Enterprises today operate across a complex landscape of platforms, clouds, and geographies, where the ability to move data seamlessly and securely has become a defining factor for agility and resilience. True data portability is no longer a nice-to-have; it is essential for improving customer experience, ensuring compliance, reducing costs, and avoiding vendor lock-in. Yet as volumes grow from terabytes to petabytes, the technical, organizational, and cultural challenges of data portability continue to test even the most sophisticated companies.
To explore what it takes to overcome these barriers, we spoke with Sai Vishnu Kiran Bhyravajosyula, staff software engineer at Rippling. With more than a decade of experience designing large-scale distributed systems and data platforms at companies like Salesforce, Confluent, Uber, and Amazon Web Services, Sai brings deep expertise in storage systems, developer productivity tooling, and multi-cloud reliability at scale. He also holds multiple U.S. patents in network optimization and content access technologies. In this conversation, he shares insights on building fault-tolerant migration systems, tackling both technical and organizational obstacles, and what the future of global data portability will look like.
From your perspective, what does “true data portability” mean for enterprises operating across multiple platforms, clouds, and geographies?
True data portability for enterprises operating across multiple platforms, clouds, and geographies means enabling data migration that serves business needs such as improving customer experience, ensuring fault tolerance, managing costs, and maintaining security and compliance.
Enterprises expect seamless transfer across systems, where data can move between applications, platforms, and cloud providers without significant rework, proprietary lock-in, or loss of fidelity. They also require strong security measures, ensuring that every solution complies with the organization’s security standards while data is in transit.
Governance and compliance play an equally important role. Solutions must respect both legal and organizational requirements, supporting lawful cross-border transfers and providing auditable controls for security, privacy, and regulatory standards. Finally, true data portability avoids vendor lock-in, giving enterprises the flexibility to choose or change platforms and services as their strategy or technology evolves.
What are the most difficult technical barriers organizations face when moving terabytes or even petabytes of data reliably across systems, and how have you approached solving them in your career?
Moving large volumes of data comes with interesting distributed challenges like ensuring integrity and consistency, minimizing downtime, dealing with legacy or incompatible formats, managing security and compliance, and maintaining performance under heavy loads.
Technical barriers when moving terabytes or petabytes of data
- Ensuring integrity and consistency of data during transfer
- Minimizing downtime while systems remain in use
- Handling legacy or incompatible formats
- Managing security and compliance requirements
- Maintaining performance under heavy loads
Approaches to solving these challenges
- Sharding data by tenant to enable batch migrations per data source
- Using incremental and parallel batched migration to increase throughput while enforcing strict access controls
- Implementing periodic checkpoints and logging for observability into the transfer progress
- Leveraging distributed orchestration systems like Temporal or Cadence, along with CDC and event systems such as Kafka or SQS for real time updates
- Validating migrations through checksum comparisons, periodic workflows, and feature flags to ensure source and destination data match
- Establishing metrics and monitoring for lag, errors, and load, with alerts and on call tools to resolve issues quickly
- Coordinating across teams with structured communication and automated event emission for alignment throughout migration
Moving large volumes of data is a risky process. What best practices or architectural principles ensure fault tolerance and data accuracy during these transfers?
Best practices and principles for fault tolerance and data accuracy in large-scale transfers include:
- Conduct pre-migration validation to estimate scale, map against system limits, and define batches for migration
- Use incremental and parallel batched migration so throughput improves and failures in one batch do not impact others
- Leverage fault-tolerant orchestration systems like Temporal or Cadence, along with CDC and event systems such as Kafka or SQS, to manage parallel transfers with retries and automated bookkeeping
- Implement periodic checkpoints and logging to track progress and provide observability into data movement
- Validate migrated data through checksum comparisons, periodic verification workflows, and feature flag-based dual reads to confirm source and destination consistency
- Establish robust metrics and monitoring for system health, CDC lag, load, and errors, with alerts and on call tools to quickly address migration issues
Beyond technology, what organizational or cultural challenges have you seen that make global data portability difficult, and how can enterprises overcome them?
Enterprises often face organizational and cultural challenges that make global data portability difficult. One common issue is communication and distributed ownership. Enterprise data is often stored in different systems that are owned by various teams, and the lack of collaboration and communication across these teams during migration creates significant obstacles.
Another challenge is inconsistent tooling. Many teams focus on building portability solutions that are tailored to their own systems without considering the broader need for data portability across the entire organization. This fragmented approach prevents enterprises from achieving seamless migration.
To address these challenges, enterprises should encourage cross-functional collaboration by holding regular synchronization sessions and appointing a dedicated single point of contact to oversee the overall migration. They should also develop consistent tooling that can be used across teams and incorporate technologies such as eventing and distributed orchestration to ensure smoother data portability.
Having worked at companies like AWS, Uber, Confluent and Salesforce, can you share a specific example of designing a data system where reliability and portability were especially critical?
I recently worked at Salesforce, where one of our large enterprise customers needed to migrate their entire data footprint from one region to another to meet strict data residency compliance requirements. This initiated a request to move all the customers’ data across Salesforce services, particularly Service Cloud. The key challenge was migrating massive volumes of data within a very limited timeframe, while ensuring zero data loss and minimal downtime, as their mission-critical workflows required uninterrupted access to the platform. On top of this, because Salesforce operates on a multi-tenant architecture, the migration had to be executed without affecting other customers. To achieve this, I designed a solution that combined incremental replication, change-data-capture pipelines, and checkpoint-based validation.
Many enterprises are pursuing multi-cloud strategies. How does data portability fit into this vision, and what pitfalls should organizations avoid?
Many enterprises are pursuing multi-cloud strategies because this approach allows them to select the best services across providers and to expand into new markets more effectively. Data portability is central to this vision since it enables organizations to move and integrate data across different cloud environments without losing flexibility or control.
However, there are several pitfalls that organizations must avoid. Inconsistency is a major challenge, as each cloud provider employs different schemas, security models, and APIs, which create interoperability issues and complicate migration. Vendor lock-in is another risk, since proprietary tools and formats can make it difficult to extract or transfer data, reducing the benefits of a multi-cloud approach. Compliance and security risks also arise, as moving sensitive data across jurisdictions may lead to privacy breaches or regulatory violations if not carefully managed. Operational complexity is another concern, as managing data across multiple clouds increases the likelihood of configuration errors, downtime, and security vulnerabilities. Finally, performance and cost must be considered, since data transfers between clouds can cause latency and lead to high egress fees that undermine both efficiency and cost predictability.
To overcome these challenges, enterprises should adopt abstraction tools, data lakes, and containerization technologies to enable interoperability across clouds. They should embrace open source solutions and open standards whenever possible. Careful planning of migrations and integrations, with strong validation, robust security measures, and regulatory awareness, is essential. In addition, centralizing monitoring and observability can help track data movement and system health across multiple cloud environments.
You hold patents in network optimization and content access technologies. How do these innovations tie into the broader goal of enabling seamless data movement at scale?
My patent on “Model-based selection of network resources for which to accelerate delivery” can be used to selectively fetch the needed resources in advance, and the system reduces redundant or reactive data requests, lowering network congestion and bandwidth use. This helps in moving the data fast.
We can also enhance the model proposed in the patent to be able to predict/pick the optimized workload per migration based on various factors.
Looking ahead, what emerging technologies or approaches do you believe will redefine how enterprises achieve global data portability over the next five years?
In the coming years, global data portability will be redefined by the rise of universal data planes and hybrid cloud integration, where standardized formats, global metadata layers, and abstraction software eliminate boundaries between on-premises and cloud environments to enable seamless and vendor-neutral movement. At the same time, AI-powered systems will take a leading role in automating workload planning and optimization, making data migration more proactive, efficient, and reliable.
