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Export from Airtable to S3

CloudQuery is an open-source data integration platform that allows you to export data from any source to any destination.

The CloudQuery Airtable plugin allows you to sync data from Airtable to any destination, including S3. It takes only minutes to get started.

Airtable
airtable
Official

Airtable

This plugin is in preview.

The CloudQuery Airtable plugin pulls data from Airtable and loads it into any supported CloudQuery destination

Publisher

cloudquery

Repositorygithub.com
Latest version

v2.2.3

Type

Source

Platforms
Date Published

S3
s3
Official

S3

This destination plugin lets you sync data from a CloudQuery source to remote S3 storage in various formats such as CSV, JSON and Parquet

Publisher

cloudquery

Repositorygithub.com
Latest version

v7.2.2

Type

Destination

Platforms
Date Published

Linux Setup

Step 1. Install CloudQuery

curl -L https://github.com/cloudquery/cloudquery/releases/download/cli-v5.22.1/cloudquery_linux_amd64 -o cloudquery
chmod a+x cloudquery

Step 2. Log in to CloudQuery CLI

cloudquery login

Step 3. Configure Airtable source plugin

You can find more information about the configuration in the plugin documentation

kind: source
# Common source-plugin configuration
spec:
  name: airtable
  registry: docker
  path: docker.cloudquery.io/cloudquery/source-airtable:v2.2.3
  tables: ["*"]
  destinations: ["s3"]
  # airtable-specific configuration
  spec:
    access_token: "${AIRTABLE_ACCESS_TOKEN}" # required
    # endpoint_url: "https://api.airtable.com" # Optional, defaults to `https://api.airtable.com`
    # concurrency: 10000 # Optional, defaults to `10000`

Step 4. Configure S3 destination plugin

You can find more information about the configuration in the plugin documentation

kind: destination
spec:
  name: "s3"
  path: "cloudquery/s3"
  registry: "cloudquery"
  version: "v7.2.2"
  write_mode: "append"
  # Learn more about the configuration options at https://cql.ink/s3_destination
  spec:
    bucket: "bucket_name"
    region: "region-name" # Example: us-east-1
    path: "path/to/files/{{TABLE}}/{{UUID}}.{{FORMAT}}"
    format: "parquet" # options: parquet, json, csv
    format_spec:
      # CSV-specific parameters:
      # delimiter: ","
      # skip_header: false

    # Optional parameters
    # compression: "" # options: gzip
    # no_rotate: false
    # athena: false # <- set this to true for Athena compatibility
    # write_empty_objects_for_empty_tables: false # <- set this to true if using with the CloudQuery Compliance policies
    # test_write: true # tests the ability to write to the bucket before processing the data
    # endpoint: "" # Endpoint to use for S3 API calls.
    # endpoint_skip_tls_verify # Disable TLS verification if using an untrusted certificate
    # use_path_style: false
    # batch_size: 10000 # 10K entries
    # batch_size_bytes: 52428800 # 50 MiB
    # batch_timeout: 30s # 30 seconds

Step 5. Run Sync

cloudquery sync airtable.yml s3.yml
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