Migrating data from Amazon Redshift

Overview

This document describes the process of migrating data from Amazon Redshift to BigQuery through public IPs.

If you'd like to transfer data from your Redshift instance through a virtual private cloud (VPC), on private IP addresses, see Migrating Amazon Redshift data with VPC.

The BigQuery Data Transfer Service allows you to copy your data from an Amazon Redshift data warehouse to BigQuery. The service will engage migration agents in GKE and trigger an unload operation from Amazon Redshift to a staging area in an Amazon S3 bucket. Then the BigQuery Data Transfer Service transfers your data from the Amazon S3 bucket to BigQuery.

This diagram shows the overall flow of data between an Amazon Redshift data warehouse and BigQuery during a migration.

Before you begin

This section outlines the step-by-step process of setting up a data migration from Amazon Redshift to BigQuery. The steps are:

Google Cloud requirements: meet the prerequisites and set permissions on Google Cloud.
Grant access to your Amazon Redshift cluster.
Grant access to your Amazon S3 bucket you'll use to temporarily stage data. Take note of the access key pair, for use in a later step.
Set up the migration with the BigQuery Data Transfer Service. You will need:
- The Amazon Redshift JDBC url. Follow these instructions to obtain the JDBC url.
- The username and password of your Amazon Redshift database.
- The AWS access key pair you will obtain from the step: Grant access to your S3 bucket.
- The URI of the Amazon S3 bucket. We recommend that you set up a Lifecycle policy for this bucket to avoid unnecessary charges. The recommended expiration time is 24 hours to allow sufficient time to transfer all data to BigQuery.

Required permissions

Before creating an Amazon Redshift transfer:

Ensure that the person creating the transfer has the following required permissions in BigQuery:
- bigquery.transfers.update permissions to create the transfer
- bigquery.datasets.update permissions on the target dataset
The bigquery.admin predefined IAM role includes bigquery.transfers.update and bigquery.datasets.update permissions. For more information on IAM roles in BigQuery Data Transfer Service, see Access control reference.
Consult the documentation for Amazon S3 to ensure you have configured any permissions necessary to enable the transfer. At a minimum, the Amazon S3 source data must have the AWS managed policy AmazonS3ReadOnlyAccess applied to it.

Google Cloud requirements

To ensure a successful Amazon Redshift data warehouse migration, make sure you have met the following prerequisites on Google Cloud.

Choose or create a Google Cloud project to store your migration data.
- In the Cloud Console, go to the project selector page.
  
  Go to the project selector page
- Select or create a Cloud project.
  
  Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.
Enable the BigQuery Data Transfer Service API.

In the Google Cloud Console, click the Enable button on the BigQuery Data Transfer Service API page.

BigQuery is automatically enabled in new projects. For an existing project, you may need to enable the BigQuery API. A green checkmark indicates that you've already enabled the API.
Create a BigQuery dataset to store your data. You do not need to create any tables.
Allow pop-ups in your browser from bigquery.cloud.google.com so that you can view the permissions window when you set up the transfer. You must allow the BigQuery Data Transfer Service permission to manage your transfer.

Grant access to your Amazon Redshift cluster

Follow the instructions from Amazon to allowlist the following IP addresses. You can allowlist the IP addresses that correspond to your dataset's location, or you can allowlist all of the IP addresses in the table below. These Google-owned IP addresses are reserved for Amazon Redshift data migrations.

Regional locations

	Region description	Region name	IP addresses
Americas
	Las Vegas	`us-west4`	34.125.53.201 34.125.69.174 34.125.159.85 34.125.152.1 34.125.195.166 34.125.50.249 34.125.68.55 34.125.91.116
	Los Angeles	`us-west2`	35.236.59.167 34.94.132.139 34.94.207.21 34.94.81.187 34.94.88.122 35.235.101.187 34.94.238.66 34.94.195.77
	Montréal	`northamerica-northeast1`	34.95.20.253 35.203.31.219 34.95.22.233 34.95.27.99 35.203.12.23 35.203.39.46 35.203.116.49 35.203.104.223
	Northern Virginia	`us-east4`	35.245.95.250 35.245.126.228 35.236.225.172 35.245.86.140 35.199.31.35 35.199.19.115 35.230.167.48 35.245.128.132 35.245.111.126 35.236.209.21
	Oregon	`us-west1`	35.197.117.207 35.197.117.207 35.197.86.233 34.82.155.140 35.247.28.48 35.247.31.246 35.247.106.13 34.105.85.54
	Salt Lake City	`us-west3`	34.106.37.58 34.106.85.113 34.106.28.153 34.106.64.121 34.106.246.131 34.106.56.150 34.106.41.31 34.106.182.92
	São Paolo	`southamerica-east1`	35.199.88.228 34.95.169.140 35.198.53.30 34.95.144.215 35.247.250.120 35.247.255.158 34.95.231.121 35.198.8.157
	South Carolina	`us-east1`	35.196.207.183 35.237.231.98 104.196.102.222 35.231.13.201 34.75.129.215 34.75.127.9 35.229.36.137 35.237.91.139
Europe
	Belgium	`europe-west1`	35.240.36.149 35.205.171.56 34.76.234.4 35.205.38.234 34.77.237.73 35.195.107.238 35.195.52.87 34.76.102.189
	Finland	`europe-north1`	35.228.35.94 35.228.183.156 35.228.211.18 35.228.146.84 35.228.103.114 35.228.53.184 35.228.203.85 35.228.183.138
	Frankfurt	`europe-west3`	35.246.153.144 35.198.80.78 35.246.181.106 35.246.211.135 34.89.165.108 35.198.68.187 35.242.223.6 34.89.137.180
	London	`europe-west2`	35.189.119.113 35.189.101.107 35.189.69.131 35.197.205.93 35.189.121.178 35.189.121.41 35.189.85.30 35.197.195.192
	Netherlands	`europe-west4`	35.204.237.173 35.204.18.163 34.91.86.224 34.90.184.136 34.91.115.67 34.90.218.6 34.91.147.143 34.91.253.1
	Zürich	`europe-west6`	34.65.205.160 34.65.121.140 34.65.196.143 34.65.9.133 34.65.156.193 34.65.216.124 34.65.233.83 34.65.168.250
Asia Pacific
	Hong Kong	`asia-east2`	34.92.245.180 35.241.116.105 35.220.240.216 35.220.188.244 34.92.196.78 34.92.165.209 35.220.193.228 34.96.153.178
	Jakarta	`asia-southeast2`	34.101.79.105 34.101.129.32 34.101.244.197 34.101.100.180 34.101.109.205 34.101.185.189 34.101.179.27 34.101.197.251
	Mumbai	`asia-south1`	34.93.67.112 35.244.0.1 35.200.245.13 35.200.203.161 34.93.209.130 34.93.120.224 35.244.10.12 35.200.186.100
	Osaka	`asia-northeast2`	34.97.94.51 34.97.118.176 34.97.63.76 34.97.159.156 34.97.113.218 34.97.4.108 34.97.119.140 34.97.30.191
	Seoul	`asia-northeast3`	34.64.152.215 34.64.140.241 34.64.133.199 34.64.174.192 34.64.145.219 34.64.136.56 34.64.247.158 34.64.135.220
	Singapore	`asia-southeast1`	34.87.12.235 34.87.63.5 34.87.91.51 35.198.197.191 35.240.253.175 35.247.165.193 35.247.181.82 35.247.189.103
	Sydney	`australia-southeast1`	35.189.33.150 35.189.38.5 35.189.29.88 35.189.22.179 35.189.20.163 35.189.29.83 35.189.31.141 35.189.14.219
	Taiwan	`asia-east1`	35.221.201.20 35.194.177.253 34.80.17.79 34.80.178.20 34.80.174.198 35.201.132.11 35.201.223.177 35.229.251.28 35.185.155.147 35.194.232.172
	Tokyo	`asia-northeast1`	34.85.11.246 34.85.30.58 34.85.8.125 34.85.38.59 34.85.31.67 34.85.36.143 34.85.32.222 34.85.18.128 34.85.23.202 34.85.35.192

Multi-regional locations

Multi-region description	Multi-region name	IP addresses
Data centers within member states of the European Union¹	`EU`	34.76.156.158 34.76.156.172 34.76.136.146 34.76.1.29 34.76.156.232 34.76.156.81 34.76.156.246 34.76.102.206 34.76.129.246 34.76.121.168
Data centers in the United States	`US`	35.185.196.212 35.197.102.120 35.185.224.10 35.185.228.170 35.197.5.235 35.185.206.139 35.197.67.234 35.197.38.65 35.185.202.229 35.185.200.120

¹ Data located in the EU multi-region is not stored in the europe-west2 (London) or europe-west6 (Zürich) data centers.

Grant access to your Amazon S3 bucket

You will need an S3 bucket to use as a staging area, for transferring the Amazon Redshift data to BigQuery. Detailed instructions from Amazon can be found here.

We recommended you create a dedicated Amazon IAM user, and grant that user only Read access to Redshift and Read and Write access to S3. This can be achieved by applying the following existing policies:
Create an Amazon IAM user access key pair.

Optional: workload control with a separate migration queue

You can define an Amazon Redshift queue for migration purposes to limit and separate the resources used for migration. This migration queue can be configured with a max concurrency query count. You can then associate a certain migration user group with the queue, and use those credentials when setting up the migration to transfer data to BigQuery. The transfer service will only have access to the migration queue.

Setting up an Amazon Redshift transfer

To set up an Amazon Redshift transfer:

Console

Go to the BigQuery page in the Cloud Console.

Go to the BigQuery page
Click Transfers.
Click Add Transfer.
On the New Transfer page:
- For Source, choose Migration: Amazon Redshift.
- For Display name, enter a name for the transfer such as My migration. The display name can be any value that allows you to easily identify the transfer if you need to modify it later.
- For Destination dataset, choose the appropriate dataset.
Under Data Source Details, continue with specific details for your Amazon Redshift transfer.
- For JDBC connection url for Amazon Redshift, provide the JDBC url to access your Amazon Redshift cluster.
- For Username of your database, enter the username for the Amazon Redshift database you'd like to migrate.
- For Password of your database, enter the database password.
  By providing your Amazon credentials you acknowledge that the BigQuery Data Transfer Service is your agent solely for the limited purpose of accessing your data for transfers.
- For Access key ID and Secret access key, enter the access key pair you obtained from Grant access to your S3 bucket.
- For Amazon S3 URI, enter the URI of the S3 bucket you'll use as a staging area.
- For Amazon Redshift Schema, enter the Amazon Redshift Schema you're migrating.
- For Table name patterns specify a name or a pattern for matching the table names in the Schema. You can use regular expressions to specify the pattern in the form: <table1Regex>;<table2Regex>. The pattern should follow Java regular expression syntax.
  Examples:
  - lineitem;ordertb will match two tables named lineitem and ordertb.
  - Leave this field empty to migrate all tables from the specified schema.
  For very large tables, we recommend transferring one table at a time. BigQuery has a load quota of 15 TB per load job.
- (Optional) In the Notification options section:
  - Click the toggle to enable email notifications. When you enable this option, the transfer administrator receives an email notification when a transfer run fails.
  - For Select a Pub/Sub topic, choose your topic name or click Create a topic. This option configures Pub/Sub run notifications for your transfer.
Click Save.
The Cloud Console will display all the transfer setup details, including a Resource name for this transfer.

Classic UI

Go to the classic BigQuery web UI.

Go to the classic BigQuery web UI
Click Transfers.
Click Add Transfer.
On the New Transfer page:
- For Source, choose Migration: Amazon Redshift.
- For Display name, enter a name for the transfer such as My Migration. The display name can be any value that allows you to easily identify the transfer if you need to modify it later.
- For Destination dataset, choose the appropriate dataset.
- For JDBC connection url for Amazon Redshift, provide the JDBC url to access your Amazon Redshift cluster.
- For Username of your database, enter username for the Amazon Redshift database you'd like to migrate.
- For Password of your database, enter the database password.
  By providing your Amazon Redshift credentials you acknowledge that the BigQuery Data Transfer Service is your agent solely for the limited purpose of accessing your data for transfers.
- For Access key ID and Secret access key, Enter the access key pair you obtained from Grant access to your S3 bucket.
- For Amazon S3 URI, enter the URI of S3 bucket you'll use as a staging area.
- For Amazon Redshift Schema, enter the Amazon Redshift Schema you would like to migrate tables from.
- For Table name patterns specify a name or pattern for matching the table names in the database schema. You can use regular expressions to specify the pattern, in the form: <table1Regex>;<table2Regex>. The pattern should follow Java regular expression syntax.
  Examples:
  - lineitem;ordertb will match two tables named lineitem and ordertb.
  - Leave this field empty to migrate all tables from the specified schema.
For very large tables, we recommend transferring one table at a time. BigQuery has a load quota of 15 TB per load job.
- (Optional) Expand the Advanced section and configure run notifications for your transfer.
  - For Pub/Sub topic, enter your topic name, for example, projects/myproject/topics/mytopic.
  - Check Send email notifications to allow email notifications of transfer run failures.
  - Don't check Disabled when setting up a transfer. See Working with transfers to disable existing transfers.
Click Add.
If prompted, click Allow to give the BigQuery Data Transfer Service permission to manage your transfer. You must allow browser pop-ups from bigquery.cloud.google.com to view the permissions window.
The web UI will display all the transfer setup details, including a Resource name for this transfer.

bq

Enter the bq mk command and supply the transfer creation flag --transfer_config. The following flags are also required:

--project_id
--data_source
--target_dataset
--display_name
--params

bq mk \
--transfer_config \
--project_id=project_id \
--data_source=data_source \
--target_dataset=dataset \
--display_name=name \
--params='parameters'

Where:

project_id is your Google Cloud project ID. If --project_id isn't specified, the default project is used.
data_source is the data source: redshift.
dataset is the BigQuery target dataset for the transfer configuration.
name is the display name for the transfer configuration. The transfer name can be any value that allows you to easily identify the transfer if you need to modify it later.
parameters contains the parameters for the created transfer configuration in JSON format. For example: --params='{"param":"param_value"}'.

Parameters required for an Amazon Redshift transfer configuration are:

jdbc_url: The JDBC connection url is used to locate the Amazon Redshift cluster.
database_username: The username to access your database to unload specified tables.
database_password: The password used with the username to access your database to unload specified tables.
access_key_id: The access key ID to sign requests made to AWS.
secret_access_key: The secret access key used with the access key ID to sign requests made to AWS.
s3_bucket: The Amazon S3 URI beginning with "s3://" and specifying a prefix for temporary files to be used.
redshift_schema: The Amazon Redshift schema that contains all the tables to be migrated.
table_name_patterns: Table name patterns separated by a semicolon (;). The table pattern is a regular expression for table(s) to migrate. If not provided, all tables under the database schema will be migrated.

For example, the following command creates an Amazon Redshift transfer named My Transfer with a target dataset named mydataset and a project with the ID of google.com:myproject.

bq mk \
--transfer_config \
--project_id=myproject \
--data_source=redshift \
--target_dataset=mydataset \
--display_name='My Transfer' \
--params='{"jdbc_url":"jdbc:postgresql://test-example-instance.sample.us-west-1.redshift.amazonaws.com:5439/dbname","database_username":"my_username","database_password":"1234567890","access_key_id":"A1B2C3D4E5F6G7H8I9J0","secret_access_key":"1234567890123456789012345678901234567890","s3_bucket":"s3://bucket/prefix","redshift_schema":"public","table_name_patterns":"table_name"}'

API

Use the projects.locations.transferConfigs.create method and supply an instance of the TransferConfig resource.

Quotas and limits

BigQuery has a load quota of 15 TB, per load job, per table. Internally, Amazon Redshift compresses the table data, so the exported table size will be larger than the table size reported by Amazon Redshift. If you are planning to migrate a table larger than 15 TB, please reach out to bq-dts-support@google.com first.

Note that costs can be incurred outside of Google by using this service. Please review the Amazon Redshift and Amazon S3 pricing pages for details.

Because of Amazon S3's consistency model, it's possible for some files to not be included in the transfer to BigQuery.

What's next

Learn about Migrating Amazon Redshift private instances with VPC.
Learn more about the BigQuery Data Transfer Service