Sample Datasets & POCs
Validate your big data use cases with our production-grade sample datasets and guided proof-of-concept implementations.
Production-Grade Test Data
Our platform includes a comprehensive library of sample datasets designed to mirror real-world data in volume, complexity, and characteristics. These datasets enable you to validate your analytics use cases and test platform performance before implementing with your own data.
All sample datasets are completely synthetic but statistically representative of real-world data, with realistic distributions, correlations, and anomalies. They contain no personally identifiable information or confidential business data.
Sample Dataset Catalog
Our sample datasets cover a wide range of industries and use cases, allowing you to test and validate your specific analytical requirements. Each dataset is designed with careful attention to realistic data patterns and relationships.
IoT & Telemetry
Industrial Sensor Network
Comprehensive dataset of industrial equipment sensor readings from manufacturing environments. Includes temperature, pressure, vibration, power consumption, and operational state data from multiple equipment types across simulated factory locations.
Key Features:
- Realistic sensor failure patterns and anomalies
- Seasonal and cyclical patterns in equipment usage
- Correlated readings between related sensors
- Maintenance event markers and equipment downtime
- Varying data quality and missing data patterns
Ideal for:
- Predictive maintenance modeling
- Anomaly detection algorithms
- Real-time streaming analytics
- Equipment efficiency optimization
Smart City Infrastructure
Urban IoT dataset including traffic sensors, environmental monitors, utility meters, and public infrastructure telemetry. Covers multiple simulated urban areas with varying population densities and infrastructure characteristics.
Key Features:
- Geospatial data with precise coordinates
- Weather correlation with infrastructure metrics
- Daily, weekly, and seasonal urban patterns
- Special event impacts on urban systems
- Interconnected system dependencies
Ideal for:
- Urban planning and optimization
- Traffic flow prediction
- Environmental impact analysis
- Resource utilization forecasting
E-commerce & Retail
E-commerce Clickstream
Comprehensive e-commerce user behavior dataset including pageviews, product interactions, search queries, cart events, and purchase transactions. Covers web and mobile app interactions with consistent user journeys across devices.
Key Features:
- Complete user journeys from entry to purchase
- Cross-device user identification
- Marketing campaign attribution data
- Realistic conversion rates and abandonment patterns
- Session-level engagement metrics
Ideal for:
- Conversion optimization analysis
- Personalization algorithm development
- Customer journey mapping
- Marketing attribution modeling
Retail Operations
Integrated retail operations dataset including point-of-sale transactions, inventory movements, supply chain events, staffing, and store performance metrics. Models a multi-region retail chain with diverse store formats and product categories.
Key Features:
- Transaction-level sales data with basket analysis
- Complete inventory lifecycle tracking
- Store layout and merchandising impact factors
- Staffing levels and productivity metrics
- Seasonal and promotional event effects
Ideal for:
- Demand forecasting models
- Inventory optimization
- Store performance analysis
- Staff scheduling optimization
Financial Services
Banking Transactions
Comprehensive banking transaction dataset modeling customer accounts, transactions, balances, and financial products. Includes checking, savings, credit card, loan, and investment activities with realistic financial behaviors.
Key Features:
- Realistic transaction patterns and frequencies
- Merchant categorization and location data
- Account lifecycle events (opening, closing, defaults)
- Simulated fraud patterns and anomalies
- Customer segments with distinct financial behaviors
Ideal for:
- Fraud detection models
- Customer segmentation and targeting
- Financial product recommendation engines
- Risk assessment algorithms
Financial Markets
Market data dataset including price history, trading volumes, order book snapshots, and financial indicators across equity, fixed income, foreign exchange, and derivatives markets. Incorporates realistic market events and correlations.
Key Features:
- Tick-by-tick price data with microsecond timestamps
- Order book depth and liquidity metrics
- Cross-asset correlations and market regimes
- Simulated market events and volatility clusters
- Fundamental data and news sentiment indicators
Ideal for:
- Trading strategy development
- Risk modeling and stress testing
- Market microstructure analysis
- Portfolio optimization algorithms
Healthcare
Healthcare Operations
Healthcare operations dataset modeling patient flow, resource utilization, staffing, and operational metrics across hospital systems. Includes emergency, inpatient, outpatient, and ancillary services with realistic healthcare patterns.
Key Features:
- Patient admission, transfer, and discharge events
- Resource utilization and capacity metrics
- Staff scheduling and productivity data
- Service line performance indicators
- Seasonal illness patterns and surge events
Ideal for:
- Patient flow optimization
- Resource allocation modeling
- Staff scheduling optimization
- Emergency department simulation
Population Health
Longitudinal patient health dataset modeling clinical encounters, diagnoses, procedures, medications, lab results, and outcomes. Includes primary care, specialty care, and acute care with consistent patient journeys across care settings.
Key Features:
- Longitudinal patient records with care continuity
- Realistic disease progression patterns
- Treatment pathways and protocol adherence
- Social determinants of health factors
- Outcomes and readmission patterns
Ideal for:
- Risk stratification models
- Care gap identification
- Clinical pathway optimization
- Readmission prediction
Proof of Concept Implementation
╔════════════════════════════════════════════════╗
║ POC IMPLEMENTATION FLOW ║
╠════════════════════════════════════════════════╣
║ ║
║ 1. REQUIREMENTS ANALYSIS ║
║ ├── Use Case Definition ║
║ ├── Success Criteria ║
║ ├── Technical Requirements ║
║ └── Dataset Selection ║
║ ║
║ 2. ENVIRONMENT SETUP ║
║ ├── Platform Provisioning ║
║ ├── Dataset Loading ║
║ ├── Integration Configuration ║
║ └── Access Provisioning ║
║ ║
║ 3. IMPLEMENTATION ║
║ ├── Data Pipeline Development ║
║ ├── Analytics Model Configuration ║
║ ├── Dashboard Creation ║
║ └── Performance Tuning ║
║ ║
║ 4. VALIDATION ║
║ ├── Functionality Testing ║
║ ├── Performance Benchmarking ║
║ ├── Success Criteria Evaluation ║
║ └── Documentation ║
║ ║
╚════════════════════════════════════════════════╝
Guided POC Methodology
Our structured proof-of-concept implementation process helps you validate your use cases quickly and effectively, with expert guidance at every step. We provide a dedicated POC environment, appropriate sample datasets, and implementation support to ensure successful validation.
Requirements Analysis
- Use case definition: Clearly articulate the business problem and analytical approach
- Success criteria: Define measurable outcomes for POC validation
- Technical requirements: Identify integration points, tools, and constraints
- Dataset selection: Choose appropriate sample datasets that match your use case
Environment Setup
- Platform provisioning: Deploy right-sized infrastructure for your POC
- Dataset loading: Populate the environment with selected sample data
- Integration configuration: Connect your BI tools and analytical applications
- Access provisioning: Set up secure access for your team members
Implementation
- Data pipeline development: Implement transformation workflows for your use case
- Analytics model configuration: Set up appropriate data models and analytical schemas
- Dashboard creation: Develop visualizations to demonstrate insights
- Performance tuning: Optimize queries and processing for efficiency
Validation
- Functionality testing: Verify all components work as expected
- Performance benchmarking: Measure system performance under various conditions
- Success criteria evaluation: Assess achievement of defined outcomes
- Documentation: Capture findings, configurations, and recommendations
Typical Timeline:
Most POCs are completed within 2-4 weeks, depending on complexity and scope. Our structured approach ensures efficient use of time while providing thorough validation of your use case.
Performance Benchmarking
Validate Platform Performance
Our sample datasets enable comprehensive performance benchmarking to validate that the NebulaLake platform meets your specific performance requirements. We provide tools and methodologies to measure and optimize system performance across various workload types.
Data Ingestion
- Batch loading: Measure throughput for various file formats and sizes
- Streaming ingest: Evaluate events-per-second processing rates
- Change data capture: Test latency and throughput for database CDC
- Multi-source ingestion: Benchmark parallel loading from diverse sources
Data Processing
- ETL performance: Measure transformation throughput and resource utilization
- Complex joins: Evaluate large-scale data integration operations
- Aggregation performance: Benchmark group-by and aggregation operations
- Window function efficiency: Test analytical and sliding window operations
Query Performance
- Interactive analytics: Measure response time for BI tool queries
- Concurrent users: Test system performance under multiple user loads
- Complex analytics: Benchmark advanced analytical queries
- Data volume scaling: Evaluate performance across growing data volumes
Scalability
- Elastic scaling: Measure resource scaling response to workload changes
- Cluster expansion: Test linear scaling with additional nodes
- Multi-workload isolation: Evaluate performance under mixed workloads
- Resource utilization efficiency: Measure resource usage optimization
Comprehensive Reporting:
All POC implementations include detailed performance reports with metrics relevant to your use case, comparative analysis, and recommendations for optimization in production environments.
╔════════════════════════════════════════════════╗
║ PERFORMANCE BENCHMARK ║
╠════════════════════════════════════════════════╣
║ ║
║ DATA INGEST ║
║ ───────────────────────────────────────────── ║
║ Batch Load (Parquet) : 2.7 TB/hour ║
║ Batch Load (CSV) : 1.2 TB/hour ║
║ Streaming Ingest : 250K events/sec ║
║ CDC Throughput : 120K changes/sec ║
║ ║
║ DATA PROCESSING ║
║ ───────────────────────────────────────────── ║
║ ETL Throughput : 1.8 TB/hour ║
║ Complex Join (10B x 5M) : 45 minutes ║
║ Aggregation (10B rows) : 12 minutes ║
║ ML Feature Gen (1B rows): 8 minutes ║
║ ║
║ QUERY PERFORMANCE ║
║ ───────────────────────────────────────────── ║
║ Point Queries : 0.5 sec (avg) ║
║ Analytical Queries : 4.2 sec (avg) ║
║ Complex Analytics : 28.7 sec (avg) ║
║ Concurrent Users (50) : 1.3x slowdown ║
║ ║
║ SCALING METRICS ║
║ ───────────────────────────────────────────── ║
║ Scale-Up Time : 45 seconds ║
║ Linear Scaling Factor : 0.92 ║
║ Resource Utilization : 87% ║
║ ║
╚════════════════════════════════════════════════╝
POC Request Process
Initial Consultation
Our data engineering team meets with your stakeholders to understand your use cases, technical requirements, and success criteria. We help you define a focused POC scope that validates your key requirements while remaining achievable within a short timeframe.
Duration: 1-2 days
POC Design & Planning
We design a tailored POC implementation that addresses your specific use case, selecting appropriate sample datasets, defining the technical architecture, and creating a detailed implementation plan with timeline and deliverables.
Duration: 2-3 days
Environment Provisioning
We provision a dedicated POC environment with the appropriate configuration for your use case, load the selected sample datasets, and establish necessary integrations with your tools and systems. Your team receives secure access credentials to the environment.
Duration: 1-2 days
Guided Implementation
Our data engineers work collaboratively with your team to implement the POC, including data pipelines, analytical models, and visualizations. We provide knowledge transfer throughout the process and address any technical challenges that arise.
Duration: 1-2 weeks
Validation & Results Review
We conduct thorough testing and performance benchmarking to validate that the implementation meets your success criteria. A detailed results review session presents findings, demonstrates capabilities, and discusses implications for full-scale implementation.
Duration: 2-3 days
Note: The entire POC process typically takes 2-4 weeks from initial consultation to final results review. The environment remains available for an additional week after completion for further exploration by your team.
Demo Dashboards
Our sample datasets come with pre-built demo dashboards that showcase analytical capabilities and provide starting points for your own visualization development. These dashboards demonstrate best practices for data visualization and analytical techniques.
Industrial IoT Analytics
Comprehensive manufacturing intelligence dashboard with real-time equipment monitoring, predictive maintenance alerts, and efficiency analytics. Demonstrates streaming data visualization, anomaly detection, and predictive modeling with industrial sensor data.
Key Visualizations:
- Real-time equipment status monitoring
- Predictive failure probability indicators
- Maintenance optimization recommendations
- Historical performance trend analysis
- Energy consumption optimization
E-commerce Performance
Comprehensive e-commerce analytics dashboard with customer behavior analysis, conversion funnel visualization, and product performance metrics. Demonstrates clickstream analysis, attribution modeling, and customer segmentation techniques.
Key Visualizations:
- Multi-touch attribution modeling
- Conversion funnel analysis with drop-off points
- Product affinity and recommendation effectiveness
- Customer segment performance comparison
- Cohort retention and lifetime value analysis
Financial Risk Analytics
Comprehensive financial risk management dashboard with fraud detection, credit risk assessment, and market risk analysis. Demonstrates anomaly detection, predictive modeling, and time series analysis with financial transaction data.
Key Visualizations:
- Real-time fraud detection alerts
- Credit risk scoring and portfolio analysis
- Market risk exposure and VaR calculations
- Stress testing scenario analysis
- Regulatory compliance monitoring
Healthcare Operations
Comprehensive healthcare operations dashboard with patient flow visualization, resource utilization analytics, and clinical efficiency metrics. Demonstrates healthcare analytics, resource optimization, and predictive modeling with healthcare operations data.
Key Visualizations:
- Patient flow and bottleneck identification
- Resource utilization and optimization
- Readmission risk prediction
- Length of stay analysis and optimization
- Clinical pathway compliance and outcomes
Ready to Validate Your Use Case?
Contact us today to discuss your proof-of-concept requirements and explore how our sample datasets and POC methodology can accelerate your big data implementation.