Question 1

Why move Redshift data into Google Cloud Storage?

Accepted Answer

Usually because the consumer lives on the Google side. A processing job, a machine learning workflow, or a team whose tooling is built around Google Cloud cannot conveniently reach a cluster in AWS, and files in a bucket are the handoff both sides understand. It also suits handing data to an external party who works in that cloud without giving them anything near your warehouse.

Question 2

What does crossing clouds involve?

Accepted Answer

Credentials on each side and egress in between. Redshift is connected with cluster details and credentials, optionally over an SSH tunnel, while Cloud Storage takes a service account JSON key and a bucket. Neither is passed to the other. Data leaves AWS and lands in Google, so transfer costs apply on top of the cluster time the read consumes. Both are modest for a daily extract and worth checking before scheduling a wide table hourly.

Question 3

Which format should I use?

Accepted Answer

Parquet where the files will be queried later, because it carries types and reads efficiently by column, which matters more here since the consumer is usually a different stack entirely. JSON Lines keeps records intact for row by row processing, and CSV is the compatibility option. One Redshift detail to note: source VARCHAR lengths are not reported, so string columns arrive without a declared width.

Question 4

Will the export slow the cluster down?

Accepted Answer

It can if the read is large and badly timed, since sync queries use the same compute as everything else on the cluster. Polytomic does not set a workload management queue, so the query inherits whatever the connecting user has, which makes putting that user in a suitable queue the most effective single control. Then use a tracking field, align the sort key with it, select only the columns you export, and schedule big initial extracts off peak.

Question 5

How do I sync Redshift to Google Cloud Storage?

Accepted Answer

Connect Redshift with cluster details, credentials and an S3 staging bucket, using an SSH tunnel if the cluster is private, and connect Cloud Storage with a service account JSON key and the destination bucket. Then select the Redshift data to export, choose CSV, JSON Lines, JSON array or Parquet, and pick the write mode. Parquet is the usual choice where the files will be read by a query engine afterwards.

Connect Redshift with Google Cloud Storage

Connect Redshift and Google Cloud Storage with Polytomic

Two sets of credentials

Types survive in Parquet

Kept current on a schedule

Trusted by businesses worldwide

Step-by-step integration guide

Connect Redshift in Polytomic

Connect Google Cloud Storage in Polytomic

Create a sync between Redshift and Google Cloud Storage

Map fields and enable the sync

Frequently asked questions

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