Bulk Data API Redshift Pipeline Example

An example of using our Terraform module for implementing a data ETL pipeline from ControlShift to Amazon Redshift.

The output of this plan is a replica of all of the tables that underlie your ControlShift instance in a new Redshift instance which allows for querying via SQL or other analysis.

If you are already using Terraform or Redshift it is probably best to either fork this example or use the module we provide directly in your own plan

Overview

The Terraform plan sets up resources in your AWS environment to process webhooks generated by the ControlShift Bulk Data API.

The integration is based on the aws-lambda-redshift-loader provided by AWS but replaces the manual setup steps from their README with a Terraform plan. In addition the Terraform plan includes resourced that are specific to accepting ControlShift Bulk Data API webhooks.

The resources created include:

• DynamoDB tables that store configuration information and logs each table load processed.
• Lambda functions that process incoming webhooks, store CSV files onto S3 and load those files into tables in Redshift.
• Glue connection, crawler and job for importing signatures full table export.
• S3 buckets for storing incoming S3 CSVs and manifests of load activity.
• S3 bucket for storing Glue resources and temporary files.
• A Web API Gateway to connect AWS Lambdas to the web.
• IAM permissions to make everything work securely.
• Network resources for allowing connection between Glue, S3 and Redshift.

Prerequisites

• Familiarity with Amazon Web Services, Redshift, and Terraform
• Use of aws-vault or a similar tool for using AWS secrets securely.
• The terraform command line tool. Download

Setup Tables in Redshift

For the ingest process to work correctly, tables that match the output of the ControlShift Bulk Data API must be setup in Redshift first. We've provided a create_tables.rb script that will use the ControlShift Bulk Data Schema API to generate CREATE TABLE DDL statements that you'll need to run to populate the tables for ingest.

First generate the DDL statements, and then apply them manually in your Redshift environment.

create_tables.rb > tables.sql

Terraform Variables

Terraform input variables are defined in variables.tf. You'll want to create your own terraform.tfvars file with the correct values for your specific environment.

Name	Description
aws_region	The AWS Region to use. Should match the location of your Redshift instance, defaults to us-east-1.
controlshift_environment	The environment of your ControlShift instance. Either staging or production.
controlshift_hostname	The hostname of your ControlShift instance. Likely to be something like action.myorganization.org.
controlshift_organization_slug	The organization's slug in ControlShift platform. Ask support team (support@controlshiftlabs.com) to find this value.
failed_manifest_prefix	A file prefix that will be used for manifest logs on failure, defaults to failed.
failure_topic_name	An SNS topic name that will be notified about batch processing failures, defaults to ControlshiftLambdaLoaderFailure.
failure_topic_name_for_run_glue_job_lambda	An SNS topic name that will be notified about batch processing failures, defaults to ControlshiftLambdaLoaderFailure.
glue_scripts_bucket_name	Your S3 bucket name to store Glue scripts in.
manifest_bucket_name	Your S3 bucket name to store manifests of ingests processed in. Terraform will create this bucket for you. Must be globally unique.
manifest_prefix	A file prefix that will be used for manifest logs on success, defaults to manifests.
receiver_timeout	The timeout for the receiving Lambda, in seconds, defaults to 60.
redshift_password	Redshift Password to use for database loads.
redshift_schema	The Redshift schema to load tables into, defaults to public.
redshift_username	Redshift Username to use for database loads.
success_topic_name	An SNS topic name that will be notified about batch processing successes, defaults to ControlshiftLambdaLoaderSuccess.
success_topic_name_for_run_glue_job_lambda	An SNS topic name that will be notified about batch processing successes, defaults to ControlshiftGlueJobSuccess.

Run Terraform

You'll need:

• AWS Credentials with rather broad permissions in your environment.
• AWS restricts certain IAM operations this terraform plan uses to credentials that have been authenticated with MFA. As a result using aws-vault or a similar tool to assume a role with the correct permissions, protected by MFA is probably necessary.

Check out a copy of this repository locally, and then in the project directory:

# download the terraform dependencies and initialize the directory
terraform init
# use aws-vault to generate temporary AWS session credentials using the bulk-data profile and then use them to apply the plan
aws-vault exec bulk-data -- terraform apply

The output of the terraform plan is a Webhook URL. You'll need to configure this in your instance of the ControlShift platform via Settings > Integrations > Webhooks.

Once the webhook is configured it should populate the tables within your Redshift instance nightly. Alternatively, you can use the "Test Ingest" feature to trigger a full-table refresh on demand from the ControlShift web UI.

Logs and Debugging

The pipeline logs its activity several places that are useful for debugging.

• In CloudWatch Logs of Lambda, Glue Job and Crawler, and S3 activity.
• In DynamoDB tables for each manifest.
• In Redshift, in the Loads tab of your datawarehouse instance.
• In each manifest load whose results stored in S3.