Process Snowplow DataUsing Databricks on AWS
Complete Terraform module to deploy a production-ready Snowplow pipeline with Databricks as your processing engine. Includes Collector, Enrichment, and automated data loading into your Databricks workspace.
Pipeline Components
- Collector Application (Kinesis)
- Enrichment Process
- Databricks Loader
- S3 Pipeline Storage
- Automatic Error Recovery
- Kinesis Stream Management
Includes VPC configuration, load balancers, EC2 instances, and all necessary AWS services for a production-ready Snowplow pipeline.
Snowplow Pipeline Challenges
Common obstacles when setting up Snowplow with Databricks processing
Complex Pipeline
Setting up Snowplow collectors and enrichment is challenging
Impact: Weeks of pipeline architecture planning
Solution: Pre-configured Snowplow components
Data Integration
Connecting Snowplow to Databricks requires expertise
Impact: Data loading gaps and inconsistencies
Solution: Automated Databricks loader setup
Stream Processing
Managing Kinesis streams and throughput
Impact: Data processing delays and bottlenecks
Solution: Optimized stream configuration
Error Handling
Lost events and failed enrichments
Impact: Missing or corrupted analytics data
Solution: Built-in error recovery flows
Pipeline Components
Production-ready Snowplow pipeline with Databricks integration
Complete Pipeline
End-to-end Snowplow pipeline with Databricks integration.
- Collector Application
- Enrichment Process
- Databricks Loader
- S3 Pipeline Storage
AWS Infrastructure
Production-ready AWS components with optimal configuration.
- Kinesis Streams
- Load Balancer (ALB)
- EC2 Instances
- VPC Integration
Built-in Security
Enterprise-grade security and access controls.
- IAM Roles & Policies
- Private Subnets
- Security Groups
- SSL/TLS Support
Data Processing
Optimized data flow with error handling and monitoring.
- Real-time Processing
- Error Recovery
- Bad Event Handling
- Kinesis Throughput Control
Technical Architecture
Core components of the Snowplow Databricks pipeline infrastructure
Pipeline Components
- Snowplow Collector (Scala Stream)
- Enrichment Process (Stream)
- Kinesis Data Streams
- S3 Raw/Enriched Buckets
- Databricks Loader
- Bad Events Storage
AWS Resources
- Application Load Balancer
- Auto Scaling Groups
- EC2 Instances
- CloudWatch Monitoring
- VPC with Private Subnets
- Route53 DNS (Optional)
Security Controls
- IAM Service Roles
- Security Group Rules
- KMS Encryption Keys
- SSL Certificate
- Private Subnet Isolation
- Network ACLs
Installation Guide
Deploy your Snowplow pipeline with Databricks integration in minutes
1. Add Module
module "snowplow_pipeline" {
source = "github.com/Datomni/terraform-aws-snowplow-databricks-pipeline"
aws_region = var.aws_region
environment = var.environment
vpc_id = var.vpc_id
private_subnet_ids = var.private_subnet_ids
# Snowplow Collector
collector_min_size = 1
collector_max_size = 2
# Enrichment
enrichment_min_size = 1
enrichment_max_size = 2
}Add this configuration to your main.tf file
2. Configure Variables
variable "aws_region" {
type = string
default = "us-west-2"
}
variable "environment" {
type = string
default = "production"
}
variable "vpc_id" {
type = string
description = "VPC ID where the pipeline will be deployed"
}
variable "private_subnet_ids" {
type = list(string)
description = "List of private subnet IDs for deployment"
}Define these required variables in your variables.tf file
3. Deploy
# Initialize Terraform and download the module terraform init # Review the infrastructure plan terraform plan # Deploy the pipeline terraform apply -auto-approve
Execute these commands to deploy your Snowplow pipeline with Databricks integration