airbyte/docs/integrations/getting-started/destination-redshift.md

Getting Started: Destination Redshift

Requirements

1. Active Redshift cluster
2. Allow connections from Airbyte to your Redshift cluster (if they exist in separate VPCs)
3. A staging S3 bucket with credentials (for the COPY strategy).

Setup guide

1. Make sure your cluster is active and accessible from the machine running Airbyte

This is dependent on your networking setup. The easiest way to verify if Airbyte is able to connect to your Redshift cluster is via the check connection tool in the UI. You can check AWS Redshift documentation with a tutorial on how to properly configure your cluster's access here

2. Fill up connection info

Next is to provide the necessary information on how to connect to your cluster such as the host whcih is part of the connection string or Endpoint accessible here without the port and database name (it typically includes the cluster-id, region and end with .redshift.amazonaws.com).

You should have all the requirements needed to configure Redshift as a destination in the UI. You'll need the following information to configure the destination:

• Host
• Port
• Username
• Password
• Schema
• Database
  • This database needs to exist within the cluster provided.

2a. Fill up S3 info (for COPY strategy)

Provide the required S3 info.

• S3 Bucket Name
  • See this to create an S3 bucket.
• S3 Bucket Region
  • Place the S3 bucket and the Redshift cluster in the same region to save on networking costs.
• Access Key Id
  • See this on how to generate an access key.
  • We recommend creating an Airbyte-specific user. This user will require read and write permissions to objects in the staging bucket.
• Secret Access Key
  • Corresponding key to the above key id.
• Part Size
  • Affects the size limit of an individual Redshift table. Optional. Increase this if syncing tables larger than 100GB. Files are streamed to S3 in parts. This determines the size of each part, in MBs. As S3 has a limit of 10,000 parts per file, part size affects the table size. This is 10MB by default, resulting in a default table limit of 100GB. Note, a larger part size will result in larger memory requirements. A rule of thumb is to multiply the part size by 10 to get the memory requirement. Modify this with care.

Optional parameters:

• Bucket Path
  • The directory within the S3 bucket to place the staging data. For example, if you set this to yourFavoriteSubdirectory, staging data will be placed inside s3://yourBucket/yourFavoriteSubdirectory. If not provided, defaults to the root directory.

Notes about Redshift Naming Conventions

From Redshift Names & Identifiers:

Standard Identifiers

• Begin with an ASCII single-byte alphabetic character or underscore character, or a UTF-8 multibyte character two to four bytes long.
• Subsequent characters can be ASCII single-byte alphanumeric characters, underscores, or dollar signs, or UTF-8 multibyte characters two to four bytes long.
• Be between 1 and 127 bytes in length, not including quotation marks for delimited identifiers.
• Contain no quotation marks and no spaces.

Delimited Identifiers

Delimited identifiers (also known as quoted identifiers) begin and end with double quotation marks ("). If you use a delimited identifier, you must use the double quotation marks for every reference to that object. The identifier can contain any standard UTF-8 printable characters other than the double quotation mark itself. Therefore, you can create column or table names that include otherwise illegal characters, such as spaces or the percent symbol. ASCII letters in delimited identifiers are case-insensitive and are folded to lowercase. To use a double quotation mark in a string, you must precede it with another double quotation mark character.

Therefore, Airbyte Redshift destination will create tables and schemas using the Unquoted identifiers when possible or fallback to Quoted Identifiers if the names are containing special characters.

Data Size Limitations

Redshift specifies a maximum limit of 65535 bytes to store the raw JSON record data. Thus, when a row is too big to fit, the Redshift destination fails to load such data and currently ignores that record.

For more information, see the docs here.