From Bottlenecks to Breakthroughs: Scaling Scientific Data Processing from On-Prem to Cloud for AI-Ready Pharma

Now You Can Scale to 1TB/hour and 5,000 Instruments and Beyond, Which Equates to NASDAQ and the Dow Jones Combined!

Wolfgang Colsman, CEO and Founder, ZONTAL John Conway, CVO and Founder, 20/15 Visioneers 18 June 2025

Executive Summary

Biopharmaceutical companies are struggling to ingest and harmonize their vast volumes of on-premises instrument data to the cloud at scale, leading to inconsistent, fragmented, and non-FAIR (Findable, Accessible, Interoperable, and Reusable) data environments. This lack of scalability and standardization results in significant delays in data availability, analytics, and scientific decision-making.

ZONTAL recently completed a large-scale project for a leading biopharmaceutical client, successfully ingesting and transferring scientific data to the cloud at a sustained, error-free rate of 1 terabyte of data per hour, scaling across 5000 instruments. We are confident that no other FAIR-aligned scientific data platform can make this claim!

As a forward-thinking leader in R&D, be aware of the value that this brings to an organization’s capabilities and ultimately to patients.

Value Proposition

In today's data-intensive life sciences environment, the ZONTAL platform stands alone in its proven ability to scale to extraordinary workloads—achieving data ingestion rates of up to 1TB/hour data ingestion across 5000 instruments distributed across three global regions, all without performance degradation. This breakthrough scalability transforms what was once considered impossible into operational reality for forward-thinking life sciences organizations.

This performance translates into measurable business outcomes across the organization:

1. Operational Efficiency

• Faster Decision-Making: Real-time visibility into instrument output allows rapid adjustments in R&D processes.

• Reduced Downtime: Proactive monitoring enables predictive maintenance and reduces instrument failure or idle time.

• Streamlined Workflows: Automating data ingestion removes manual steps, reducing human error and freeing up scientists’ time for high-value work.

2. Accelerated R&D and Time-to-Market

• Shorter Experiment Cycles: Scientists get immediate feedback on experiments, enabling faster iteration.

• Data-Driven Discovery: Real-time access to comprehensive datasets supports AI/ML modeling, accelerating lead identification and optimization.

3. Improved Data Integrity, FAIRness, and Compliance

• Compliance: Automated ingestion ensures complete, consistent, and traceable data, supporting FAIR data principles.

• Processes Become Cloneable: Standardized and repeatable pipelines promote reproducibility and audit readiness.

4. Scalable Insights for Digital Transformation

• ZONTAL Platform Foundation: Aggregating real-time data from thousands of instruments and migrating to the cloud enables a unified data platform for analytics, AI/ML, and digital twins.

• Enterprise-Level Insights: Enables cross-site benchmarking, quality metrics, and operational intelligence at scale.

5. Cost Optimization

• Resource Optimization: Better use of instrumentation, lab space, and human capital.

• Waste Reduction: Early detection of anomalies reduces wasted materials in experiments and manufacturing.

• Cloud Storage Efficiency: Storage can be optimized in the cloud.

ZONTAL's advantage becomes particularly evident in large-scale deployments where other platforms fall short. When scaling to 5000 instruments and beyond, competing solutions typically experience severe degradation in resource utilization and throughput efficiency. By contrast, ZONTAL maintains near-linear scalability by fundamentally reimagining how FAIR data is ingested, processed, and distributed across infrastructure.

For organizations managing these massive data volumes generated by modern scientific workflows, ZONTAL delivers transformative business value. Scientists can access and analyze data in near real-time rather than waiting hours for processing. Forget about the typical day-plus delay with instrument data acquisition—those days are over. Cross-functional teams gain immediate visibility into experimental results.

Most importantly, the full potential of your distributed computing investment is finally realized—with actual performance that closely matches theoretical capacity. This enables companies to accelerate Design of Experiments, enhancing AI/ML capabilities in record speed, as models can be updated and validated within minutes to hours after data ingestion.

Avoid Scaling Breakdowns

Scaling challenges in scientific environments often stem from the need to handle both massive sequence files and countless small files efficiently. For example, when your infrastructure requires 1-2 minutes to ingest a small file that takes only one minute to process, the math becomes devastating at enterprise scale. These inefficiencies delay time-to-market, reduce research productivity, and ultimately erode shareholder value.

This performance gap has cascading business impacts. Your scientists— whose time represents your most valuable resource—spend hours waiting for or wrangling data instead of advancing research. Critical crossfunctional collaboration between research, clinical, and regulatory teams stalls. Historical data that could inform new therapeutic approaches becomes effectively inaccessible. Most concerningly, your substantial investments in advanced analytics and AI capabilities remain underutilized because the data pipeline cannot deliver insights efficiently.

The cost implications are just as severe. As data volumes grow, organizations typically respond by scaling infrastructure linearly—only to discover that performance does not scale proportionally. This approach leads to runaway infrastructure costs without proportional gains in productivity, placing unsustainable pressure on operational budgets.

For today's life sciences leaders, this isn't merely an IT challenge—it's a strategic vulnerability that threatens your organization's ability to deliver life-changing therapies efficiently.

Solving this requires more than incremental improvements. It demands a fundamentally new approach to laboratory data management—one designed specifically for the scale, complexity, and scientific context of modern R&D workflows.

Ensuring Fast Ingestion-Times in Large Scaled Systems

The ZONTAL platform is a highly scalable system that leverages Kubernetes and the AWS cloud infrastructure. It is built on containerized, microservices-based architecture and makes extensive use of AWS-managed services.

To validate the ZONTAL platform’s performance, we conducted a comprehensive series of stress tests—including a simulation of 1,000 instruments simultaneously dropping off data simultaneously to mimic extreme burst loads with a high concurrency. To reflect real-world conditions and maximize learning, the test plan varied the ratio of small to large files, as well as the provisioned concurrency and auto-scaling metrics. Insights from these tests were used to fine-tune parameters for optimal performance.

In real-world deployments, the ZONTAL platform has demonstrated ingestion times of under 10 seconds and end-to-end data processing times consistently under 90 seconds for smaller files. For larger files (e.g., 1GB), end-to-end data processing times consistently remain under 30 minutes. These workflows include raw data archiving and the creation of FAIR digital assets (data products) for long-term data preservation and reuse.

The creation of the FAIR digital assets involves converting proprietary binary file formats into human-readable, vendor-neutral representations, aligning metadata with public ontologies, and ingesting it into ZONTAL’s Data Hub.

ZONTAL’s Data Hub enables seamless access to the data products via REST APIs, SQL interfaces, and the Model Context Protocol (MCP), supporting real-time data lakes, data science workflows, and AI applications using commonly adopted tools.

Dynamic Auto-Scaling: The Core of ZONTAL's Enterprise Performance

Intelligent Resource Optimization in Real-Time

The ZONTAL platform is engineered with advanced vertical and horizontal auto-scaling as a core capability, enabling virtually limitless scalability that adapts to your organization's dynamic workloads. Unlike traditional systems that require pre-allocated capacity allocation based on anticipated peak usage, ZONTAL's reactive scaling architecture responds in real-time to actual demand—optimizing both performance and cost efficiency.

When instrument data volume increases, the platform automatically provisions additional computational resources within seconds, maintaining consistent processing times even during unexpected surges. This real time responsiveness eliminates the performance bottlenecks that often plague conventional scientific data platforms when faced with concurrent requests from thousands of instruments.

The Complexity of Scaling Down: ZONTAL's Innovative Approach

While scaling up resources in response to demand is relatively straightforward, efficiently scaling down poses significant technical challenges that most platforms fail to effectively address. The ZONTAL platform overcomes this complexity through deep integration with AWS infrastructure and proprietary resource optimization algorithms.

Its advanced scale-down capability monitors multiple system parameters—including queue depths, processing backlogs, and historical usage patterns—to make intelligent, real-time decisions about when and how to relinquish computing resources. This allows the platform to maintain a perfect balance: ensuring sufficient capacity for potential workload fluctuations while aggressively reclaiming unused resources to reduce operational costs.

Global Operations: Meeting the 24/7 Challenge

For multinational pharmaceutical organizations operating across global regions, the ZONTAL platform enables geography-aware resource allocation. This ensures optimal performance for scientists and instruments in each region while efficiently managing the overall infrastructure footprint. The system anticipates load transitions between time zones—provisioning resources ahead of regional workday start times and gradually releasing them as activity decreases.

Measurable Cost Benefits

This intelligent scaling approach delivers substantial financial and operational benefits beyond efficiency:

• Reduced Cloud Costs: Enterprise deployments typically realize 40-60% reduction in cloud infrastructure expenses compared to static provisioning approaches

• Resource Utilization: Average resource utilization increases from industry standard 30-40% to 70-80%

• Predictable Performance: Processing time variability is reduced by 85%, delivering a more consistent and reliable experience for scientists

Technical Implementation

ZONTAL achieves this high level of performance through a multi-layered architectural approach:

1. Containerized Microservices: Each component of the data processing pipeline runs as an independently scalable service.

2. Intelligent Queue Management: Advanced prioritization algorithms ensure critical workflows maintain performance, even during peak load conditions.

3. Predictive Resource Allocation: The platform anticipates workload patterns and provisions resources in advance to avoid delays.

4. Graceful Degradation Protocols: Core functionality is preserved while non-critical processes are temporarily deferred during resource constraints.

The result is a platform that continuously balances performance and cost—expanding and contracting precisely as needed while maintaining the consistent performance that scientific workflows demand.

Global Instrument Load Distribution and Auto-Scaling

These key elements illustrate the system’s behavior, followed by the resulting operational benefits of this approach:

• Predictive Scaling: The system anticipates workload transitions between regions.

• Resource Efficiency: Infrastructure closely matches actual demand rather than being provisioned for a theoretical maximum.

• Cost Optimization: Significant resource reduction during global off-hours.

• Performance Consistency: Maintained processing capabilities regardless of load fluctuations.

This visualization helps demonstrate how the ZONTAL platform intelligently manages resources across a global pharmaceutical R&D enterprise, ensuring both consistent performance for scientists and optimal operational efficiency for the organization.

Technical Implementation: How ZONTAL Achieves Seamless Auto-Scaling

The ZONTAL platform's exceptional scaling capabilities leverage advanced cloud-native architecture and proprietary optimization algorithms. Here's a deeper look at the technical implementation that enables this unprecedented performance:

Kubernetes-Optimized Architecture

The platform utilizes a sophisticated deployment on AWS EKS (Elastic Kubernetes Service) with custom configurations that surpass standard implementations:

• Horizontal Pod Autoscaling (HPA): Custom metrics beyond CPU and memory drive scaling decisions, including queue depths, processing backlogs, and anticipated regional demand shifts.

• Cluster Autoscaler Integration: Seamless node expansion and contraction based on workload requirements.

• Custom Scheduling Policies: Workload distribution optimized for scientific data processing patterns.

Microservice Orchestration

Each component in the ZONTAL data processing pipeline operates as an independently scalable service with specialized configurations:

• File Ingestion Services: Scale rapidly to handle burst uploads from instruments.

• Format Conversion Pipeline: Dynamically allocates resources based on file complexity.

• Metadata Extraction Layer: Scales according to extraction complexity and file types.

• FAIR Data Transformation: Allocates resources based on ontology mapping requirements. • Data Indexing and API Services: Scales to maintain consistent query performance

Data Flow Optimization

The platform incorporates specialized handling for scientific data types:

• Adaptive Chunking: Instrument files are processed in optimized chunks based on their characteristics.

• Parallel Processing Pathways: Different data types follow optimized processing paths.

• Prioritization Engine: Critical workflows maintain performance even during peak loads.

• Backpressure Management: Sophisticated flow control prevents system overload during extreme scenarios.

AWS Infrastructure Integration

ZONTAL's platform integrates deeply with AWS services to deliver optimal performance:

• EC2 Auto-Scaling Groups: Custom warm pools maintain instant scaling capacity.

• SQS Queue Management: Distributed queuing with priority lanes for different workflow types.

• S3 Intelligent-Tiering: Automated storage optimization based on access patterns.

• CloudWatch Metrics: Custom metrics drive scaling decisions using predictive analytics.

Real-World Performance Metrics

From actual enterprise deployments, the ZONTAL platform achieves:

• Scale-Up Response Time: Less than 30 seconds from demand detection to new capacity availability.

• Scale-Down Efficiency: Resources reduced within 2-5 minutes of sustained decreased demand.

• Resource Utilization: Consistently above 70% utilization (compared to the industry average of 30-40%).

• Cost Efficiency: Typically a 40-60% reduction in infrastructure costs compared to static provisioning.

This technical architecture enables ZONTAL to maintain consistent performance for scientists while optimizing infrastructure costs—fundamental capabilities that competing platforms simply cannot match at enterprise scale.

Call to Action

Making IT, Informatics, and AI decisions to deploy critical time-sensitive systems requires trust that the system builders and deployers have a deep understanding of scientific data, processes, and proven test data. ZONTAL has made significant investments alongside its clients and R&D teams to ensure this trust.

Whether you are managing 500 or 5000 instruments, with processing times of 1 minute or 30 minutes, the ZONTAL platform can be trusted and relied upon. Contact us to discover the ZONTAL platform delivers FAIR, reliable, and redundant performance at scale for your biopharma deployments.