Redshift Private Previewlink
The Fivetran Redshift connector allows you to sync tables from your Redshift dataset to your destination. We can sync any number of schemas and tables from within your Redshift database.
TIP: If you want to sync multiple Redshift databases, we recommend that you create a connector for each database.
Featureslink
Feature Name | Supported | Notes |
---|---|---|
Capture deletes | check | All tables and fields |
Custom data | check | All tables and fields |
Data blocking | check | Column level, table level, and schema level |
Column hashing | check | |
Re-sync | check | Connector and table level |
History | check | |
API configurable | check | API configuration |
Priority-first sync | ||
Fivetran data models | ||
Private networking | check | AWS PrivateLink |
Schema informationlink
Fivetran tries to replicate the exact schema and tables from your Redshift source dataset to your destination.
We name the source schema with the dataset name you provided in the connector setup form. We replicate the source's tables and views to the destination schema. We name the destination schema with the connector name appended by the dataset name. For example, if the connector name is redshift_db
and the dataset name is fivetran_schema
, the destination schema name is redshift_db_fivetran_schema
.
Type transformations and mappinglink
As we extract your data, we match Redshift data types to types that Fivetran supports. If we don't support a data type, we automatically change that type to the closest supported type or, in some cases, don't load that data at all. Our system automatically skips columns with data types that we don't accept or transform.
The following table illustrates how we transform your Redshift data types into Fivetran-supported types:
Data Type | Fivetran Type | Regular Column Support | Primary Key Support (Teleport) |
---|---|---|---|
SMALLINT | SHORT | Yes | Yes |
INTEGER | INT | Yes | Yes |
BIGINT | LONG | Yes | Yes |
DECIMAL | BIGDECIMAL | Yes | No |
REAL | BIGDECIMAL | Yes | No |
DOUBLE PRECISION | BIGDECIMAL | Yes | No |
BOOLEAN | BOOLEAN | Yes | No |
CHAR | STRING | Yes | Yes |
VARCHAR | STRING | Yes | Yes |
DATE | LOCALDATE | Yes | No |
TIMESTAMP | LOCALDATETIME | Yes | No |
TIMESTAMPTZ | LOCALDATETIME | Yes | No |
GEOGRAPHY | JSON | Yes | No |
GEOMETRY | JSON | Yes | No |
SUPER | STRING | Yes | No |
HLLSKETCH | N/A | No | No |
TIME | N/A | No | No |
TIMETZ | STRING | Yes | No |
VARBYTE | BINARY | Yes | No |
ARRAY | N/A | No | No |
STRUCT | N/A | No | No |
Updating datalink
Fivetran performs incremental updates of any new or modified data from your source database. We use Fivetran Teleport Sync, our proprietary database replication method, to fetch only the data that has changed since our last sync. Learn more in our Fivetran Teleport Sync documentation.
NOTE: Teleport captures deletes differently than log-based replication mechanisms. Learn more in our Changes to data between syncs documentation.
Automatic table re-syncslink
We do not support DDL operations.
Tables with a primary keylink
We merge changes to tables with primary keys into the corresponding tables in your destination:
- An INSERT in the source table generates a new row in the destination with
_fivetran_deleted
=FALSE
. - A DELETE in the source table updates the corresponding row in the destination with
_fivetran_deleted
=TRUE
. - An UPDATE in the source table updates the data in the corresponding row in the destination.
Tables without a primary keylink
Fivetran currently does not support Redshift tables without a primary key.