What Your Team Actually Wants to Know

FAQs

• What is legacy data infrastructure modernization?

  Legacy data infrastructure modernization is the process of migrating outdated data systems (typically 10-15+ years old) to modern cloud-native platforms and tooling. This includes migrating legacy ETL processes, on-premise data warehouses, proprietary data pipelines, and custom-built data integration systems to modern frameworks like Apache Airflow, dbt, Snowflake, or Databricks.

  Most enterprises have legacy data infrastructure built with tools like Informatica, Perl scripts, Shell scripts, or proprietary ETL systems that were appropriate when built but now consume excessive engineering resources to maintain. Modernization enables real-time data processing, reduces infrastructure costs, and creates the foundation for AI and machine learning initiatives.

  Common legacy systems we migrate: Informatica PowerCenter, custom Perl/Shell ETL, legacy Oracle/Teradata data warehouses, on-premise Hadoop clusters, proprietary data integration platforms, legacy SCADA historians (manufacturing), and undocumented custom pipelines.

• How long does a legacy ETL migration typically take?

  Most legacy ETL migration projects complete in 6-12 weeks depending on complexity:

  6-8 weeks: Migrations involving 20-40 ETL jobs with standard transformations and documented logic
  8-10 weeks: Infrastructure consolidation projects migrating 2-3 legacy systems to unified cloud platform
  10-14 weeks: Complex migrations with 100+ ETL jobs, multiple source systems, or regulatory compliance requirements (HIPAA, SOC 2, financial services)

  Timeline factors that add complexity: undocumented legacy code requiring reverse-engineering, compliance requirements needing audit trails, high-volume data processing (1M+ records/day), and mission-critical systems with zero-downtime requirements.

  Our approach uses parallel-run validation where old and new systems operate simultaneously for 2-8 weeks, allowing thorough testing before production cutover.

• What does zero-downtime migration mean for data infrastructure?

  Zero-downtime migration means business operations continue without interruption during the infrastructure transition. We achieve this through parallel-run architecture:

  Phase 1 (Weeks 1-2): Audit existing infrastructure, design modern replacement, document all dependencies
  Phase 2 (Weeks 3-6): Build new infrastructure while legacy system continues serving production
  Phase 3 (Weeks 4-8): Run both systems in parallel, validate outputs match exactly (automated comparison)
  Phase 4 (Week 8+): Production cutover during low-traffic window with 1-hour rollback capability

  During parallel validation, the legacy system remains the source of truth for business operations. New system processes duplicate data streams for validation only. Cutover happens only after proving complete output parity.

  Example: Sharecare’s migration involved processing 500K+ healthcare records daily. Legacy Perl pipelines ran production for 8 weeks while we validated the new Python/Airflow implementation. Business users experienced zero disruption.

   

• Can you migrate legacy systems with no documentation?

  Yes, most of our projects involve undocumented legacy systems. Typical scenario: custom ETL built 10-15 years ago, original engineer no longer with company, minimal documentation, business-critical operations depend on it.

  Our reverse-engineering process:

  1. Code analysis: Review legacy scripts/jobs to understand transformation logic
  2. Data lineage mapping: Trace data flows from sources through transformations to destinations
  3. Output validation: Run test datasets to document actual behavior
  4. Interview stakeholders: Gather tribal knowledge from anyone who’s touched the system
  5. Document thoroughly: Create comprehensive documentation before rebuilding

  Example: Sharecare’s 15-year-old Perl/Shell ETL had zero documentation and the original engineer had left years prior. We reverse-engineered the logic, documented it completely, then rebuilt in Python/Airflow. Their team now has full documentation and can modify pipelines themselves.

  Undocumented systems require additional discovery time (typically 1-2 extra weeks) but are fully migratable.

• What compliance requirements do you support for data migrations?

  We support enterprise compliance requirements including:

  Healthcare: HIPAA compliance for protected health information (PHI), including encryption, access controls, audit trails, and Business Associate Agreements (BAA)

  Financial Services: SOC 2 Type II controls, financial services regulatory requirements, data residency compliance, audit trail documentation for regulatory review

  General: GDPR for EU data processing, data classification frameworks, encryption at rest and in transit, role-based access controls (RBAC), audit logging for all data access

  All migrations include comprehensive documentation for compliance audits: data lineage diagrams, security control mapping, access logs, validation reports, and change management documentation.

  Example: Financial services client’s migration passed regulatory audit post-implementation. We provided complete audit trail showing parallel validation methodology, data integrity verification, and security control implementation.

• What happens if the migration fails or data doesn't match?

  All migrations include rollback procedures and validation protocols:

  Parallel-run validation (2-8 weeks): Both old and new systems process production data. Automated comparison validates outputs match exactly. We identify discrepancies before cutover.

  Production cutover window: Migrations happen during low-traffic periods with 1-hour rollback capability. If issues arise, we revert to legacy system within minutes.

  Post-cutover monitoring (30-60 days): Intensive monitoring period with engineering support to address any issues immediately.

  In practice, failures are extremely rare because parallel validation catches issues before production cutover. During Sharecare’s migration, we identified 3 edge cases during parallel validation that would have caused data discrepancies—fixed them before cutover, resulting in flawless production deployment.

  If validation reveals the new system can’t replicate legacy behavior, we extend parallel-run period or re-architect approach. You’re never forced to cutover before proving it works.

• Do you migrate on-premise data warehouses to cloud?

  Yes, we migrate on-premise data warehouses (Oracle, Teradata, SQL Server, legacy Hadoop) to modern cloud data platforms including Snowflake, Databricks, Google BigQuery, Amazon Redshift, and Azure Synapse.

  Typical migration includes:

  • Schema redesign for cloud-native architecture
  • ETL/ELT pipeline migration to modern frameworks
  • Query and reporting workload migration
  • BI tool reconnection (Tableau, Power BI, Looker)
  • Performance optimization for cloud environment
  • Decommissioning on-premise infrastructure

  Cost savings from warehouse consolidation typically cover project costs within one quarter. Example: Healthcare payer migrated from on-premise Informatica + Oracle warehouse ($60K/month infrastructure) to Databricks ($20K/month), saving $480K annually.

• Can our team maintain the infrastructure after migration?

  Yes, knowledge transfer is built into every project. Our approach:

  During migration (Weeks 1-8): Your engineers work embedded with our team, learning new infrastructure as we build it

  Documentation deliverables: Complete technical documentation including architecture diagrams, data flow documentation, runbooks for common operations, troubleshooting guides, and monitoring/alerting setup

  Training: Hands-on training for your team on new infrastructure, covering pipeline modifications, deployment procedures, monitoring tools, and incident response

  Support period (30-60 days post-cutover): We remain available for questions and issues but expect your team to operate the infrastructure with our guidance

  Goal is independence. Example: Sharecare’s team hasn’t needed our involvement in 6+ months post-migration—they operate, modify, and extend the infrastructure themselves.

• What modern data stack tools do you use for migrations?

  We select tools based on your requirements, existing infrastructure, and team expertise. Common modern data stack components:

  Orchestration: Apache Airflow, Prefect, Dagster
  Transformation: dbt (data build tool), SQL-based transformations
  Cloud data platforms: Snowflake, Databricks, Google BigQuery, Amazon Redshift
  Data integration: Fivetran, Airbyte, custom Python connectors
  Monitoring: Datadog, Monte Carlo, Great Expectations for data quality
  Version control: Git-based infrastructure-as-code (Terraform, Pulumi)

  We don’t force specific vendor lock-in. If you’re already invested in Databricks, we work within that ecosystem. If you need vendor-neutral approach, we architect with open-source tools (Airflow, dbt).

  Technology decisions documented and explained during discovery phase—you approve architecture before implementation.

• Do you work with companies outside the United States?

  Yes, we work with enterprises globally including US, Canada, Europe, and Asia-Pacific regions. All work is conducted remotely with collaboration during your business hours.

  Data residency requirements (EU data must stay in EU, etc.) are supported through regional cloud deployment. Compliance with local data protection regulations (GDPR, CCPA, etc.) included in architecture design.