Description

This curriculum spans the design and operationalization of data archiving in the ELK Stack with a scope comparable to a multi-phase internal capability program, covering policy integration, lifecycle automation, security controls, and legacy modernization across distributed storage tiers.

Module 1: Assessing Data Retention Requirements and Legal Obligations

Map data retention policies to jurisdiction-specific regulations such as GDPR, HIPAA, or SOX based on data classification.
Collaborate with legal and compliance teams to define minimum and maximum data retention durations for different log types.
Classify data streams by sensitivity and regulatory exposure to determine archiving priority and access controls.
Document exceptions for forensic data that require extended retention beyond standard policies.
Establish criteria for data expiration reviews, including audit triggers and stakeholder sign-offs.
Integrate retention rules into ingest pipelines to tag data with lifecycle metadata at ingestion.
Define procedures for handling data subject access requests (DSARs) involving archived logs.
Design audit trails for retention policy changes to support compliance verification.

Module 2: Designing Index Lifecycle Management (ILM) Policies

Configure ILM policies to transition indices from hot to warm, cold, and delete phases based on age and access frequency.
Set shard allocation settings to move indices to nodes with appropriate storage types (SSD vs. HDD).
Adjust replica counts during lifecycle phases to balance availability and cost.
Define rollover conditions using size, age, or document count thresholds for time-series indices.
Implement force merge operations in the cold phase to reduce segment count and storage overhead.
Monitor ILM policy execution delays and tune retry settings to prevent backlog accumulation.
Use index templates to automatically apply ILM policies to new data streams or indices.
Test ILM transitions in staging to validate performance impact before production rollout.

Module 3: Implementing Snapshot and Restore Strategies

Select repository types (S3, NFS, Azure Blob) based on durability, access latency, and cost requirements.
Configure repository access credentials with least-privilege IAM roles or file system permissions.
Define snapshot schedules aligned with backup SLAs and RPOs for critical indices.
Test partial restores of individual indices to validate granularity and recovery time.
Monitor snapshot completion and failure rates using Elasticsearch monitoring APIs.
Implement retention tagging for snapshots to automate cleanup of outdated backups.
Encrypt snapshots at rest using repository-level or Elasticsearch-managed encryption keys.
Validate snapshot integrity by comparing checksums and metadata across environments.

Module 4: Configuring Cold and Frozen Tiers

Deploy dedicated frozen tier nodes with sufficient memory and file system cache for searchable snapshots.
Mount shared storage (e.g., S3-backed repositories) accessible to frozen tier nodes for snapshot access.
Convert cold indices to frozen using searchable snapshots to reduce heap and CPU usage.
Set query timeout and circuit breaker limits for frozen data to prevent long-running searches.
Monitor query latency on frozen data and adjust shard size to improve retrieval performance.
Pre-warm frequently accessed archived indices by caching metadata on frozen nodes.
Balance cost and query responsiveness by determining which indices qualify for frozen tier promotion.
Plan capacity for frozen tier nodes based on concurrent search demand and data volume.

Module 5: Optimizing Index Design for Archival Efficiency

Reduce shard count in archival indices to minimize overhead during snapshot and restore operations.
Apply index compression (best_compression) to cold and frozen indices to reduce storage footprint.
Disable unnecessary features such as _source, norms, or doc_values on fields not used in archived queries.
Use runtime fields sparingly in archived data to avoid computational overhead during search.
Pre-aggregate high-cardinality data where possible to reduce index size and improve query speed.
Implement time-based index naming conventions to simplify automation and lifecycle routing.
Validate mapping compatibility across versions to ensure archived indices can be restored in future upgrades.
Remove unused fields and aliases from indices prior to archival to reduce metadata bloat.

Module 6: Automating Archival Workflows with Elastic Stack Tools

Use Elastic Agent and Fleet to standardize data collection and tagging for archival eligibility.
Configure Watcher alerts to trigger archival actions when indices meet age or size thresholds.
Orchestrate snapshot creation and ILM transitions using Kibana automated actions or custom scripts.
Integrate with external schedulers (e.g., cron, Airflow) to coordinate cross-system archival processes.
Log archival job outcomes to a dedicated index for audit and troubleshooting.
Implement retry logic and alerting for failed archival tasks to ensure data protection.
Use Kibana Spaces to isolate archival monitoring dashboards and restrict access by role.
Version-control ILM policies, index templates, and snapshot scripts using Git for change tracking.

Module 7: Securing Archived Data and Access Paths

Apply role-based access control (RBAC) to restrict snapshot and restore operations to authorized roles.
Encrypt data in transit between Elasticsearch nodes and snapshot repositories using TLS.
Mask sensitive fields in archived logs using ingest pipelines before indexing.
Rotate repository access keys and credentials on a defined schedule or after team changes.
Enable audit logging for security-relevant actions such as snapshot deletion or policy modification.
Store encryption keys in a dedicated key management system (KMS) rather than configuration files.
Validate that archived indices inherit security mappings from their source data streams.
Conduct periodic access reviews to remove outdated user permissions for archived data.

Module 8: Monitoring, Auditing, and Capacity Planning

Track storage consumption by index, data stream, and lifecycle phase using Elastic metrics.
Set up alerts for low disk space on hot and warm nodes to prevent ILM transition failures.
Measure snapshot duration and success rate to identify repository performance bottlenecks.
Forecast archival storage needs based on ingestion trends and retention policies.
Correlate query performance on frozen data with node resource utilization.
Generate monthly reports on archived data volume, cost, and access frequency for stakeholders.
Conduct disaster recovery drills involving full cluster restore from snapshots.
Review and update archival architecture annually to align with data growth and feature updates.

Module 9: Migrating and Decommissioning Legacy Data

Inventory legacy indices not governed by ILM and assess their archival or deletion eligibility.
Reindex outdated mappings into current templates to ensure compatibility with frozen tiers.
Perform data integrity checks after reindexing to validate document count and field accuracy.
Coordinate decommissioning windows with application teams to avoid disruption.
Document data lineage for migrated indices, including source, transformation steps, and ownership.
Securely erase data from decommissioned nodes using storage-level wiping procedures.
Update data dictionaries and metadata catalogs to reflect archival status of migrated indices.
Archive audit logs of the migration process for compliance and operational reference.