SAP HANA to Databricks Migration with Real-Time Streaming
Gulf Marketing Group (GMG), a major Dubai-based retail and distribution company operating across the Middle East, North Africa, and South Asia, was running critical analytical workloads on SAP B4P HANA. While SAP HANA served well as an in-memory database, its rigid architecture limited GMG's ability to leverage advanced analytics, AI/ML capabilities, and real-time streaming at scale. With data volumes growing rapidly alongside business expansion, GMG needed a flexible, cost-effective platform that could handle both batch and real-time workloads. Vrahad Analytics executed a seamless migration to the Databricks Data Intelligence Platform, transforming GMG's analytics capabilities while ensuring zero data loss throughout the transition.
Client
Gulf Marketing Group (GMG)
Cloud Platform
Azure / Multi-Cloud
Duration
5 Months
The Challenge
GMG's reliance on SAP B4P HANA for analytical workloads was creating increasing friction as the business expanded across new markets and product categories. The platform's limitations were becoming a strategic bottleneck for the organization's data-driven ambitions.
SAP B4P HANA's licensing model was becoming prohibitively expensive as data volumes grew, with costs scaling linearly with data size rather than compute usage, making the platform economically unsustainable for GMG's growth trajectory.
The rigid SAP architecture made it extremely difficult to implement advanced analytics and AI/ML use cases — data scientists had to extract data out of HANA into separate environments, creating data silos and governance gaps.
Real-time data processing was limited by HANA's batch-oriented analytical capabilities, meaning retail operations like inventory management and customer insights were always based on stale data.
Operational complexity was growing as data volumes increased, with HANA administration requiring specialized skills that were expensive and difficult to retain in the competitive Dubai market.
No native support for streaming data ingestion meant that point-of-sale transactions, e-commerce events, and supply chain updates had to be batch-loaded, creating blind spots in operational visibility.
Data governance across GMG's multi-country operations was fragmented, with no unified catalog or lineage tracking, making it difficult to comply with varying data protection regulations across different markets.
Our Solution
We executed a seamless migration from SAP B4P HANA to the Databricks Data Intelligence Platform, designing a modern architecture that leverages Delta Live Tables for declarative pipeline management, Structured Streaming for real-time ingestion, and Unity Catalog for unified governance across all of GMG's markets and business units.
Designed a comprehensive migration strategy that mapped every SAP HANA table, view, and stored procedure to equivalent Databricks constructs, ensuring functional parity before cutover while identifying opportunities for improvement.
Implemented Delta Live Tables (DLT) for all batch and streaming pipelines, replacing complex custom orchestration with declarative pipeline definitions that automatically handle dependencies, retries, and data quality expectations.
Built a real-time streaming layer using Structured Streaming that ingests point-of-sale transactions, e-commerce events, and supply chain updates with sub-second latency, enabling real-time inventory and customer analytics.
Deployed Unity Catalog as the centralized governance layer across all of GMG's business units and markets, with fine-grained access controls, automated lineage tracking, and audit logging for regulatory compliance.
Created automated operational tooling for pipeline monitoring, alerting, and self-healing — including automatic retry with exponential backoff, dead-letter queue management, and Slack/Teams notifications for pipeline failures.
Implemented a phased cutover strategy that ran HANA and Databricks in parallel during transition, with automated data comparison validators ensuring consistency between the two platforms before final switchover.
Implementation Phases
Platform Assessment & Migration Planning
4 WeeksInventoried all SAP HANA objects (tables, views, procedures, scheduled jobs), assessed data volumes and dependencies, designed the target Databricks architecture, and created a detailed migration runbook with risk mitigation strategies.
Databricks Platform Setup & DLT Design
3 WeeksProvisioned the Databricks workspace with Unity Catalog, designed Delta Live Tables pipeline definitions for all workloads, configured cluster policies and compute resources, and established the monitoring framework.
Data Migration & Validation
6 WeeksMigrated all data from SAP HANA to Delta Lake tables in batches, with automated row count, checksum, and schema validation at each step. Ran parallel comparison queries to ensure data consistency between source and target.
Streaming Layer Implementation
4 WeeksBuilt the real-time streaming pipelines using Structured Streaming, configured event source connectors, implemented exactly-once processing guarantees, and validated end-to-end latency against SLA requirements.
Parallel Run & Cutover
3 WeeksRan both platforms in parallel with automated consistency checks, gradually shifted analytical workloads to Databricks, executed final cutover with zero downtime, and decommissioned SAP HANA analytical components.
Technologies Used
Key Results
Measurable outcomes and business impact delivered through this engagement.
Completed full SAP HANA to Databricks migration with zero data loss, verified through automated row-count and checksum validation across every migrated table and view.
Enabled near real-time data ingestion and analytics for retail operations, reducing data latency from hours to sub-second for point-of-sale and e-commerce events.
Reduced operational complexity significantly through DLT's declarative pipeline approach, replacing hundreds of lines of custom orchestration code with concise, self-documenting pipeline definitions.
Established unified governance across all data assets via Unity Catalog, providing complete lineage visibility, fine-grained access controls, and audit trails for multi-market regulatory compliance.
Positioned GMG for scalable growth with a flexible, cost-effective platform that scales compute independently of storage, reducing total cost of ownership compared to SAP HANA.
Unlocked advanced analytics and AI/ML capabilities natively within the Databricks platform, enabling GMG's data science team to build models directly on production data without extraction.
Before vs. After Comparison
Data Latency
Before
Hours (batch)
After
Sub-second
Real-time
Pipeline Complexity
Before
Custom orchestration
After
Declarative DLT
80% less code
Data Loss
Before
N/A
After
Zero
100% integrity
Governance
Before
Fragmented
After
Unified Catalog
Full lineage
Platform Cost
Before
High (SAP license)
After
Pay-per-use
Significant savings
ML Capability
Before
External only
After
Native platform
Fully integrated
Migration Scale — Objects Migrated
Workload Distribution on Databricks
Platform Capability — SAP HANA vs Databricks
Cumulative Objects Migrated Over Time
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