Source connector

Redshift connector

Redshift integration for databases, warehouses and BI tools

Columnar cloud data warehouse on AWS. Datrise captures Redshift entities such as tables, rows, schemas, sort keys, distribution keys and query history, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.

The Amazon Redshift (JDBC/ODBC/libpq wire protocol) + Redshift Data API authenticates with username / password, service account, and Redshift caps calls at Redshift Data API calls are capped per account, e.g. ExecuteStatement at 30 transactions/sec, BatchExecuteStatement and GetStatementResult at 20 TPS, DescribeStatement at 100 TPS; direct SQL connections are instead bound by connection and concurrency quotas (2,000 max connections on RA3/RG nodes, 50 concurrency-scaling query slots across manual WLM queues).

Of the 6 objects, 5 sync as upserts keyed on the primary key and 1 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `redshift_tables`) with a schema derived from the Redshift API rather than a flattened CSV, in any of the 43 supported destinations.

Datrise is in early access: pick a destination below to open the Redshift pipeline page, and join the waitlist to get the Redshift connector with guided onboarding.

Incremental syncSchema-aware mapping43 destinations available

What Redshift data Datrise extracts

Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.

Object	Landing table	Sync mode
tables	redshift_tables	Upsert by primary key, incremental on updated-at
rows	redshift_rows	Upsert by primary key, incremental on updated-at
schemas	redshift_schemas	Upsert by primary key, incremental on updated-at
sort keys	redshift_sort_keys	Upsert by primary key, incremental on updated-at
distribution keys	redshift_distribution_keys	Upsert by primary key, incremental on updated-at
query history	redshift_query_history	Append-only, incremental by timestamp

Redshift API: setup facts for the connector

Verified against the vendor documentation on 2026-09-02. Unknown values are left blank rather than guessed.

API	Amazon Redshift (JDBC/ODBC/libpq wire protocol) + Redshift Data API
Authentication	username / password, service account — Clients connect with a native database user/password or with temporary IAM-generated credentials (`GetClusterCredentials` / Redshift Serverless workgroup role), and the separate Data API authenticates purely via IAM (SigV4), never a stored DB password.
Pagination	none — There is no API-level pagination; large reads are chunked by the query itself (`LIMIT`/`OFFSET`, a keyset predicate, or a server-side `DECLARE CURSOR`) or, for full extracts, by `UNLOAD` splitting output across one or more S3 files per node slice.
Rate limit	Redshift Data API calls are capped per account, e.g. ExecuteStatement at 30 transactions/sec, BatchExecuteStatement and GetStatementResult at 20 TPS, DescribeStatement at 100 TPS; direct SQL connections are instead bound by connection and concurrency quotas (2,000 max connections on RA3/RG nodes, 50 concurrency-scaling query slots across manual WLM queues)
Incremental cursor	Not published
Webhooks	No
Bulk export	Yes — UNLOAD to Amazon S3 (text, JSON, or Parquet; splits into files up to 6.2 GB each)
Deletes detectable	No

Official API reference

What bites when replicating Redshift

There is no native deleted-row feed: MVCC keeps deleted tuples until `VACUUM` reclaims them, but no system view exposes them as a change stream, so connectors must diff full tables or rely on a soft-delete column the source schema provides.
Without an `updated_at`/monotonic key on a table, there is no way to do incremental extraction at all; every pull is a full table scan.
`UNLOAD` rejects a `LIMIT` clause on the outer `SELECT` and caps each output file at 6.2 GB, so a naive `SELECT ... LIMIT n` export pattern has to be rewritten as a predicate-bounded query.
The Redshift Data API throttles `ExecuteStatement` at 30 TPS and `BatchExecuteStatement`/`GetStatementResult` at 20 TPS per account, well below what a direct JDBC/ODBC connection can sustain, so high-throughput extraction should use a direct SQL connection instead of the Data API.

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Popular destinations for Redshift

Choose a destination below to open the ready pipeline page.

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Redshift connector FAQ

What Redshift data does the connector extract?: 6 objects: tables, rows, schemas, sort keys, distribution keys and query history. Each lands in its own table with types preserved; history-style objects (1: query history) load as append-only streams.
Is the Redshift integration incremental?: Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
Where can I load Redshift data?: Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each Redshift → destination pair has its own page with the object-to-table mapping.

Related sources

Other sources in the same category, each with its own connector page.

Early access

Launch your Redshift pipeline with guided onboarding

Join the waitlist to get priority access for Redshift integrations with guided setup, schema-aware mapping, and production-grade incremental sync.