If your organization is serious about data governance, you’ve probably looked at DataHub—the open-source metadata platform that centralizes assets, lineage, ownership, and policies across your stack. But to unlock DataHub’s value at scale, you need more than a quickstart script. You need a secure, auditable, and reproducible infrastructure from day one.
This guide shows how to deploy and operate DataHub with Terraform (Infrastructure as Code) using cloud security best practices, policy-as-code guardrails, and an audit-ready change process that satisfies technical and compliance stakeholders alike.
What you’ll learn:
- Why Terraform is a natural fit for DataHub (repeatability, drift detection, compliance)
- A secure-by-design reference architecture for AWS/Azure/GCP
- How to organize Terraform modules, remote state, and promotion across environments
- Policy-as-code, CI/CD, and zero-trust controls for a hardened deployment
- DataHub-specific security hardening and operations playbooks
- Practical tips to integrate lineage and governance across your data stack
Keywords: Terraform for DataHub, secure DataHub deployment, auditable infrastructure, Infrastructure as Code, data governance, metadata lineage, Kubernetes, encryption at rest, policy as code.
Why Use Terraform for DataHub?
DataHub sits at the heart of your data ecosystem. That means your deployment must be:
- Repeatable: One definition for dev, test, and prod—no snowflake environments.
- Secure: Private networking, strong identity and access management (IAM), and encrypted secrets.
- Auditable: Every change is tracked, reviewed, and explainable.
Terraform delivers:
- Versioned infrastructure (Git history is your change log)
- Drift detection (plan diffs reveal unexpected changes)
- Policy-as-code (enforce standards automatically)
- Provider ecosystem (Helm, Kubernetes, AWS/Azure/GCP services)
If you’re still weighing IaC options, this practical comparison helps you decide: Terraform vs. CloudFormation for data infrastructure.
A Secure-by-Design Reference Architecture
Below is a cloud-agnostic pattern you can implement on AWS, Azure, or GCP.
Core components
- DataHub services: GMS (Metadata Service), Frontend, MCE/MAE event streams
- Event streaming: Kafka (managed or self-hosted)
- Search indexing: Elasticsearch/OpenSearch
- Relational storage: MySQL or PostgreSQL
- Optional: Object storage for backups and artifacts
Networking and perimeter
- Private subnets only for stateful services
- Ingress via an Application/HTTP(s) Load Balancer with WAF
- Security Groups/NSGs to restrict east-west traffic
- Private endpoints/VPC peering to data services
Identity and access
- Least privilege IAM roles/service accounts
- Workload identity federation for CI/CD (no long-lived keys)
- Scoped access tokens for DataHub ingestion and admin tasks
Secrets and encryption
- Secrets in Vault, AWS Secrets Manager, Azure Key Vault, or GCP Secret Manager
- TLS everywhere (in transit) and KMS-backed encryption at rest
- Rotate tokens and credentials automatically
Observability and reliability
- Prometheus/Grafana for metrics; structured logs to CloudWatch/Log Analytics/Cloud Logging
- Health probes on DataHub services
- Snapshots and backups for Kafka, search, and relational stores
Deployment Patterns That Work
- Kubernetes (EKS/AKS/GKE) + Helm: Use Terraform’s helm_release to pin DataHub chart versions and handle rolling upgrades.
- Managed services: MSK/Confluent for Kafka, OpenSearch/Elastic Cloud, and RDS/Aurora/PostgreSQL for the relational store.
- Container platforms: ECS/Fargate + managed data services if you don’t want Kubernetes complexity.
Tip: Favor managed services for Kafka and search in production. These systems are complex to operate at scale.
Organizing Your Terraform the Right Way
- Repository structure:
- modules/
- network
- cluster
- kafka
- search
- database
- datahub_app (Helm/Kubernetes)
- envs/
- dev
- staging
- prod
- Remote state:
- AWS: S3 + DynamoDB locking
- Azure: Blob Storage + state locks
- GCP: GCS + locking
- Promotion:
- PR-based promotion from dev → staging → prod
- Pin module and provider versions to guarantee reproducibility
Auditable by Default: Change Control and Policy-as-Code
A robust, audit-ready workflow typically includes:
- PR reviews with mandatory approvals and signed commits
- Terraform plan artifacts attached to PRs
- Infracost for change-aware cost estimation (optional)
- Static analysis and security scanning (TFLint, tfsec, Checkov)
- OPA/Conftest or Sentinel (Terraform Cloud/Enterprise) to enforce:
- No public subnets without justification
- Mandatory encryption and backups
- Allowed Kubernetes versions and regions
- Required tags for ownership, environment, cost center
This pipeline gives you traceability for SOC 2/ISO 27001 controls (change management, access control, configuration management).
DataHub-Specific Hardening
- Authentication/SSO:
- Integrate OIDC/SAML (e.g., Okta, Azure AD, Google)
- Enforce MFA and SCIM for lifecycle management
- Authorization:
- Use DataHub roles/ACLs and group-based access
- Scope ingestion tokens per source; rotate regularly
- Network and ingestion security:
- Restrict ingestion endpoints with IP allowlists/VPN/PrivateLink
- TLS for ingestion pipelines; sign ingestion configs where possible
- Governance and privacy:
- Classify and tag PII/PHI; restrict visibility via policies
- Enable approvals for schema changes and glossary updates
- Observability:
- Scrape DataHub metrics; set SLOs and alerts on ingestion failures, indexing lag, and API latencies
To connect lineage and governance across your stack, see this practical end-to-end guide for DataHub + dbt. It shows how to propagate models, tests, ownership, and documentation into DataHub automatically.
CI/CD Pipeline for Terraform and DataHub
A sample pipeline might include:
- Format/validate: terraform fmt/validate, TFLint
- Security checks: tfsec/Checkov, OPA policies
- Plan + cost: terraform plan + Infracost
- Manual approval gate (prod)
- Apply with logs and artifact retention
- Post-deploy checks: health probes, smoke tests, canary traffic
- Rollback strategy: plan/apply of previous version, or blue/green
Multi-Environment and Multi-Region Strategy
- Separate state files, workspaces, and accounts/subscriptions per environment
- Parameterize via tfvars and module inputs
- Blue/green or canary for DataHub upgrades (roll out GMS and Frontend gradually)
- Backups and cross-region replication for Kafka topics, search indices, and databases
Disaster Recovery and Upgrades
- RPO/RTO targets:
- Database snapshots (hourly/daily)
- Kafka topic replication and retention policies
- Search index snapshots
- Upgrades:
- Pin Helm chart and app versions
- Use Kubernetes rolling updates; pre-warm indices where feasible
- Test in staging with realistic ingestion load before prod cutover
Governance, Lineage, and Compliance
DataHub becomes your “single source of truth” for technical lineage and ownership. Combine it with policy-as-code and you’ll:
- Prove data provenance and change history
- Enforce visibility and access controls consistently
- Accelerate audits with centralized documentation
If you’re evaluating different lineage strategies, this breakdown of automated data lineage explains benefits, approaches, and real-world impact.
Common Pitfalls (And How to Avoid Them)
- Unlocked Terraform state: Always use remote state + locking.
- Overprivileged roles: Apply least privilege; audit IAM policies.
- Hard-coded secrets: Store in a secrets manager; never in Terraform code.
- Unpinned versions: Pin providers, modules, Helm charts.
- Public ingress by accident: OPA/Conftest rules to block unsafe resources.
- Ignoring egress: Restrict outbound traffic with NAT + firewall rules.
- No backup plan: Schedule snapshots and test recovery regularly.
Quick Start Checklist
- Remote state with locking in place
- VPC/VNet with private subnets; WAF on public ingress
- Managed Kafka and search; encrypted RDS/Postgres
- Kubernetes cluster with restricted node and pod access
- Terraform CI/CD with plan approvals and policy checks
- DataHub SSO + RBAC + scoped ingestion tokens
- Metrics, logs, and alerts wired into your observability stack
- Backups, snapshots, and DR tested
Where Terraform Meets Real-World Data Governance
Terraform gives you control, DataHub gives you visibility. Together, they create a secure, auditable foundation for modern data governance—one that scales with your business and simplifies compliance instead of slowing you down.
If you decide Terraform isn’t the best fit for your stack, revisit the trade-offs here: Terraform vs. CloudFormation for data infrastructure.
FAQ: Terraform + DataHub
1) What’s the simplest production-ready way to deploy DataHub with Terraform?
Use managed services where possible: Kubernetes (EKS/AKS/GKE) for compute; managed Kafka (MSK/Confluent), OpenSearch/Elastic Cloud for search, and a managed MySQL/PostgreSQL (RDS/Aurora/Cloud SQL). Deploy DataHub with Helm via Terraform’s helm_release provider. This balances control, reliability, and operational effort.
2) Can I run DataHub in production without Kubernetes?
Yes. You can use ECS/Fargate (AWS) or container apps (Azure/GCP) with managed data services. Kubernetes remains the most flexible option for upgrades, autoscaling, and resilience, but it’s not strictly required.
3) How do I make my DataHub infrastructure audit-ready?
Adopt PR-based workflows, store Terraform state remotely with locking, keep plan/apply artifacts, and enforce policy-as-code checks (OPA/Conftest or Sentinel). Tag resources with owner, environment, and purpose; wire logs/metrics to a centralized platform. This provides traceability for SOC 2/ISO 27001 controls.
4) What security settings matter most for DataHub?
- Private networking and WAF-protected ingress
- Least-privilege IAM roles/service accounts
- TLS everywhere; KMS-backed encryption at rest
- Secrets stored in a dedicated secrets manager
- SSO + RBAC in DataHub, with scoped ingestion tokens
- IP allowlists or private links for ingestion endpoints
5) How do I handle secrets in Terraform?
Never hard-code secrets. Use a secrets manager (Vault, AWS Secrets Manager, Azure Key Vault, GCP Secret Manager). Reference secrets through Terraform data sources or inject them at runtime via your platform’s secret injection mechanism.
6) How do I integrate DataHub with dbt, Airflow, and warehouses?
Use DataHub’s ingestion framework and dbt/airflow plugins to pull models, tests, lineage, and ownership. For a step-by-step approach, see this end-to-end DataHub + dbt blueprint.
7) What’s the recommended upgrade strategy?
Pin specific Helm chart/app versions, apply rolling updates, and test upgrades in staging with realistic ingestion workloads. Keep automated backups for your database, Kafka, and search. Consider blue/green or canary deployments for critical changes.
8) How can I prove lineage and data provenance to auditors?
Turn on automated lineage ingestion from ETL/ELT tools and SQL parsers, maintain ownership and glossary in DataHub, and keep Terraform change history for infrastructure. Automated lineage provides the evidence trail auditors need; this guide to automated data lineage explains the benefits and approaches.
9) How should I structure environments?
Use separate workspaces/states per environment and optionally separate cloud accounts/subscriptions for strong isolation. Parameterize differences via tfvars. Promote changes via PRs from dev → staging → prod.
10) Is Terraform always better than native cloud IaC?
Not always. Many teams prefer Terraform for its multi-cloud support and broad provider ecosystem. If you’re deep in a single cloud, a native IaC tool might fit. Evaluate your roadmap using this comparison: Terraform vs. CloudFormation for data infrastructure.
By combining Terraform’s rigor with DataHub’s visibility, you’ll create a secure, auditable metadata platform that scales—without sacrificing speed or control.
