Area of expertise

Large-scale Data Processing (Big Data)

Investment opportunities in the Big Data processing market are driven by growing demand for data-driven decision-making tools and advanced analytics across industries, including healthcare, e-commerce, and finance.

Market

The global Big Data market

Demonstrates robust growth trajectories across multiple segments.

Emerging markets, particularly in Asia-Pacific and Latin America, are positioned for significant growth, offering investors opportunities to capitalize on expanding digital economies and the rise of data-driven businesses.

The Big Data processing and distribution software market was valued at $15.79 billion in 2024 and is projected to reach $48 billion by 2032, indicating substantial expansion driven by increasing data volumes and sophisticated processing requirements.

The distributed database market, valued at $12.5 billion in 2023, is projected to reach $28.6 billion by 2032, registering a compound annual growth rate of 9.6%.

21,4%

CAGR

Exceptional growth potential

The AIOps market shows exceptional growth potential, valued at $1.87 billion in 2024 and projected to reach $8.64 billion by 2032, exhibiting a CAGR of 21.4%.

Expertise

Our expertise in large-scale data processing

Learn how we utilize Big Data technologies to tackle large-scale data processing problems.

Explore our Big Data case studies

Data consistency management

We implement hybrid consistency models and CRDTs, expertly balancing strict ACID requirements with eventual consistency needs in distributed environments.

Data skew and processing imbalance

We utilize salting techniques, adaptive repartitioning, and tiered storage optimization to resolve data skew and significantly enhance system performance.

Network-induced latency and communication challenges

We strategically optimize data locality and employ high-performance communication protocols to minimize latency and improve global cluster efficiency.

Fault tolerance and system reliability

We adopt advanced reliability solutions such as erasure coding and optimized checkpointing to maximize uptime and cost-effectively reduce downtime.

Backpressure management and stream processing

We effectively manage backpressure with dynamic rate adjustments, adaptive buffering, and pull-based consumption models to maintain system stability and data integrity.

Security and observability at scale

We deploy Confidential Computing, Zero-Trust architectures, and scalable observability frameworks to enhance security and monitoring in extensive distributed systems.

Real-time analytics revolution

We deliver real-time analytics solutions critical for driving profitability, responsiveness, and competitive advantage.

Artificial intelligence and machine learning integration

We integrate AI and ML into data processing workflows, democratizing analytics capabilities and empowering autonomous decision-making across organizations.

Data lakehouse architecture adoption

We lead in adopting and optimizing data lakehouse architectures, unifying analytics platforms, enhancing scalability, and significantly reducing operational costs.

Market growth and investment opportunities

We capitalize on robust growth in the Big Data processing and AIOps markets by providing advanced analytics solutions tailored to industry demands.

Data privacy and governance evolution

We leverage cutting-edge technologies, such as synthetic data and homomorphic encryption, to ensure secure and compliant data analysis amid evolving privacy regulations.

How we work

At the core of our methodology lies a research-driven, architecture-first philosophy designed to transform petabytes of raw data into actionable intelligence while addressing the inherent complexities of modern distributed systems. We combine battle-tested frameworks like Apache Kafka, Spark, and Flink with cutting-edge innovations to deliver scalable, reliable, and cost-efficient solutions tailored to your business objectives.

We outline our systematic approach to solving Big Data challenges and driving tangible value across your organization.

Engagement models

Phase 1

Strategic planning & objective definition

Step

Business-data alignment framework

We initiate projects with a value alignment process that maps organizational KPIs to data capabilities.

SWOT-driven use case prioritization

Evaluate 15-20 potential applications through weighted scoring of business impact (40%), technical feasibility (30%), and ROI potential (30%).

Regulatory compliance audit

Preemptively address GDPR, CCPA, and industry-specific mandates through automated policy mapping tools, achieving 98% regulation coverage.

Step

Architectural blueprinting

Our 3-tier capacity modeling prevents infrastructure sprawl.

Outcome

A prioritized roadmap of data initiatives.

Hot layer

In-memory processing for sub-50ms analytics.

Warm layer

Columnar storage for 80% of operational queries.

Cold layer

Erasure-coded object storage reducing costs by 60% versus traditional backups.

Outcome

A prioritized roadmap of data initiatives.

Phase 2

Data ecosystem assessment

Step

Maturity benchmarking

We employ an assessment matrix across seven dimensions.

Outcome

A clear benchmark of data maturity and areas for improvement.

1. Data Quality Index (DQI) scoring

2. Architecture coherence analysis

3. Technical debt quantification

4. Alignment with strategic goals

5. Risk exposure mapping

6. Ecosystem interoperability grading

7. Yield potential estimation

Outcome

A clear benchmark of data maturity and areas for improvement.

Phase 3

Architecture design & toolchain selection

We create a scalable and efficient data architecture tailored to your needs.

Step

Consistency-performance optimization

Clients usually request designing data storage tiers for real-time analytics (fast queries) and archival storage (cost-effective).

Strong consistency zones

Fast converging consensus for critical transactions, such as financial transactions (5ms commit latency).

Eventual consistency domains

CRDTs with 200ms convergence guarantees for recommendation engines.

Step

Skew mitigation architecture

Outcome

Architectural blueprints optimizing cost and performance.

Adaptive salting engine

Dynamically injects 5-15% entropy into partition keys.

Cost-aware repartitioning

ML models predict shuffle patterns with 89% accuracy.

Outcome

Architectural blueprints optimizing cost and performance.

Phase 4

Pipeline implementation

We develop robust data ingestion and processing pipelines that ensure efficient data flow from source to insight.

Step

Ingestion framework

Learn how we utilize Big Data technologies to tackle large-scale data processing problems.

Explore our Big Data case studies

Batch ingestion

We use batch processing engines like Apache Spark to enable high-throughput ingestion (up to 1 million records/sec) for large, periodic data loads.

Stream ingestion

We deploy streaming engines, like Apache Flink, for real-time stream processing. These engines can handle events with just 15ms of event-time lag.

Change Data Capture (CDC)

We synchronize database changes with sub-second replication latency, ensuring minimal lag in dynamic systems.

Tailored implementation of engines

Some clients prefer implementing their special-purpose stream processing engines. Recently, we implemented a .NET streaming platform for low-latency IoT streams on top of an actor system framework that provides unparalleled performance even on old hardware.

Step

Processing optimization

See our technological expertise in large-scale data processing

Outcome

Reliable, scalable, and efficient data flows ready for production and business decision-making.

Vectorized execution

We have expertise utilizing Apache Arrow-like systems for in-memory data transformations, achieving up to 10GB/s per core processing speed.

Energy-aware scheduling

We deploy intelligent scheduling that balances GPU and CPU resources to reduce energy consumption by up to 35%, aligning performance with sustainability.

Outcome

Reliable, scalable, and efficient data flows ready for production and business decision-making.

Phase 5

Data quality enforcement

We implement comprehensive quality controls to guarantee the reliability and trustworthiness of your data throughout the pipeline.

Step

Holistic quality gates

We provide support in ISO 9001 compliance

Schema evolution tracking

We use compatibility scoring methods to prevent breaking changes and ensure that schema changes remain non-disruptive.

Automated contract testing

Enforce data contracts at each pipeline stage to validate payloads and structures.

Step

Statistical validation

Distribution monitoring

We track key distribution metrics using KL divergence to detect unexpected shifts in data behavior.

Drift detection

We use distancing methods and other statistical techniques to identify concept drift in data over time.

Step

Business rule enforcement

Rule-based validators

We implement domain-specific rules (for example, revenue should never be negative) to catch logical inconsistencies early.

Outcome

High-confidence data streams that support critical business decisions without interruption.

Phase 6

Security implementation

We embed robust, modern security practices throughout the data lifecycle to protect your sensitive information and ensure compliance with industry regulations.

Security expertise

Outcome

Significantly reduced risk exposure and ensured seamless regulatory compliance.

Zero-trust architecture

Identity-first access

We implement identity-based authentication and authorization, verifying every system and service.

Microsegmentation

We segment your network and data zones to contain breaches and reduce lateral attack movement.

Confidential data handling

Encrypted processing

We secure data during processing without performance loss by utilizing confidential computing technologies like Intel SGX or Istio service mesh on top of Kubernetes.

PII isolation

Personally Identifiable Information is separated, masked, and access-controlled to meet GDPR, CCPA, and similar mandates.

Proactive threat detection

Behavioral analytics

We deploy machine learning models to detect suspicious activity patterns across pipelines.

Audit trails

Immutable logging of all access and operations enables forensic analysis and ensures traceability.

Outcome

Significantly reduced risk exposure and ensured seamless regulatory compliance.

Phase 7

Monitoring & observability

We implement a comprehensive observability stack to provide deep insights into your data systems and ensure continuous reliability and performance.

Our monitoring expertise

Outcome

Proactive, data-driven infrastructure management with reduced incident resolution times.

Multi-layer metrics

We capture system, application, and business-level metrics to monitor health and performance.

L1 (System): Uptime, memory, CPU, and storage utilization.
L2 (Application): Data pipeline latency, throughput, error rates.
L3 (Business): Data usage trends, user behavior, and business KPI correlations.

Real-time dashboards

Custom dashboards using tools like Grafana or Looker visualize key metrics at a glance.

Smart alerting

Threshold and anomaly-based alerting systems notify teams of deviations before they impact operations.

Seasonality modeling

We may forecast expected behavior to catch unusual trends.

End-to-end tracing

Clients usually request distributed tracing to identify slowdowns and root causes across the pipeline.

Immutable logs

Logging strategies ensure reliable forensic trails for debugging and compliance.

Outcome

Proactive, data-driven infrastructure management with reduced incident resolution times.

Phase 8

Continuous optimization

We apply advanced techniques to continuously enhance your data infrastructure’s performance, scalability, and cost-effectiveness.

Outcome

Optimized operations that reduce costs while elevating data performance and responsiveness.

Performance tuning

Query plan optimization

Using cost-based optimizers and execution profiling, we refactor queries for faster performance.

Materialized views & caching

Frequently accessed data is cached or precomputed using materialized views to reduce compute overhead.

Code efficiency reviews

Regular code audits identify bottlenecks and improve processing logic across the pipeline.

Resource efficiency

Autoscaling infrastructure

Dynamically scaling cloud resources based on workload demand ensures cost efficiency without sacrificing reliability.

Container orchestration

Efficient orchestration through platforms like Kubernetes optimizes resource usage across distributed systems.

Idle resource detection

We use telemetry and heuristics to decommission or reallocate underutilized services.

Evolution of architecture

Tech debt management

We track component health and prioritize refactoring or replacing outdated systems.

Sustainable computing

Monitoring tools track energy usage and recommend greener alternatives without impacting performance.

Outcome

Optimized operations that reduce costs while elevating data performance and responsiveness.

Phase 9

Knowledge institutionalization

We embed data practices and knowledge into your organization’s daily operations to ensure sustainability, autonomy, and long-term value.

Training & enablement

Role-specific workshops

We conduct tailored sessions for engineers, analysts, and business users to operationalize new systems.

Onboarding playbooks

Documentation and guides are created to help future hires quickly ramp up and maintain best practices.

Documentation & knowledge hubs

Living documentation

We build and maintain centralized documentation portals that evolve alongside the architecture.

Self-service portals

Dashboards and query templates enable business users to explore and analyze data independently.

MLOps & automation integration

Model lifecycle management

We implement registries and pipelines that allow reproducible and version-controlled model deployment.

DataOps pipelines

Automating data validation, CI/CD for pipelines, and metadata tracking ensures consistency and transparency.

Cultural integration

Communities of practice

We help foster cross-functional data communities to share learnings and drive continuous improvement.

Data stewardship programs

Designating data champions in departments ensures quality and governance practices are upheld.

Outcome

A self-sustaining data culture where expertise is distributed, systems are maintainable, and innovation continues post-engagement.

Measurable outcomes

This systematic approach transforms Big Data challenges into competitive advantages through relentless focus on architectural coherence, automated governance, and business-value traceability. By institutionalizing data excellence across the lifecycle, enterprises unlock sustainable value from their most strategic asset.

Times greater insights

Real-time dashboards refresh in less than 100ms versus the industry average of 3.7s.

Pipeline reliability

Through hybrid checkpointing (45s recovery vs 10min baseline).

TCO reduction

Via tiered storage and spot instance orchestration.

Recent achievements in large-scale data processing

See our portfolio for more results

Developing comprehensive web-based client-sensitive load balancing solutions for high-throughput and low-latency IoT data systems.

Creating and deploying scale-out architectures with incremental checkpoints that ensure exact state consistency.

Designing and executing a FaaS architecture for distributed event processing in a C-like environment, outperforming standard data processing systems such as JVM-based Apache Flink and Spark regarding throughput and latency by 24%.

Leading the development of actor-system-based, <15ms latency, stateful processing systems that support launching ad-hoc processing pipelines through user-friendly interfaces.

Integrating distributed NoSQL databases with data processing engines facilitates applications that would otherwise necessitate maintaining a separate data lake.

Streamlining IoT schema standardization to cut future system maintenance efforts by 250% and extend product lifespans, allowing various company-wide initiatives to depend confidently on these new systems to achieve forthcoming business objectives.

Feeling ready to get started?

Connect with our team to discover how we can architect, implement, and optimize your next-generation data systems.