Data Science

Big Data Solutions & Services

DevSimplex provides comprehensive big data solutions to help businesses process, store, and analyze massive volumes of structured and unstructured data.

View Case Studies
10x faster
Success Rate
500TB+
Avg Delivery
3+
Projects Delivered
96%
Client Retention

Trusted by 200+ businesses worldwide

Transform Data Complexity Into Business Value

From terabytes to petabytes—build big data infrastructure that delivers insights at scale.

Distributed architectures that process massive datasets 10x faster

Real-time and batch processing for comprehensive analytics coverage

Cloud-native platforms with auto-scaling and cost optimization

Data lake foundations that support structured and unstructured data

Production-ready solutions with monitoring, governance, and security built-in

Our Offerings

End-to-end software solutions tailored to your business needs

Big Data Architecture Design

Architecture

Design scalable and efficient big data architectures to handle massive data volumes and processing requirements.

Features:

  • Scalable architecture design
  • Data lake architecture
  • Distributed processing design
HadoopSparkKafka

What You Get:

  • Architecture design
  • Technology recommendations
  • Implementation roadmap
  • Performance estimates
  • Cost analysis

Data Lake Implementation

Data Lake

Build and implement data lakes to store and process massive volumes of structured and unstructured data.

Features:

  • Data lake setup and configuration
  • Data ingestion pipelines
  • Data catalog and governance
AWS S3Azure Data LakeHadoop

What You Get:

  • Data lake implementation
  • Ingestion pipelines
  • Data catalog
  • Governance framework
  • Documentation

Real-Time Big Data Processing

Real-Time Processing

Implement real-time data processing systems to handle streaming data and enable real-time analytics.

Features:

  • Streaming data processing
  • Real-time analytics
  • Event-driven architecture
KafkaSpark StreamingFlink

What You Get:

  • Streaming pipeline
  • Real-time processing
  • Analytics setup
  • Monitoring system
  • Documentation

Big Data Analytics Platform

Analytics

Build comprehensive analytics platforms to analyze massive datasets and generate actionable insights.

Features:

  • Advanced analytics
  • Machine learning integration
  • Interactive dashboards
SparkPrestoHive

What You Get:

  • Analytics platform
  • Dashboards
  • Query interfaces
  • Visualization tools
  • Analytics reports

Big Data Migration & Modernization

Migration

Migrate and modernize big data infrastructure to cloud platforms and modern technologies.

Features:

  • Legacy system migration
  • Cloud migration
  • Technology modernization
Cloud PlatformsMigration ToolsModern Frameworks

What You Get:

  • Migration plan
  • Modernized infrastructure
  • Data migration
  • Performance optimization
  • Documentation

Big Data Consulting & Strategy

Consulting

Strategic consulting to develop big data strategies, assess current state, and plan implementations.

Features:

  • Big data strategy development
  • Current state assessment
  • Technology evaluation
Strategy FrameworksAssessment ToolsAnalytics

What You Get:

  • Strategy document
  • Assessment report
  • Technology recommendations
  • Implementation roadmap
  • ROI analysis

Why Choose DevSimplex for Big Data?

We combine deep technical expertise with proven methodologies to deliver big data solutions that scale with your business.

Proven Scalability

Our big data architectures handle petabyte-scale datasets with distributed processing that grows with your needs.

Real-Time Processing

Stream processing capabilities deliver insights in milliseconds, enabling real-time decision-making and analytics.

Cloud-Native Expertise

Leverage modern cloud platforms and managed services to reduce operational overhead and accelerate deployment.

Enterprise-Grade Security

Built-in data governance, encryption, and compliance frameworks protect your most valuable asset—your data.

Advanced Analytics Ready

Architectures designed for ML and AI workloads, turning massive datasets into predictive insights.

Cost Optimization

Smart storage tiering, compute optimization, and efficient processing reduce infrastructure costs by 30-50%.

Use Cases

Real-world examples of successful implementations across industries

E-commerce

Challenge:

Processing massive volumes of transaction, customer, and product data for real-time analytics

Solution:

Big data platform with real-time processing and advanced analytics for customer insights and recommendations

Benefits:

  • Real-time customer analytics
  • Improved recommendation engine
300% ROI within 12 months

Financial Services

Challenge:

Analyzing massive volumes of transaction data for fraud detection and risk management

Solution:

Big data platform with real-time fraud detection and risk analytics

Benefits:

  • Real-time fraud detection
  • Improved risk management
400% ROI within 18 months

Healthcare

Challenge:

Processing and analyzing massive volumes of patient data for research and treatment insights

Solution:

Big data platform with data lake and advanced analytics for healthcare insights

Benefits:

  • Improved patient outcomes
  • Better research capabilities
250% ROI within 24 months

Key Success Factors

Our proven approach to delivering software that matters

1

Scalable Architecture Design

We architect big data systems using distributed computing principles, ensuring they handle growing data volumes without performance degradation.

500TB+ data processed daily across our platforms
2

Modern Technology Stack

Leveraging Hadoop, Spark, Kafka, and cloud data lakes, we build on proven technologies optimized for big data workloads.

10x faster processing vs. traditional databases
3

Real-Time Capabilities

Stream processing architectures enable real-time analytics, monitoring, and alerting for time-sensitive use cases.

Sub-second latency for streaming analytics
4

Data Governance

Built-in data quality, lineage tracking, and governance frameworks ensure data reliability and compliance.

99.9% data accuracy and quality
5

Cost-Effective Operations

Optimized storage tiers, compute efficiency, and cloud-native approaches reduce total cost of ownership significantly.

30-50% reduction in infrastructure costs

Our Process

A systematic approach to quality delivery and successful outcomes

1

Assessment & Strategy

2-4 weeks

Comprehensive assessment of data requirements, current state, and big data strategy development.

Deliverables:

  • Current state assessment
  • Data requirements analysis
  • Big data strategy
  • Technology recommendations

Activities:

Requirements gatheringCurrent state analysisTechnology evaluationStrategy developmentPlanning
2

Architecture & Design

2-4 weeks

Design scalable big data architecture, data models, and processing workflows.

Deliverables:

  • Architecture design
  • Data models
  • Processing workflows
  • Technology stack

Activities:

Architecture designData modelingWorkflow designTechnology selectionDesign review
3

Implementation

8-24 weeks

Build and implement big data infrastructure, pipelines, and analytics capabilities.

Deliverables:

  • Big data infrastructure
  • Data pipelines
  • Processing systems
  • Analytics platform

Activities:

Infrastructure setupPipeline developmentSystem integrationTestingOptimization
4

Optimization & Support

Ongoing

Performance optimization, monitoring, and ongoing support for big data systems.

Deliverables:

  • Performance optimization
  • Monitoring setup
  • Documentation
  • Training

Activities:

Performance tuningMonitoringDocumentationTrainingSupport

Technology Stack

Modern tools and frameworks for scalable solutions

Processing

Apache Spark
Big data processing
Hadoop
Distributed processing
Kafka
Streaming data

Storage

Data Lakes
Large-scale storage
HDFS
Hadoop file system
S3
Cloud storage

Analytics

Presto
Query engine
Hive
Data warehouse
Analytics Platforms
Business intelligence

Case Studies

Real-world success stories and business impact

E-commerce Big Data Platform

Major Online RetailerE-commerce

Challenge:

Processing massive volumes of transaction, customer, and product data for real-time analytics and recommendations

Solution:

Comprehensive big data platform with data lake, real-time processing, and advanced analytics

16 weeks

Results:

Real-time customer analytics
40% increase in sales

Tech:

HadoopSparkKafka

Financial Services Fraud Detection

Regional BankFinancial Services

Challenge:

Analyzing massive volumes of transaction data for real-time fraud detection and risk management

Solution:

Big data platform with real-time processing and machine learning for fraud detection

20 weeks

Results:

Real-time fraud detection
50% reduction in fraud losses

Tech:

SparkKafkaML Platforms

Healthcare Data Lake

Healthcare NetworkHealthcare

Challenge:

Processing and analyzing massive volumes of patient data for research and treatment insights

Solution:

Healthcare data lake with advanced analytics and research capabilities

24 weeks

Results:

Improved patient outcomes
Better research capabilities

Tech:

Data LakeSparkAnalytics

Client Stories

What our clients say about working with us

"DevSimplex's big data platform transformed our ability to analyze customer data in real-time. The platform handles massive volumes of data and provides actionable insights that drive our business decisions."
Michael Chen
CTO
Major Online Retailer
"The big data fraud detection system has significantly reduced our fraud losses. Real-time processing and machine learning capabilities enable us to detect and prevent fraud instantly."
Sarah Johnson
Risk Director
Regional Bank
"Our healthcare data lake has enabled breakthrough research and improved patient outcomes. The platform processes massive volumes of data while maintaining HIPAA compliance."
Dr. Robert Martinez
Chief Medical Officer
Healthcare Network

Frequently Asked Questions

Get expert answers to common questions about our enterprise software development services, process, and pricing.

Big data refers to extremely large datasets that cannot be processed using traditional data processing tools. It typically involves data volumes in terabytes or petabytes, requiring distributed processing and specialized technologies.

We use modern big data technologies including Apache Spark, Hadoop, Kafka, Flink, and cloud-based data lakes. We select technologies based on your specific requirements and use cases.

Implementation timelines vary based on complexity. Basic implementations take 4-8 weeks, while comprehensive enterprise solutions can take 16-32 weeks. We provide detailed timelines during planning.

Yes, we provide big data migration and modernization services. We can migrate from legacy systems to modern cloud-based platforms with minimal downtime.

A data warehouse stores structured, processed data optimized for analytics. A data lake stores raw data in its native format, supporting both structured and unstructured data. Data lakes are better for big data scenarios.

Still Have Questions?

Get in touch with our team for personalized help.

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