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Snowflake Loader supports 3 authentication options: storage integration, IAM role and IAM credentials.

Setting up a storage integration

This section is only for users loading data into Snowflake using storage integration auth mechanism.

Snowflake highly recommend this option, which avoids the need to supply AWS IAM credentials when creating stages or loading data.

To setup an integration, we recommend following Snowflake’s tutorial up to step 6 so that you

  1. create an IAM policy with required permissions
  2. create an IAM role using the policy created at step 1
  3. create a storage integration at Snowflake
  4. update trust policy of the IAM role

Now you can configure your loader where top level auth looks like following as an example:

"auth": { "integrationName": "SNOWPLOW_S3_INTEGRATION" }
Code language: JavaScript (javascript)

Don’t forget to use 1-0-3 version of configuration schema.

Assuming that loader configuration is updated, you can run loader’s setup subcommand to have remaining entities created automatically.

Setting up a load role

This section is only for users loading data into Snowflake using roleArn/sessionDuration auth mechanism. With this mechanism, Snowflake Loader sends AssumeRole request to AWS Security Token Service and it returns temporary credentials (with lifespan equal sessionDuration of seconds), which then passed with COPY INTO statement, allowing Snowflake to aunthenticate itself in your account. This is similar to what RDB Loader does for loading Redshift, main difference is that Snowflake Loader authenticates third-party AWS account (belonging to Snowflake Computing) to read data from S3.

First step is to create necessary AWS IAM entities restricted only to reading data from S3.

Create following IAM Policy, called SnowflakeLoadPolicy:

{ "Version": "2012-10-17", "Statement": [ { "Sid": "VisualEditor0", "Effect": "Allow", "Action": [ "s3:GetAccelerateConfiguration", "s3:GetObject", "s3:GetObjectVersion" ], "Resource": [ "arn:aws:s3:::YOUR-SNOWFLAKE-BUCKET/prefix/*" ] }, { "Sid": "VisualEditor1", "Effect": "Allow", "Action": [ "s3:ListBucket" ], "Resource": [ "arn:aws:s3:::YOUR-SNOWFLAKE-BUCKET" ] } ] }
Code language: JSON / JSON with Comments (json)

This policy allows read-only access to your S3 bucket.

Next, you need to create an IAM role that will provide credentials.

  1. IAM -> Roles -> Create role -> AWS service -> EC2
  2. Attach just created SnowflakeLoadPolicy
  3. Trust relationships -> Edit Trust relationship
  4. Insert following document (replacing 123456789123 with your account id and EMR_EC2_DefaultRole with your EMR role) and save it:
{ "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Principal": { "AWS": "arn:aws:iam::123456789123:role/EMR_EC2_DefaultRole" }, "Action": "sts:AssumeRole" } ] }
Code language: JSON / JSON with Comments (json)
  1. Now save Role ARN as your roleArn in target configuration

Setting up Snowflake

Snowflake Loader provides quick setup action that automatically creates following entities:

  • atomic database schema
  • atomic.events table to store enriched events
  • File format – entity to describe how Snowplow enriched data should be processed
  • External Stage in atomic schema – reference to S3 path; output of Snowplow Snowflake Transformer. Can optionally contain AWS credentials
  • Virtual Warehouse – computing entity of Snowflake; smallest (X-Small) be default

All above safely can have default settings. Warehouse can be scaled up manually.

(Optional) If you want to use storage integration, you should create it before running setup command. Check the related section of this page to see instructions.

Two things you need to create manually are Snowflake database and DynamoDB table. After database is created, you can run setup.

To do this you need to use setup CLI action for Snowflake Loader:

$ wget http://snowplow-hosted-assets.s3.amazonaws.com/4-storage/snowflake-loader/snowplow-snowflake-loader-0.8.0.jar $ java -jar snowplow-snowflake-loader-0.8.0.jar \ setup \ --config /path/to/self-describing-config.json \ --resolver /path/to/resolver.json # OR $ java -jar snowplow-snowflake-loader-0.8.0.jar \ setup \ --base64 --config $(cat /path/to/self-describing-config.json | base64 -w 0) \ --resolver $(cat /path/to/resolver.json | base64 -w 0)
Code language: PHP (php)

Storing credentials in stage

Apart from using AWS Role and static credentials, it is also possible to save credentials in Snowflake stage.

This can be added manually (if stage already exists). Snowflake Console -> Databases -> YOUR DB -> Stages -> Edit YOUR STAGE Or during setup from local machine (if stage doesn’t exist). If you add credentials to config, run setup – they’ll be added to stage and after than can be safely removed from config


To use DynamoDB table as processing manifest you need to create table with partition key RunId with string type and fill manifest property in configuration with newly created table name.


Snowplow data in Snowflake is stored in single fat table called atomic.events (schema can be changed, table name cannot).

Initial atomic.events DDL for Snowflake can be found in atomic-def.sql.

Dataflow Runner

Dataflow Runner used to run Snowplow Snowflake Transformer Spark job on EMR cluster. It also can run loader.

EMR Cluster has default configuration. Only ec2.keyName and logUri must be changed. Everything else is optional. Edit and save below as cluster.json:

{ "schema":"iglu:com.snowplowanalytics.dataflowrunner/ClusterConfig/avro/1-1-0", "data":{ "name":"dataflow-runner - snowflake transformer", "logUri":"s3://snowplow-snowflake-test/logs/", "region":"us-east-1", "credentials":{ "accessKeyId":"env", "secretAccessKey":"env" }, "roles":{ "jobflow":"EMR_EC2_DefaultRole", "service":"EMR_DefaultRole" }, "ec2":{ "amiVersion":"5.9.0", "keyName":"key-name", "location":{ "vpc":{ "subnetId":null } }, "instances":{ "master":{ "type":"m2.xlarge" }, "core":{ "type":"m2.xlarge", "count":1 }, "task":{ "type":"m1.medium", "count":0, "bid":"0.015" } } }, "tags":[ ], "bootstrapActionConfigs":[ ], "configurations":[ { "classification":"core-site", "properties":{ "Io.file.buffer.size":"65536" } }, { "classification":"mapred-site", "properties":{ "Mapreduce.user.classpath.first":"true" } }, { "classification":"yarn-site", "properties":{ "yarn.resourcemanager.am.max-attempts":"1" } }, { "classification":"spark", "properties":{ "maximizeResourceAllocation":"true" } } ], "applications":[ "Hadoop", "Spark" ] } }
Code language: JSON / JSON with Comments (json)

Edit and save below as playbook.json:

{ "schema":"iglu:com.snowplowanalytics.dataflowrunner/PlaybookConfig/avro/1-0-1", "data":{ "region":"{{.awsRegion}}", "credentials":{ "accessKeyId":"env", "secretAccessKey":"env" }, "steps":[ { "type":"CUSTOM_JAR", "name":"Snowflake Transformer", "actionOnFailure":"CANCEL_AND_WAIT", "jar":"command-runner.jar", "arguments":[ "spark-submit", "--conf", "spark.hadoop.mapreduce.job.outputformat.class=com.snowplowanalytics.snowflake.transformer.S3OutputFormat", "--deploy-mode", "cluster", "--class", "com.snowplowanalytics.snowflake.transformer.Main", "s3://snowplow-hosted-assets/4-storage/snowflake-loader/snowplow-snowflake-transformer-0.8.0.jar", "--config", "{{base64File "./config.json"}}", "--resolver", "{{base64File "./resolver.json"}}", "--events-manifest", "{{base64File "./events_manifest.json"}}" ] }, { "type":"CUSTOM_JAR", "name":"Snowflake Loader", "actionOnFailure":"CANCEL_AND_WAIT", "jar":"s3://snowplow-hosted-assets/4-storage/snowflake-loader/snowplow-snowflake-loader-0.8.0.jar", "arguments":[ "load", "--base64", "--config", "{{base64File "./config.json"}}", "--resolver", "{{base64File "./resolver.json"}}" ] } ], "tags":[ ] } }8
Code language: JSON / JSON with Comments (json)

To run above configuration you can use following command:

$ dataflow-runner run-transient --emr-config cluster.json --emr-playbook playbook.json

This will start both Transformer and Loader on EMR cluster.

Note that loader also can be launched on local machine, with paths specified for --config and --resolver – you’ll have to omit --base64 for that.

Staging enriched data

Snowflake Transformer (and Loader) expects certain structure in stageUrl.

s3://snowplow-data/enriched/archive/ + run=2020-12-01-16-30-50 + run=2020-12-01-17-31-02 + run=2020-12-01-18-30-55
Code language: JavaScript (javascript)

If your pipelilne uses EmrEtlRunner, those folders will be created as part of data archivation. However without EmrEtlRunner, you need to create this folder structure yourself before running Snowflake Transformer and Loader. We recommend to create this structure with auxiliary S3DistCp step before transformer (add as first step in playbook.json):

{ "type": "CUSTOM_JAR", "name": "Staging enriched data", "actionOnFailure": "CANCEL_AND_WAIT", "jar": "/usr/share/aws/emr/s3-dist-cp/lib/s3-dist-cp.jar", "arguments": [ "--src", "s3://snowplow-data/enriched-sink/", "--dest", "s3://snowplow-data/enriched/archive/run={{nowWithFormat "2006-01-02-15-04-05"}}/", "--s3Endpoint", "s3.amazonaws.com", "--srcPattern", ".*\\.gz", "--deleteOnSuccess", "--s3ServerSideEncryption" ] },
Code language: JavaScript (javascript)

Tranformer then will discover new folders in s3://snowplow-data/enriched/archive/ and keep the state (processed and loaded directories) in DynamoDB manifest.