Data Warehouse connector

The Redshift data catalog your whole team can trust.

The Dawiso Redshift connector turns your cluster into a searchable data catalog: every database, schema, table, view and routine, with keys, constraints and object relationships mapped.

Read the docs

Live connector Stable connector

Redshift

Dawiso

Metadata-only · your data never leaves the source

Type: Cloud data warehouse
Auth: User + password (read-only group)
Sync: Scheduled, incremental
Direction: Read-only · metadata

First things first

What is a data connector?

Metadata-only Read-only access Incremental sync Cross-system lineage

A data connector is the bridge between a tool in your stack and the catalog that gives you a unified view of it. Once a connector is configured, it reaches into the source system on a schedule, reads out the metadata - schemas, tables, dashboards, jobs, ownership, lineage - and represents it inside the catalog. Your actual rows and values stay where they are.

Connectors are the reason a data catalog can answer questions like "which Power BI dashboard depends on this Snowflake table?" or "who owns the orders topic in Kafka?" - automatically, without anyone keeping a spreadsheet up to date.

Three properties separate a good connector from a brittle one: it should be read-only and safe, it should be incremental so a full re-scan isn't required for every refresh, and it should resolve lineage across system boundaries, not just inside one tool.

About the platform

What is Amazon Redshift?

Amazon Redshift is the cloud data warehouse from AWS. It uses massively parallel processing and columnar storage to keep analytical queries fast over petabyte-scale data. Retailers, fintechs and AWS-native teams use it as their analytical home base, often alongside S3, AWS Glue and QuickSight.

Glue Data Catalog covers what sits in S3 and Lake Formation. What it doesn't cover is the BI report consuming the Redshift table, the data product the business owns, and the policy security signed off on. That's where the Dawiso Redshift data catalog joins the picture: read-only, metadata-only, and cross-platform.

Architecture

How Dawiso connects to Redshift

A small read-only role on the Redshift side. The Dawiso scanner pulls metadata on a schedule. Everything ends up in your catalog, business-readable.

Source

Amazon Redshift cluster

Databases & schemas
Tables & external tables
Views & materialized views
Procedures & functions

REST · JDBC

Dawiso scanner

Read-only metadata

Schema & object discovery
Dependency resolution
SQL flow parsing (optional)
Sampling on opt-in

Internal

Catalog

Dawiso platform

Searchable metadata
Lineage & ownership
Business glossary
Policy & classifications

Connection details

Protocol: Redshift JDBC + INFORMATION_SCHEMA and pg_catalog SVV views
Authentication: User + password · dedicated read-only group
Lineage: Primary keys, foreign keys and constraints resolved from INFORMATION_SCHEMA and pg_catalog SVV views, mapping the relationships between tables, views and schemas

Setup

Connect Redshift in 4 steps

01

Create a read-only group

Run CREATE GROUP dawiso_integration_group, then a dedicated user inside that group. Dawiso ships the full GRANT script in the docs.
02

Grant metadata access

GRANT TEMP on the database, USAGE on information_schema and target schemas, SELECT on pg_catalog SVV views (svv_all_schemas, svv_table_info, svv_all_columns).
03

Connect in Dawiso

Provide the Redshift cluster endpoint (or DNS), port 5439, the user and password. Choose Shared or Private connection depending on network exposure.
04

Run ingestion

Scheduled incremental sync keeps databases, schemas and tables current. Repeat the GRANT script for every database and schema you want to ingest.

Capabilities

What you get with the Redshift connector

Schema & table catalog

Every Redshift database, schema, table, view and routine is searchable, with column descriptions, types and owners.
Keys & relationships

Primary keys, foreign keys and constraints are read from the catalog views, so the relationships between Redshift tables, views and schemas are mapped and searchable.
PII classification

Classify a column once. Dawiso flags every Redshift column carrying email, IBAN or government IDs across all databases and schemas.
Ownership & certification

Mark tables as certified, deprecated or under review. The owner is visible in the catalog and stays alongside the Redshift object.
External tables included

Spectrum external tables are catalogued next to native Redshift tables, so the lake side of your warehouse stays just as searchable.
GDPR & DORA evidence

Sensitive columns are tagged and the policy follows them through views and into downstream BI, with a full audit trail for regulators.

Business value

Why teams turn on the Redshift connector

-65%
Fewer 'which table?' pings

Analysts find the certified gold table in Dawiso instead of pinging the data team to ask which staging view maps to revenue.
10x
Faster impact analysis

Before altering a Redshift view, see exactly which tables, dashboards and ML features depend on it. Seconds, not days.
Read-only
Zero risk to production

Dawiso uses a dedicated group with SELECT on metadata views only. Your queries, your data and your write paths are untouched.

Ready to catalog your Redshift?

Set up the connector in an afternoon. See your first lineage graph the same day.

Read the docs

Frequently asked questions

Still curious? Talk to our team ->

What is the AWS data catalog?

AWS Glue Data Catalog stores table and schema metadata for S3 and Redshift Spectrum. Dawiso adds the layer Glue does not cover: BI reports, business glossary and policies, with keys, constraints and object relationships mapped across Redshift and the rest of your stack.

What is catalog in Redshift?

Redshift exposes system catalog views (pg_catalog and SVV views) describing databases, schemas and tables. Dawiso reads them read-only and turns them into a business-readable catalog with keys, constraints, ownership, classification and search.

What is a data catalog used for?

A data catalog makes every Redshift table discoverable, documented and trustworthy - who owns it, what it means and where it came from. Dawiso turns scattered schemas into one searchable catalog the whole business can use.

What permissions does Dawiso need in Redshift?

A dedicated dawiso_integration_group with TEMP on the database, USAGE on information_schema and target schemas, plus SELECT on pg_catalog SVV views (svv_all_schemas, svv_table_info, svv_all_columns, svv_external_tables, svv_redshift_databases) and pg_proc. Read-only end to end.

Does Dawiso copy our Redshift data?

No. Dawiso queries INFORMATION_SCHEMA and pg_catalog views for metadata only. Row-level data stays inside Redshift. Column profiling and sampling are opt-in per data source and never run automatically.

How does Dawiso map relationships between Redshift objects?

Dawiso reads primary keys, foreign keys and constraints from INFORMATION_SCHEMA and pg_catalog SVV views, then maps the relationships between tables, views and schemas so dependencies are searchable across the cluster.

Does it work with Redshift Serverless and private clusters?

Yes. Connection takes the cluster endpoint, IP or internal DNS name on port 5439. For clusters not exposed to the public internet, use the Private connection mode and route ingestion through the Dawiso Integration Runtime.