Overview

Thank you for your interest in a tailored Apache Iceberg implementation. Based on our conversation, you need a robust, petabyte-scale table format that works seamlessly across your multi-cloud (GCP & AWS) and on-premises environments. You’ve also highlighted critical requirements: handling over 3 PB of data, guaranteeing sub-2 second query SLAs, and migrating numerous Spark-based pipelines onto Iceberg. This personalized proposal outlines how we can achieve these goals, drawing on our extensive Data Modernization experience at scale.

Why Apache Iceberg?

• Scalable Metadata Management: Iceberg’s hidden-partition and snapshot-isolation features ensure catalog size remains manageable, even with trillions of partitions.
• Time-Travel & Rollback: With native support for time-travel queries, you can audit and rollback data states without complex ETL.
• Multi-Engine Compatibility: Works with Spark, Flink, Presto, Trino, and other engines, enabling a unified architecture for your existing pipelines.
• Performance & Cost Efficiency: Optimized small-file handling, compaction, and predicate pushdown ensure sub-second reads and keep cloud compute costs under control.

Proposed Approach

We recommend a four-phase engagement combining discovery, design, implementation, and optimization. This phased approach ensures transparency, risk mitigation, and continuous validation against your SLA targets.

Phase 1: Discovery & Assessment

• Review your current data landscape on GCP, AWS, and on-prem.
• Analyze existing Spark jobs, data volumes, storage formats, and performance baselines.
• Define success metrics: 99.9% availability, sub-2s SLA, data consistency checks.
• Deliverable: Detailed assessment report with migration roadmap.

Phase 2: Architecture & Design

• Design Iceberg catalog integration (Hive, AWS Glue, Azure Data Catalog, or custom metastore) and secure access patterns.
• Define partitioning strategies, file layout, and snapshot retention policies to meet your query SLA at 3 PB scale.
• Set up CI/CD pipelines for table DDL/DML changes, leveraging Spark on Databricks or EMR for orchestration.
• Deliverable: Detailed architecture diagrams, data flow specifications, and governance plan.

Phase 3: Implementation & Validation

• Migrate a representative subset of Spark pipelines to Iceberg tables on your storage layer (GCS, S3, on-prem HDFS or object storage).
• Validate data accuracy, run performance benchmarks, and adjust compaction/partition settings.
• Conduct user acceptance testing and integrate automated smoke tests into your CI/CD.
• Deliverable: Migration runbooks, test results, and go-live checklist.

Phase 4: Optimization & SLA Assurance

• Monitor query performance using built-in metrics and cloud monitoring tools.
• Implement auto-compaction, metadata pruning, and resource-based scaling to guarantee sub-2s SLAs.
• Provide a knowledge transfer workshop for your teams on Iceberg best practices.
• Deliverable: Performance tuning report, operational playbook, and ongoing support guidelines.

Next Steps & Discovery Workshop

Your discovery workshop has been scheduled for Monday at 2:00 PM CEST. On the personalized page, you’ll find a “Join Workshop” button you can use to connect directly with our delivery lead. During this session, we’ll review your assessment findings, refine the architecture, and align on detailed milestones.

Indicative Budget Overview

Based on similar multi-cloud Iceberg engagements at petabyte scale, we provide a high-level cost outline covering architecture design, migration engineering, and SLA-driven performance optimization. Detailed budget ranges will be refined during or after the discovery workshop once we validate scope and resource requirements.