Enterprise data pipeline engineering that eliminates data silos and scales to petabyte workloads

Build real-time and batch processing pipelines with distributed architectures, automated data quality validation, and fault-tolerant systems that handle millions of events per second while maintaining sub-second latency.

Enterprise data pipeline engineering 01 (1)

Leaders trusting our AI solutions:

10M+

Events processed per second with distributed Kafka clusters

99.99%

Pipeline uptime with fault-tolerant architecture design

<100ms

End-to-end data latency for real-time streaming pipelines

Proud members and partners of

Challenges Xenoss eliminates with enterprise data pipeline engineering

Data silos preventing unified analytics and decision-making

Enterprise data is trapped in disconnected systems – CRM, ERP, databases, APIs, and legacy applications. Teams waste weeks manually extracting and correlating data from multiple sources, creating inconsistent reports and delayed insights that hurt business agility.

Pipeline failures causing critical business process disruptions

Traditional ETL pipelines break when data formats change, APIs go down, or processing volumes spike. A single failure can cascade through dependent systems, causing executive dashboards to go stale and analytics teams to lose trust in data reliability.

Inability to process real-time data for time-sensitive operations

Batch processing systems create 6-24 hour delays between data generation and availability. Fraud detection, inventory management, and customer personalization require sub-second data processing that traditional pipelines can’t deliver without massive infrastructure investments.

Data quality issues corrupting downstream analytics and ML models

Dirty data, schema mismatches, and duplicate records flow through pipelines undetected. Poor data quality costs enterprises $12.9M annually while destroying confidence in AI/ML initiatives and leading to incorrect business decisions based on flawed analytics.

Manual pipeline management that doesn’t scale with data growth

Data engineering teams spend 80% of their time on maintenance instead of innovation. Manual monitoring, error handling, and performance tuning create bottlenecks that prevent organizations from scaling data operations as business requirements evolve.

Performance bottlenecks under enterprise-scale data volumes

Pipelines designed for gigabytes fail catastrophically when processing terabytes. Memory limitations, network congestion, and processing inefficiencies create hours-long delays, making real-time analytics impossible and batch jobs miss critical SLA windows.

Lack of data lineage and observability for compliance auditing

Regulatory compliance requires complete data traceability from source to destination. Without proper lineage tracking and audit trails, enterprises face compliance violations, struggle with data governance, and can’t troubleshoot pipeline issues effectively.

Vendor lock-in limiting flexibility and increasing long-term costs

Proprietary ETL tools create expensive dependencies with licensing costs that scale with data volume. Organizations lose architectural flexibility, face vendor price increases, and struggle to adopt new technologies that could improve performance or reduce costs.

Build enterprise data pipeline infrastructure from scratch or enhance your existing systems

What we engineer for enterprise use cases

Real-time streaming data processing engines

Custom Apache Kafka and Apache Pulsar implementations that process millions of events per second with guaranteed message delivery. Build fault-tolerant streaming architectures with exactly-once processing semantics for financial transactions, IoT telemetry, and user behavior analytics.

Distributed batch processing platforms

Scalable Apache Spark and Hadoop clusters optimized for petabyte-scale data processing. Implement custom partitioning strategies, memory optimization, and dynamic resource allocation to handle enterprise workloads with predictable performance and cost efficiency.

Data quality validation and monitoring systems

Automated data profiling, schema validation, and anomaly detection pipelines that catch quality issues before they corrupt downstream analytics. Real-time monitoring dashboards with configurable alerts for data freshness, completeness, and accuracy violations.

Multi-cloud data integration architectures

Unified data pipelines that seamlessly move data across AWS, Azure, GCP, and on-premise systems. Handle format transformations, API rate limiting, and network optimization to create single source of truth from disparate enterprise data sources.

Custom ETL/ELT orchestration frameworks

Apache Airflow and Prefect-based workflow management with dependency resolution, retry logic, and parallel execution. Build complex data transformation pipelines with automatic scaling, error recovery, and comprehensive lineage tracking for regulatory compliance.

High-performance data storage optimization

Columnar storage implementations using Apache Parquet and Delta Lake with intelligent partitioning and compression. Optimize query performance for analytics workloads while minimizing storage costs through lifecycle management and tiered storage strategies.

Event-driven microservices data architecture

Decoupled pipeline components using message queues, event sourcing, and CQRS patterns. Build resilient systems where individual services can be updated, scaled, or replaced without affecting the entire data processing workflow.

Data pipeline observability and DevOps automation

Comprehensive monitoring, logging, and alerting systems with distributed tracing for end-to-end pipeline visibility. Implement Infrastructure-as-Code, automated testing, and CI/CD pipelines for reliable deployment and maintenance of data infrastructure.

How to start

Transform your enterprise with AI and data engineering—faster efficiency gains and cost savings in just weeks

Challenge briefing

2 hours

Tech assessment

2-3 days

Discovery phase

1 week

Proof of concept

8-12 weeks

MVP in production

2-3 months

Tech stack for data pipeline engineering

Why Xenoss is trusted to build enterprise-grade data pipeline infrastructure

We solve the complex engineering challenges that prevent enterprises from scaling data operations reliably.

Built data infrastructure that processes trillions of events for Fortune 500 companies

Engineered production pipelines handling petabyte-scale workloads for Adidas, Uber, and HSBC. Our systems process billions of daily transactions with 99.99% uptime, supporting mission-critical business operations that can’t afford data delays or quality issues.

Mastered distributed systems architecture for fault-tolerant data processing

Built custom Kafka clusters, Spark optimizations, and multi-region failover systems that maintain data consistency during outages. Our distributed architectures handle node failures gracefully while preserving exactly-once processing guarantees for financial and regulatory workloads.

Eliminated data silos through unified pipeline architectures

Integrated 50+ data sources including legacy mainframes, cloud APIs, real-time streams, and batch systems into unified platforms. Our integration frameworks break down organizational data barriers while maintaining security, governance, and compliance requirements.

Optimized pipeline performance to handle 10M+ events per second

Developed proprietary optimization techniques for Spark job tuning, Kafka partitioning strategies, and memory management that deliver 10x performance improvements. Our pipelines maintain consistent throughput even during peak traffic spikes and data volume surges.

Automated monitoring that prevents data quality issues from reaching analytics

Built real-time data profiling, schema validation, and anomaly detection systems that catch quality problems before they corrupt downstream processes. Our observability platforms provide complete data lineage tracking and alert systems for proactive issue resolution.

Reduced infrastructure costs by 60% through intelligent resource optimization

Designed auto-scaling systems with spot instance management, intelligent caching layers, and storage lifecycle policies that minimize cloud costs. Our architectures automatically adjust compute resources based on workload patterns, eliminating over-provisioning waste.

Built GDPR, SOX, and HIPAA-compliant data processing systems

Implemented end-to-end encryption, audit logging, and data governance controls that meet regulatory requirements for financial services and healthcare. Our security frameworks include role-based access controls, data masking, and compliance reporting automation.

Infrastructure-as-Code and CI/CD systems that eliminate manual deployment risks

Created automated testing, deployment, and monitoring systems that reduce pipeline maintenance overhead by 80%. Our DevOps practices include blue-green deployments, automated rollbacks, and comprehensive pipeline health monitoring for operational excellence.

Featured projects

Retail | AI & ML

Multi-agent extendable hyperautomation platform for enterprise accounting automation

Learn More

Oil & Gas | AI & ML

ML-based virtual flow meter

Learn More

AdTech

Implementing a proprietary SDK with a lightweight tracker for a new generation mediation platform

Learn More

Solution development | High load

Developing a gaming advertising platform with 1.4B monthly video impressions

Learn More

Solution development | AI & ML

Building performance-oriented mobile DSP with innovative user behavior prediction mechanism

Learn More

Solution development | AI & ML

Fast rollout of AI-powered creative management platform used by Nestlé, Adidas & Uber

Learn More

Solution development | High load

Building a video-on-demand platform with 1.1M monthly users for a leading content distributor in Europe

Learn More

Solution development | High load

Reducing infrastructure costs by 20 times for a programmatic ad marketplace with 1B audience reach

Learn More

AdTech | High load

Multifunctional Customer Data Platform (CDP)

Learn More

Build enterprise data pipeline infrastructure that scales to petabyte workloads

Talk to our data engineers about designing distributed streaming and batch processing systems with Apache Kafka, Spark optimization, real-time monitoring, and fault-tolerant architectures that handle 10M+ events per second while maintaining 99.99% uptime and complete data lineage tracking.

Xenoss team helped us build a well-balanced tech organization and deliver the MVP within a very short timeline. I particularly appreciate their ability to hire extreme fast and to generate great product ideas and improvements.

Oli Marlow Thomas,

CEO and founder, AdLib

Get a free consultation

What’s your challenge? We are here to help.

Leverage more data engineering & AI development services

AI capabilities

Data Engineering

Data fabric
Data platforms
Data pipelines
Data infrastructure
Data migration
Data mining and preparation
Infrastructure cost optimization
Data stack integration
Real-time data solutions
Data analytics solutions
Data visualization & reporting solutions
Enterprise application modernization services
Data strategy
Data engineering consulting
Business intelligence

Machine Learning and automation

ML & MLOps
ML system TCO optimization
Model & algorithm development and integration
RPA (Robotic Process Automation)

Enterprise data pipeline engineering that eliminates data silos and scales to petabyte workloads

Challenges Xenoss eliminates with enterprise data pipeline engineering

Build enterprise data pipeline infrastructure from scratch or enhance your existing systems

Real-time streaming data processing engines

Distributed batch processing platforms

Data quality validation and monitoring systems

Multi-cloud data integration architectures

Custom ETL/ELT orchestration frameworks

High-performance data storage optimization

Event-driven microservices data architecture

Data pipeline observability and DevOps automation

Tech stack for data pipeline engineering

Mastered distributed systems architecture for fault-tolerant data processing

Eliminated data silos through unified pipeline architectures

Optimized pipeline performance to handle 10M+ events per second

Automated monitoring that prevents data quality issues from reaching analytics

Reduced infrastructure costs by 60% through intelligent resource optimization

Built GDPR, SOX, and HIPAA-compliant data processing systems

Infrastructure-as-Code and CI/CD systems that eliminate manual deployment risks

Multi-agent extendable hyperautomation platform for enterprise accounting automation

ML-based virtual flow meter

Implementing a proprietary SDK with a lightweight tracker for a new generation mediation platform

Developing a gaming advertising platform with 1.4B monthly video impressions

Building performance-oriented mobile DSP with innovative user behavior prediction mechanism

Fast rollout of AI-powered creative management platform used by Nestlé, Adidas & Uber

Building a video-on-demand platform with 1.1M monthly users for a leading content distributor in Europe

Reducing infrastructure costs by 20 times for a programmatic ad marketplace with 1B audience reach

Multifunctional Customer Data Platform (CDP)

Related content

Best practices for building scalable, reliable, and secure data pipelines

Building enterprise knowledge bases with LLMs: architecture considerations for Vanilla RAG, GraphRAG, and agentic RAG

Data tool sprawl is draining your budget: Here’s how to fix it