Test connection before continuing. A pass means Summation could authenticate and reach your data; a failure shows the message the source returned.
Step 2 of 3 of the wizard, titled 'Connection details' (2/3). It has Name and optional Description fields, a 'Need help setting this up? View the setup guide' link, and a Snowflake section with an Authentication toggle between Password and Key pair, plus Account Identifier (myorg-myaccount), Warehouse (COMPUTE_WH), optional Role (ACCOUNTADMIN), Username, and Password fields. Below is a 'Scheduled snapshots' row with an Enabled checkbox and the note 'Datasets inherit this schedule unless they override or disable it.', and Test connection, Back, and Continue buttons with the hint 'Test your connection before continuing.'

Step 2 — connection details and credentials

3

Add datasets

Pick the tables, files, or repositories to expose. The browser is tailored to the connector — a database/schema/table tree for warehouses, a file browser for object storage, a repo picker for GitHub — with a tree on the left, search on the right, and a running count of what you’ve selected.Each selection becomes a dataset. Dataset names must be unique across your tenant; the wizard suggests a unique name when there’s a conflict.
An 'Add datasets to this connector' dialog with a checkbox tree on the left rooted at '/' showing DEMO_DB, SNOWFLAKE, SNOWFLAKE_LEARNING_DB, and SNOWFLAKE_SAMPLE_DATA, and a searchable list on the right showing the same four entries each tagged with a yellow DATABASE badge and a drill-in chevron, above a footer reading '0 datasets selected' with Cancel and Add dataset buttons.

Step 3 — pick the datasets to bring in

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Manage a connection

Click any row on the Data tab to open its detail page.
  • Configuration & stored secrets — the non-secret fields (project, host, account) and which secrets are stored. Secret values are never shown after save; they render as ****.
  • Test connection — re-check credentials and reachability at any time.
  • Edit — update credentials when they rotate, or change configuration like the warehouse, role, or region.
  • Disconnect — disconnect every dataset in this connection at once, leaving the definitions in place. Other connections aren’t affected, and you can reconnect later.
  • Delete — permanently remove the connection, its credentials, and all of its datasets.
The detail page for a connection named bq_test with a green status dot, a rename pencil, Test connection and Edit buttons, and a ⋮ menu. A 'Configuration & stored secrets' panel shows bigquery_project_id set to bigqueryproject-490617 and CON_BQ_TEST_BIGQUERY_SERVICE_ACCOUNT_JSON masked as asterisks. Below, Datasets and Refresh history tabs sit above a table of datasets — customer, lineitem, nation, orders, part, partsupp — each with its source path, a 'Daily at 4:45 pm' refresh with either a green check 'Last 20h ago' or a red 'Failed Aug 5', a 'Full refresh' strategy chip, and per-row refresh and ⋮ controls.

A connection's detail page

Connection statuses. The colored dot on each connection shows its current state:

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Datasets and refresh

A connection’s Datasets tab lists everything it brings in — the dataset name, its source reference, when it refreshes, how the last refresh went, and the sync Strategy. Use + Add dataset to bring in more, the per-row refresh button to sync one now, and the Refresh history tab to see past runs across the connection.Sync strategies. How each refresh updates the stored copy:Incremental strategies need a column to track progress by — typically an updated-at timestamp or an increasing id.

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Connect an app

The Apps tab lists app integrations grouped by category — AI, Analytics, Commerce, and more. Click + on an app to review what it does, then Connect; most apps hand off to that provider’s sign-in to authorize access. Once connected, Addison can work with the app directly in a chat.
The Apps tab of the Connectors page showing 'Available apps' grouped by category: AI with Exa ('Search and extract structured content from across the internet with Exa.'); Analytics with Domo, PostHog, and Tableau; and Commerce with Shopify. Each card has an icon, a description, and a + button to connect.

Available apps, by category

A connect dialog for Shopify headed 'Give Summation access to your Shopify connector', listing three points: 'Use Shopify in Addison — Manage Shopify products, orders, customers, inventory, and commerce workflows', 'Your data stays yours — Summation does not train models on your connector content. Addison uses this connection only to answer your requests', and 'You control access — Disconnect anytime from MCP settings'. A note reads 'Third-party connectors are not built or maintained by Summation. Review the permissions before connecting. Usage is subject to the Summation Privacy Policy.' above a Connect button.

Review an app before connecting

Connected apps move to a Connected section at the top of the tab, where you can view the connector’s details or Disconnect it. Don’t see the app you need? Request an app tells us what to add, and a custom MCP server URL connects one we don’t list.
Third-party apps aren’t built or maintained by Summation — review the permissions before connecting. Summation doesn’t train models on your connector content, and you can disconnect at any time.

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Supported data sources

Each guide documents the fields that connector’s form asks for, where to find the values, and what the common errors mean.

Snowflake

Cloud data warehouse

BigQuery

Google Cloud data warehouse

Databricks

SQL Warehouse, Spark Connect, or Delta Lake

Postgres

PostgreSQL and compatible databases

Redshift

Amazon Redshift data warehouse

MySQL

MySQL and compatible databases

SQL Server

Microsoft SQL Server and compatible databases

ClickHouse

Columnar OLAP database

MongoDB

Document database collections

Oracle

Oracle Database, including Autonomous DB

MotherDuck

Hosted DuckDB

REST API

JSON APIs over HTTP

S3

AWS object storage (Parquet, CSV, JSON)

Apache Iceberg

Glue-backed or S3 warehouse lakehouse tables

AWS Glue

S3-backed Iceberg, Parquet, and CSV tables

GCS

Google Cloud Storage

GitHub

Code repositories and issues
Don’t see what you need? Tell us — connectors are added regularly.

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Troubleshooting

  • Test connection fails immediately — double-check the host, port, and that your network or firewall allows traffic from Summation’s egress IPs.
  • Test connection succeeds but datasets fail to load — usually a permissions problem on the source side. Check that the user or role you provided has read access to the schemas and tables you want.
  • Secrets seem to disappear after editing — secret fields are write-only. They render blank after save; the stored value is still there until you overwrite it.
  • Dataset name conflicts — names must be unique across the tenant. Rename in the wizard before deploying.
  • A dataset shows a failed refresh — open the connection’s Refresh history for the run’s error, then re-run that dataset with its row refresh button.
If a problem persists, copy the error from the Test connection result and contact support.