Big DataEngineering Services
Build enterprise-grade data infrastructure that scales. Our data engineers design and implement pipelines that process petabytes of data reliably and cost-effectively.
What is Big Data Engineering?
Big data engineering involves designing, building, and maintaining the infrastructure needed to collect, store, process, and analyze large volumes of data. We help organizations build modern data platforms that turn raw data into competitive advantage.
Key Capabilities
- Data pipeline design and implementation
- Data lake and data warehouse architecture
- Real-time and batch data processing
- Data quality and governance
- Cloud-native data infrastructure
- Cost optimization for data workloads
Why Businesses Choose Big
Key benefits that drive business value and competitive advantage
Scalable Processing
Process petabytes of data with distributed computing frameworks.
Real-Time Insights
Stream processing for real-time analytics and decision making.
Cost Efficiency
Optimize storage and compute costs with modern architectures.
Data Quality
Ensure data accuracy and consistency across the organization.
Industry Use Cases
How leading companies leverage Big for competitive advantage
Customer 360 Data Platform
Unify customer data from all touchpoints for personalization and analytics.
Key Benefits:
Technologies:
Risk & Compliance Data Lake
Centralized data platform for risk analytics and regulatory reporting.
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Technologies:
IoT Data Processing
Process and analyze high-volume sensor data from connected devices.
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Technologies:
Content Analytics Platform
Analyze viewing patterns and content performance at scale.
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Technologies:
Our Big Data Expertise
Our team of 25+ data engineers has processed over 50 petabytes of data across industries.
Data Pipeline Development
Build reliable, scalable data pipelines for batch and streaming data.
Data Platform Architecture
Design modern data architectures including data lakes and warehouses.
Real-Time Analytics
Enable real-time analytics with stream processing and low-latency queries.
Data Governance
Implement data quality, cataloging, and governance frameworks.
Technology Stack
Tools, frameworks, and integrations we work with
Core Tools
Integrations
Frameworks
Success Stories
Real results from our Big projects
Enterprise Data Lake
Challenge:
A major retailer needed to consolidate data from 100+ sources including POS, e-commerce, inventory, and marketing for unified analytics.
Solution:
We built a cloud-native data lake on AWS using Spark for processing, Airflow for orchestration, and dbt for transformation. The platform processes 5TB+ daily with full data lineage.
Results:
- 100+ data sources integrated
- 5TB+ processed daily
- 80% reduction in time-to-insight
- $2M annual savings in ETL costs
Technologies Used:
Real-Time Fraud Detection Pipeline
Challenge:
A payment processor needed to detect fraudulent transactions in real-time while processing millions of transactions per hour.
Solution:
We implemented a streaming architecture with Kafka and Flink for real-time processing, ML models for fraud scoring, and sub-second response times for transaction decisions.
Results:
- 10M+ transactions/hour processed
- Sub-100ms fraud scoring
- 40% improvement in fraud detection
- $15M annual fraud prevented
Technologies Used:
Engagement Models
Flexible engagement options to match your project needs
Data Platform Build
End-to-end data platform design and implementation.
Includes:
- Architecture design
- Pipeline development
- Data modeling
- Documentation
New data platforms
Data Engineering Team
Dedicated data engineers embedded in your team.
Includes:
- Senior engineers
- Full-time commitment
- Knowledge transfer
- Agile delivery
Ongoing data initiatives
Data Architecture Consulting
Expert guidance on data strategy and architecture.
Includes:
- Assessment
- Architecture review
- Technology selection
- Roadmap
Strategic planning
Frequently Asked Questions
What's the difference between a data lake and data warehouse?
A data lake stores raw data in its native format (structured, semi-structured, unstructured) at low cost, ideal for data science and exploration. A data warehouse stores processed, structured data optimized for BI and reporting. Modern "lakehouse" architectures combine both, offering data lake flexibility with warehouse performance.
When should we use batch vs real-time processing?
Batch processing is more cost-effective for analytics that don't need immediate results (daily reports, historical analysis). Real-time processing is essential when you need immediate action (fraud detection, live dashboards, personalization). Many organizations use both: real-time for operational needs and batch for deeper analytics.
How do you ensure data quality?
We implement data quality at every stage: schema validation at ingestion, data quality tests (Great Expectations, dbt tests) in pipelines, anomaly detection for data drift, and monitoring dashboards for data freshness and completeness. We also establish data contracts between producers and consumers.
What cloud platform is best for big data?
All major clouds have strong big data offerings. AWS is most mature with broad service selection. GCP excels in analytics (BigQuery) and is often most cost-effective. Azure integrates well with Microsoft tools. We help you choose based on your specific requirements, existing infrastructure, and team skills.
How do you handle data governance and compliance?
We implement comprehensive data governance: data catalogs for discoverability, column-level access controls for security, data lineage for compliance, PII detection and masking, and audit logging. For regulated industries, we ensure compliance with GDPR, HIPAA, CCPA, and other requirements.
Ready to Scale Your Data Infrastructure?
Transform your data capabilities with modern big data engineering. Let's discuss your data challenges.