Reliability · infrastructure recovery
SAP Databricks Disaster Recovery
Impact
The drill makes the dependency order visible: infrastructure must be ready, then the workload must start, reach its data, and produce a verifiable output.
- Terraform provisioning
- Multi-region Databricks
- Failover drills
01 · Problem & operating context
A standby region is not useful if it becomes a different platform.
Regional recovery is difficult to trust when the secondary environment is assembled by hand. Small differences in network access, identity, workspace settings, or runtime configuration can turn a planned failover into a new incident.
The task was to make the platform foundations repeatable, then make the workload handoff explicit enough to test before a regional failure creates time pressure.
02 · My role & ownership
I encoded the recovery foundation in reusable Terraform.
I designed and implemented reusable Terraform modules for the primary and secondary Azure Databricks foundations. The modules covered the workspace, network, compute, identity, and logging boundaries a workload needs before it can restart.
I also participated in the failover drill that checked the handoff. Workload-specific runbooks and the organization-wide decision to move traffic remained shared responsibilities.
- Designed
- Multi-region recovery architecture and its provisioning sequence.
- Implemented
- Reusable Terraform modules for region and Databricks foundations.
- Covered
- Workspace, network, compute, identity, and logging foundations.
Designed the multi-region disaster-recovery architecture and its recovery sequencing.
Implemented reusable Terraform modules for the Databricks and network foundations.
Covered workspace, network, compute, identity, and logging foundations in the recovery path.
Workload-specific runbooks and organizational recovery decisions remain shared context.
03 · System architecture
Terraform establishes the ground; the drill proves the handoff.
Terraform reproduces workspace, network, compute, identity, and logging foundations for Databricks across primary and standby roles. The two environments share a recognizable structure before a workload needs to move.
The failover drill then checks the handoff in sequence: the foundation is ready, the workload starts, data access works, and the output can be verified. Provisioning and workload recovery remain distinct states.
- Provision
- Terraform repeats workspace, network, compute, identity, and logging foundations.
- Prepare
- The Databricks runtime boundary is ready for the failover drill.
- Drill
- A failover drill checks the dependency handoff before workload recovery.
04 · Decisions & trade-offs
Repeat the foundation, then verify the workload in order.
Reusable modules are safer than a hand-built standby because the recovery boundary is encoded and reviewable. They do not make recovery automatic: identity, network readiness, data access, workload restart, and output verification still need an explicit order.
I kept that order visible in the Terraform and drill design. The modules reduce configuration drift before an incident, while state, dependencies, and the drill itself remain real maintenance work.
Provision the secondary foundation from reusable Terraform modules, then validate the workload in sequence.
Maintain a manually configured standby environment and repair drift during an incident.
Repeatable infrastructure makes the platform shape consistent before a failure creates time pressure.
Modules, state, dependencies, and recovery validation become part of the system that must be maintained.
05 · Failure modes & recovery
A provisioned environment is not yet a recovered workload.
A successful provisioning run can still leave a blocked identity path, unavailable endpoint, missing log stream, or workload that cannot read its data. The drill treats infrastructure readiness and workload validation as separate states.
If a dependency is not ready, the sequence stops there instead of sending work to an environment that only looks complete. The next step is to repair the dependency, restart the affected workload, and validate its output.
- Failure signal
- A regional dependency, Databricks runtime, identity path, or workload output is not ready.
- Attribution
- Recovery sequencing keeps infrastructure, runtime, access, and workload failures distinct.
- Response
- Stop at the failed boundary, repair the dependency, then resume the bounded sequence.
- Authority
- Operators and workload owners decide when traffic or processing can move to the secondary region.
- Recovery check
- Network access, identity, runtime, logging, workload restart, and output are checked in order.
06 · Evidence & boundaries
Evidence & boundaries
- Verified result
- Reusable infrastructure and a failover drill validate the foundation for secondary-region workload recovery.
- Contribution scope
- I designed and implemented reusable Terraform modules for multi-region Azure Databricks recovery: workspace, network, compute, identity, and logging foundations.
- Metrics not established by public material
- No public RTO, RPO, availability, or recovery-time delta is established.