Data migration is a critical and recurring process in most data-centric projects. It involves transferring data seamlessly between systems, whether from external platforms to organizational databases, between systems within the same organization, or across databases within a single application. This delicate process ensures data integrity while enhancing clarity in the target systems. A typical data migration journey includes several phases: field-level mapping to the target system, data modeling for the target environment, data cleaning, validation, processing, reconciliation, and comprehensive reporting.
With nearly 18 years of expertise in successful data migrations, I have witnessed the evolution of tools and methodologies in this domain. However, not all migrations can rely on a one-size-fits-all approach. Each project presents unique challenges influenced by factors such as business requirements, project budget, timelines, data volume, frequency of migration, criticality of the data, and the feasibility of available tools and technologies.
In my current role I design software engineering solutions for an industry-leading business that specializes in providing comprehensive insurance protection for customers across numerous prominent countries. Subscriber enrollments and claims data are critical to our operations, forming the foundation for delivering exceptional services and maintaining business continuity. To support these processes, we rely extensively on robust relational databases, including Oracle (News - Alert) and PostgreSQL. Given the dynamic nature of our industry, our business frequently involves mergers and acquisitions with other vendors and firms, as well as adhering to legal mandates for seamless data transfers between systems. As a result, data migration is not just an occasional task, but a continual and integral part of our business strategy, ensuring smooth integration and compliance while supporting growth and scalability.
Selecting the right approach is vital for achieving a fast, scalable, and cost-efficient migration solution. In this context, I designed and implemented a cloud-based scalable data migration framework, which leverages cutting-edge technologies to address the growing demands of modern data projects. This framework integrates advanced tools and methodologies to ensure efficiency, flexibility, and accuracy. The following two approaches from recent projects showcase the technological shift in data migration over time.
Traditional data migration project
A few years ago, our team successfully migrated millions of mobile carrier records from an external system into the company’s relational database to support new protection programs and streamlined claims processing. The source data was delivered in data files, with formats differing significantly from the target system. Each customer record required extensive processing to create a complete subscriber profile in our claims system, spread across multiple schemas and tables. The migration project involved intensive collaboration with business stakeholders, application users, architects, and subject matter experts to establish precise field-level mappings. The objective was to seamlessly integrate data into the existing system without introducing new tables or columns and to avoid application changes.
The solution we implemented employed a conventional data migration approach which is foundational for data migration in a relational database, leveraging these components:
·Data ingestion: data files were transferred to the organization’s secured file transfer (SFTP) system. Using SQL*Loader, control files and Shell scripts, the source data was imported into preprocessing tables, following the predefined field-level mappings.
·Data validation and cleaning: Oracle PL/SQL stored procedures and packages performed rigorous data validation and cleansing, ensuring only high-quality data progressed further.
·Profile setup: leveraging global temporary tables, Oracle PL/SQL and bulk collect functionality; the cleaned data was transformed into actionable entities for creating complete subscriber profiles.
·Job scheduling: Oracle Scheduler or Tivoli Scheduler was utilized to automate and manage tasks at predetermined intervals.
During this process, we encountered various challenges, starting with file movement dependencies. We found that file transfers between the SFTP folder and the processing server relied heavily on UNIX shell scripts, which restricted access to production servers and required administrative intervention for file status validation. Another problem was limited access for monitoring. Developers faced constraints in monitoring initial data loads, since the SQL Loader was installed on the database server, and thus required Database Administrator’s (DBA) permissions.
Additional issues created by this conventional methodology included intensive resource utilization and job scheduling complexities. Large datasets resulted in significant resource consumption during data loading and execution of business logic. Extensive performance tuning was necessary to mitigate resource strain, along with scheduled downtime to prevent table locks. Furthermore, task scheduling was heavily dependent on Oracle Scheduler or third-party scheduling mechanisms, which presented operational limitations.
Despite these challenges, the solution successfully achieved the migration goals, showcasing the efficiency and reliability of a traditional relational database solution.
Cloud-based data migration framework: a modern approach
More recently, our team undertook a two-year incremental data migration effort, transitioning subscriber data from a vendor system into the company’s cloud-based platform. This included subscriber profile setup and dependent enrollment and claims data setup for various mobility carriers, ensuring a seamless customer experience after strategic system consolidations. Manual execution and monitoring for such a scale were not sustainable. Moreover, inconsistent batch volumes and stringent client requirements for a high success rate demanded an innovative and scalable solution.
To address these challenges, I designed a cloud-native data migration framework leveraging Amazon Web Services’ (News - Alert) (AWS) powerful tools and services. This modern approach prioritized scalability, automation, and accuracy, significantly improving operational efficiency.
In this innovative framework, six components proved to be transformative. These features included:
- Data storage and accessibility: Amazon S3 replaced the traditional file storage system, providing secure and scalable file storage. The stored files offered instant access, enabling immediate validation, or sharing without requiring downstream processing. Integration with Delta Lake enabled an ELT (Extract, Load, Transform) approach, ensuring efficient data handling and integrity.
- Dynamic data mapping: DynamoDB replaced traditional control files, serving as a dynamic repository for field-level mappings. This simplified maintenance and reduced dependencies on static configurations.
- Data validation and quality checks: Using Amazon Athena, we could quickly visualize data for format issues, enabling proactive identification and communication of discrepancies before further processing.
- ETL process automation with AWS Glue: AWS Glue streamlined the ETL processes, with effortless scheduling and deployment of jobs. Spark-based data partitioning was dynamically managed using the
repartition ()method, ensuring optimal resource utilization and scalability. AWS Glue version 3.0 introduced auto-scaling capabilities, allowing the system to adjust resources dynamically based on workload requirements, eliminating over-allocation while ensuring efficiency. - Big data processing: Data validation and business logic execution shifted to Apache Spark and Scala, utilizing in-memory computations for faster processing. The final transformed data was directly prepared for target systems, minimizing the load on relational databases.
- Optimized workload management and monitoring: Auto-scaling with AWS Glue allowed for resource flexibility during peak data loads, while worker configurations ensured the platform met project-specific needs. Monitoring with Kibana proved to bes very effective in tracking the progress of the data migration and the alerts, and using elastic search enabled us perform quick troubleshooting.
Key advantages
This new framework transformed the data migration process into an efficient, scalable, and reliable system capable of handling dynamic workloads over extended periods. Among the many advantages it produced, enhanced scalability and efficient data validation were key: automatic resource scaling addressed fluctuating batch sizes effortlessly, and the immediate identification of discrepancies ensured compliance with client requirements.
With this new framework, we also achieved streamlined operations, improved efficiency, and cost optimization. The elimination of manual interventions significantly reduced human dependency and errors, and faster data processing reduced migration timelines while maintaining accuracy. Positive results were also produced by flexible resource allocation, which avoided over-provisioning, therefore optimizing costs.
This cloud-based data migration framework not only addressed the unique challenges of the project, but also set a benchmark for scalability and operational excellence in large-scale data migrations. We successfully eliminated two years of manual effort—equivalent to the workload of two full-time employees—which translated to $300,000 USD in labor cost savings, alongside the numerous additional benefits achieved through the implementation.
Sanjay Puthenpariyarath is recognized as an expert data engineer for his original contributions and success in designing and implementing scalable data architecture solutions for Fortune 500 companies in the banking telecom, and e-commerce industries. For nearly two decades, he has specialized in big data processing, data pipeline development, cloud data engineering, data migration, and database performance tuning, using cutting-edge technologies that have enabled him to optimize data workflows and achieve significant improvements in financial and operational outcomes. Sanjay received a Bachelor of Engineering degree in Electronics and Communication Engineering from Anna University, India, and earned a Master of Science degree in Information Technology from the University of Massachusetts, Lowell (US). As a senior leader, he enjoys mentoring data engineers, promoting data-driven organizational cultures, and delivering complex projects on time and within budget.
