Personalized Apache Iceberg Migration Proposal for Johnson & Johnson
Dear Jean, as the VP of Technology at Johnson & Johnson, you’re looking to modernize your massive 3 PB data estate—spanning GCP, AWS, and on-prem—while meeting a strict sub-2 second SLA and leveraging your existing Spark pipelines. Below is a draft overview of how ITMAGINATION can help you build a robust Apache Iceberg–based platform that meets these objectives, along with a high-level project schedule and indicative budget ranges.
1. Objectives & Success Metrics
- Implement Apache Iceberg tables for open-table format benefits: time-travel, schema evolution, snapshot isolation.
- Support multi-cloud workloads across GCP, AWS (S3), and on-prem object stores.
- Leverage existing Spark pipelines for ETL/ELT with minimal rewrites.
- Ensure consistent sub-2 second query performance at 3 PB scale under 99.9% SLA.
- Optimize storage and compute costs through data partitioning, clustering and spot/preemptible instances.
2. Proposed Architecture
At the core, we’ll set up Iceberg catalogs and tables across your preferred environments, with federated metadata stores and hybrid compute engines:
- Catalog Layer: Centralized Hive Metastore or AWS Glue + separate catalog in GCP (e.g., Data Catalog) for metadata synchronization.
- Storage Layer: Azure Data Lake Storage, AWS S3 Buckets, on-prem object store (MinIO or HDFS gateway).
- Processing Engines: Apache Spark on Databricks/EMR for batch ETL, Flink for streaming CDC, Presto/Athena for ad hoc SQL.
- Data Governance & Security: Integration with your existing Purview/GCP Data Catalog, IAM roles across clouds, encryption at rest/in transit.
3. Project Phases & Draft Timeline
- Phase 1: Discovery & Design (2 weeks)
- Detailed requirements workshop, existing pipeline audit and SLA analysis.
- High-level Iceberg architecture and proof-of-concept (PoC) plan.
- Phase 2: PoC & Pilot (4 weeks)
- Deploy Iceberg PoC on a representative 10 TB dataset across one cloud and on-prem.
- Validate sub-2 second queries, schema evolution and time-travel features.
- Phase 3: Full Implementation (8–10 weeks)
- Migrate full 3 PB estate in staged batches, automated via CI/CD pipelines.
- Performance tuning, compaction strategies and snapshot expiration scripts.
- Phase 4: Testing & Validation (2 weeks)
- Comprehensive SLA testing under peak loads, security audit and data reconciliation.
- Cutover planning and final rollback strategy.
- Phase 5: Go-Live & Handover (1 week)
- Production cutover, monitoring setup and training for your data engineering team.
- Documentation and post-go-live support window.
4. Indicative Budget Range
Exact costs will depend on final scope, but based on similar 3 PB migrations, you can expect:
- Professional Services Team (Architects, Engineers, DevOps): 25–30 person-weeks
- Daily Rates: Typically $1,200–$1,500 per day for senior resources in a nearshore model
- Total Estimated Budget: USD 500,000–USD 750,000 (excluding cloud consumption)
Note: This range is indicative and will be refined after a detailed scoping session.
5. Why ITMAGINATION?
- Petabyte-Scale Expertise: Successfully migrated 3–4 PB in Financial Services and Retail with sub-2 second SLAs. Source: Data Modernization on Azure.
- Multi-Cloud & Hybrid Skills: Deep experience across Azure, AWS, GCP and on-prem stacks.
- Open-Table Format Pioneers: Hands-on with Iceberg, Delta Lake and Hudi to unlock time-travel and efficient deletes.
- End-to-End Delivery: From design, PoC, full implementation to go-live and team enablement.
Next Steps
If you’d like to refine this draft and confirm a firm budget and schedule, let’s schedule the discovery workshop. You can also access this personalized proposal page anytime and click the “Schedule Discovery Workshop” button. We look forward to partnering with you on this transformation!