Hybrid Cloud Data Platform with Real-Time Tokenization for Multi-Country Banking

TECHNOLOGIES

SERVICES

EXPERTISES

Project Context

Solution

Outcome

About Client
A leading multi-country banking institution operating across the Middle East region with over $400 million in revenue. The bank manages millions of customer accounts across retail banking, corporate banking, card issuing, and merchant acquiring operations – serving a complex, high-volume financial ecosystem where data accuracy and speed directly impact competitive positioning.
Business challenge
The bank faced a critical inflection point: their legacy infrastructure had become a strategic liability, preventing them from competing effectively in an increasingly digital banking landscape.
The infrastructure had reached breaking point. Six-month hardware procurement cycles meant every new analytics initiative, every new product launch, every customer insight request sat in a queue. Business teams operated with 24-hour-old data in an industry where real-time decisioning separates market leaders from followers.
But the real pain ran deeper:
- Fragmented data silos across Oracle and Microsoft SQL databases prevented the organization from seeing the complete customer picture. Different departments maintained conflicting definitions for basic business metrics – what "active customer" meant in retail banking differed from the card issuing team's definition. Executive dashboards showed contradictory numbers depending on which system you asked.
- Semi-manual ETL processes using Qlik created a constant stream of data quality issues. Business decisions were being made on unreliable data, and nobody could confidently say which numbers were correct.
- IT teams drowned in operational firefighting. Every data request required a ticket. Every report needed manual intervention. The backlog measured in weeks, and strategic initiatives took a back seat to keeping legacy systems running.
- Oracle licensing costs spiraled while infrastructure flexibility decreased. Scaling meant more expensive licenses, more hardware, more complexity.
The regulatory trap: While cloud migration promised relief, strict Middle East regulations mandated that sensitive customer data – PII, financial records, card data – must physically remain on-premises. A full cloud migration wasn't just inadvisable; it was legally impossible.
The trigger: The bank couldn't launch new analytics products. Customer-facing innovations stalled. Competitors with modern infrastructure were moving faster, and the gap was widening.

Approach
Binariks recognized this wasn't a simple "lift and shift" migration – it required rethinking the entire data architecture to balance regulatory compliance with cloud innovation.
Discovery and strategic framing: The engagement began with comprehensive assessment – mapping the existing Oracle/MS SQL landscape, categorizing data by sensitivity, and identifying the real bottlenecks beyond infrastructure. Binariks brought together solution architects, cloud specialists, security experts, and data governance professionals to frame the problem properly before writing any code.
The breakthrough insight: Instead of choosing between compliance and cloud, the team designed a hybrid architecture with intelligent data segregation. Sensitive data would remain on-premises within a tokenization vault, while de-identified and non-sensitive data could leverage cloud scalability. Business users would access a unified logical view regardless of where data physically resided.
Team composition strategy: Binariks scaled a 30-40 person team dynamically across 45 months, matching expertise to project phases:
- Early phases: Architects, security specialists, and infrastructure engineers establishing the foundation
- Mid-phases: Data engineers, platform developers, and domain analysts delivering business value
- Later phases: SREs, data scientists, and governance leads hardening and scaling the platform
Phased delivery approach: Rather than a risky big-bang migration, the team established infrastructure foundations, then delivered MVP use cases (card issuing analytics, then acquiring analytics) to prove value quickly. Each phase validated assumptions, refined approaches, and built organizational confidence before expanding scope.
The execution framework:
- Selected specialists who passed rigorous technical and domain-specific interviews
- Established collaborative working models with regular clarification sessions to align on evolving requirements
- Implemented CI/CD practices from day one to enable rapid, reliable deployments
- Built comprehensive monitoring and governance frameworks to maintain system health and compliance
Implementation
Binariks engineered a hybrid cloud data platform that fundamentally transformed how the bank processes, secures, and derives value from data.
Core Architecture
Hybrid cloud with intelligent data segregation:
- Cloud layer (AWS): Non-sensitive and de-identified data processed using S3 data lakes, EMR (Apache Spark) for distributed processing, RDS for structured storage, and DynamoDB for high-throughput metadata management
- On-premises layer: Sensitive data (PII, card numbers, account details) secured within Protegrity's tokenization vault
- Unified access layer: Single logical data view for business users, abstracting physical location complexity
Real-time tokenization for compliance and utility:
- Implemented Protegrity for low-latency (<50ms) tokenization of sensitive fields before cloud transmission
- Format-preserving encryption maintained data utility – analysts could join, filter, and analyze without accessing raw sensitive values
- Secure on-demand de-tokenization in memory for authorized users when business context required original values
- Enabled seamless joining of cloud-based analytical datasets with on-premises sensitive data without compromising regulatory compliance
Cloud-native data processing:
- Migrated compute-intensive workloads (machine learning training, batch analytics, BI queries) from legacy Oracle to AWS EMR with Apache Spark
- Implemented scalable streaming ingestion via Kafka for near real-time transaction processing
- Deployed containerized microservices on Kubernetes for elastic scaling and hybrid portability
- Introduced Presto query engine for fast, cost-efficient ad-hoc SQL queries across S3 data lakes
Unified data governance framework:
- Established enterprise-wide data catalog with standardized business attribute definitions – eliminating the conflicting interpretations that plagued decision-making
- Implemented role-based access control (RBAC) with consistent security policies across hybrid environments
- Created automated data lineage tracking and audit trails for regulatory compliance (PCI-DSS, GDPR principles, AML/KYC)
- Data governance council established with ongoing stewardship processes
Self-service analytics enablement:
- Deployed curated, governed datasets accessible via Tableau dashboards and Jupyter notebooks
- Business analysts query pre-processed data independently – no IT tickets, no weeks-long delays
- Data scientists gained secure sandbox environments with tokenized production data for model development
- 200+ business users now generate insights in minutes instead of submitting requests and waiting days
DevOps transformation:
- CI/CD automation: Jenkins pipelines replaced manual, error-prone deployments. Blue-green deployment strategies enabled zero-downtime releases
- Infrastructure as Code: Terraform modules made infrastructure provisioning repeatable, version-controlled, and peer-reviewed. Provisioning time dropped from weeks to hours
- Container orchestration: Kubernetes enabled consistent deployments across on-premises and cloud with auto-scaling and self-healing capabilities
- Observability: Centralized logging (Splunk) and monitoring (CloudWatch) replaced fragmented tooling. Distributed tracing identified performance bottlenecks before they impacted users
- Secrets management: HashiCorp Vault centralized API keys, credentials, and certificates with dynamic secrets and audit logging
Technology Selection
AWS as cloud platform: Regional data center presence met regulatory requirements. Mature service ecosystem (S3, EMR, RDS, DynamoDB) and existing bank vendor relationship reduced adoption risk. PCI-DSS and ISO 27001 certifications satisfied compliance teams.
Protegrity for tokenization: Real-time performance was non-negotiable. Format-preserving encryption maintained data utility for analytics. Reversible tokenization allowed authorized de-tokenization when needed. Proven banking deployments reduced implementation risk.
Apache Spark on EMR: Scalable distributed processing for big data workloads. Cost-effective compared to proprietary tools. Python support aligned with data science team capabilities. Mature ecosystem with extensive community support.
Kafka for streaming: High-throughput, fault-tolerant real-time ingestion. Industry standard with extensive connector ecosystem. Enabled near real-time analytics replacing batch-only legacy processes.
Kubernetes: Container orchestration for hybrid portability. Enabled microservices architecture with declarative configuration, auto-scaling, and self-healing. Industry standard reducing vendor lock-in concerns.
Phased Implementation
- Phase 1 - Discovery & Architecture Design (6 months): Comprehensive assessment, hybrid architecture design, AWS foundation setup, Protegrity selection, data governance framework definition
- Phase 2 - Infrastructure Foundation (6 months): AWS infrastructure provisioning (S3, EMR, RDS, VPCs), Protegrity vault deployment, secure connectivity establishment (AWS Direct Connect), CI/CD pipeline implementation (Jenkins, Terraform), monitoring setup (Splunk, CloudWatch)
- Phase 3 - MVP: Card Issuing Analytics (7 months): First production use case – migrated card issuing transaction data from Oracle to AWS with tokenization, built Spark-based ETL pipelines, deployed Tableau dashboards, established self-service Jupyter environments. Delivered near real-time transaction visibility and enabled fraud detection models.
- Phase 4 - MVP: Acquiring Analytics (9 months): Scaled platform to merchant acquiring domain – onboarded transaction and merchant master data, extended tokenization rules, built acquiring-specific analytical models, enabled cross-business analytics linking issuing and acquiring for comprehensive customer insights.
- Phase 5 - Hardening & Enterprise Scaling (17 months): Onboarded additional business domains (retail banking, corporate banking, risk), optimized Spark jobs achieving 20-30% cost reduction, implemented auto-scaling policies, strengthened data lineage and metadata management, conducted security audits, decommissioned legacy Oracle databases.

Value Delivered

The hybrid data platform eliminated the constraints that had paralyzed the bank's ability to compete and innovate.
The infrastructure bottleneck – gone.
New data products that previously took 4-6 months now launch in 4-6 weeks (75% faster). The platform processes millions of transactions daily, auto-scaling elastically from 10 to 100+ nodes during peak periods without manual intervention.
Data quality and governance issues – resolved.
The enterprise-wide data catalog with standardized business definitions eliminated conflicting interpretations. Automated data lineage tracking and audit trails transformed regulatory compliance from a painful, manual burden to an automated, auditable process. Compliance effort reduced by an estimated 25%.
IT bottlenecks – eliminated.
Self-service analytics freed the IT team from operational firefighting. Business teams generate insights and reports in minutes instead of submitting tickets and waiting days or weeks. IT effort for reporting tasks reduced by 50%, with capacity redirected toward strategic innovation.
Cost efficiency – delivered.
Migration from expensive Oracle licensing to cloud-native open-source technologies achieved 40% reduction in total infrastructure and licensing costs. Auto-scaling and right-sizing policies optimized AWS spend. DevOps maturity reduced operational overhead by 25%.
Real-time decisioning – enabled.
Business users who previously worked with 24-hour-old data now access near real-time analytical datasets for same-day decision-making. The platform supports streaming transaction monitoring with machine learning models detecting fraud anomalies within seconds.
Measurable business impact:
- 80% faster time-to-insight: Business teams generate reports in minutes instead of hours
- Data latency transformed: From 24-hour delays to near real-time access
- Release frequency doubled: From monthly to bi-weekly deployments through CI/CD automation
- Incident resolution improved 60%: Enhanced observability enabled proactive issue detection
- Platform adoption scaled dramatically:
The foundation for continuous innovation: The platform now powers mission-critical operations across customer analytics (360-degree customer view), real-time fraud detection, automated regulatory reporting (PCI-DSS, AML/KYC), AI/ML development (credit scoring, churn prediction, personalized offers), and rapid product innovation.
Beyond solving the original problem: While the engagement addressed all stated challenges – infrastructure constraints, data quality issues, IT bottlenecks, compliance barriers – the platform delivered additional strategic value discovered during implementation:
- Cross-business analytics capabilities linking issuing, acquiring, deposits, and digital banking for comprehensive customer intelligence
- Machine learning infrastructure enabling data scientists to develop and deploy models at scale
- Regulatory reporting automation transforming compliance from reactive burden to proactive capability
- Market differentiation positioning the bank as a modernization leader among regional competitors
The bank transformed from infrastructure-constrained to innovation-ready – competing effectively in an increasingly digital banking landscape with the agility and scalability that modern financial services demand.

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