Services & Solutions

Cloud-Native Data Pipelines

Accelerate your digital transformation by moving industrial data to the cloud—unlocking elastic scale, advanced AI/ML capabilities, and pay-as-you-go economics without the burden of managing infrastructure.

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Purpose & Pain Points Solved

Cloud-native data pipelines eliminate the constraints of on-premise infrastructure, enabling you to leverage serverless computing, managed services, and AI/ML platforms that scale infinitely while you pay only for what you use.

On-Premise Infrastructure Constraints

Physical servers hit capacity limits, require manual scaling, and need expensive hardware upgrades to handle growing data volumes.

Impact: Unable to scale with business growth, missed opportunities for advanced analytics due to infrastructure limitations

24/7 Infrastructure Management Burden

IT teams spend time patching servers, managing backups, provisioning storage, and handling hardware failures instead of building value-add solutions.

Impact: High operational costs, diverted resources from strategic initiatives, increased downtime

Delayed Time-to-Insight

Lengthy procurement cycles for hardware, weeks to deploy new analytics tools, and manual data preparation delay business value from operational data.

Impact: Slow response to market changes, competitors leveraging cloud move faster with AI/ML initiatives

Inaccessible Advanced Analytics

On-premise infrastructure lacks the scale and tools needed for big data analytics, ML model training, and AI workloads that require massive compute and specialized services.

Impact: Cannot leverage modern AI/ML capabilities, falling behind competitors in digital transformation

What This Service Enables

Unlimited Scale

Cloud services automatically scale to handle any data volume—from kilobytes to petabytes

Advanced AI/ML

Access cutting-edge machine learning services and GPU compute without capital investment

Rapid Innovation

Deploy new analytics in days, not months—no hardware procurement or data center buildouts

Cloud-Native Architecture

A modern, serverless architecture that eliminates infrastructure management and scales automatically.

Edge → Cloud Ingestion

Factory gateways publish data to cloud IoT endpoints via MQTT over TLS

Components:

N3uron/MQTT Clients
AWS IoT Core / Azure IoT Hub
Device Registry & Certificates

Data Flow:

Real-time machine/sensor data streamed from factory floor

Stream Processing

Cloud streaming services buffer and process data in real-time with automatic scaling

Components:

Kinesis / Event Hubs / Kafka
Stream Processing Jobs
Dead Letter Queues

Data Flow:

Filtering, aggregation, windowing, complex event processing

Transformation & Routing

Serverless functions transform data and route to appropriate storage destinations

Components:

Lambda / Azure Functions
Data Validation & Enrichment
Business Logic Processing

Data Flow:

Format conversion, API enrichment, contextual data joining

Storage & Lakehouse

Scalable cloud storage and data warehouse for long-term retention and analytics

Components:

S3 / Azure Data Lake / GCS
Snowflake / BigQuery / Synapse
Data Catalog

Data Flow:

Raw data → structured analytics tables → ML feature stores

Analytics & AI

BI tools and ML platforms leverage cloud compute for insights and predictions

Components:

Power BI / Tableau / Looker
SageMaker / Azure ML / Vertex AI
Jupyter Notebooks

Data Flow:

Dashboards, reports, predictive models, anomaly detection

Enabling Tools & Technologies

We leverage best-in-class cloud services from AWS, Azure, and Google Cloud to build production-grade data pipelines.

Cloud IoT Ingestion Services

Managed IoT endpoints that securely receive device data at massive scale with built-in authentication and device management.

Key Capabilities:

AWS IoT Core: Fully managed MQTT broker with device registry and certificates
Azure IoT Hub: Enterprise IoT platform with device twins and direct methods
Google Cloud IoT Core: Integrated with Pub/Sub for seamless data flow
Automatic scaling to millions of devices and messages per second
TLS 1.2/1.3 encryption and X.509 certificate authentication
Device shadowing and firmware update management
Built-in integration with cloud analytics services

Stream Processing & Event Hubs

Real-time data streaming platforms that buffer, route, and process industrial data in motion at cloud scale.

Key Capabilities:

AWS Kinesis: Real-time streaming with automatic partitioning and scaling
Azure Event Hubs/Stream Analytics: Managed event ingestion with SQL-based processing
Apache Kafka on Confluent Cloud: Enterprise streaming with exactly-once semantics
Google Cloud Dataflow: Unified batch and stream processing
Automatic buffering and replay for fault tolerance
Complex event processing and windowed aggregations
Native connectors to downstream analytics platforms

Serverless Computing & ETL

Event-driven, auto-scaling compute that transforms and routes data without managing servers or infrastructure.

Key Capabilities:

AWS Lambda: Run code in response to events with zero infrastructure management
AWS Glue: Serverless ETL with automatic schema discovery and data catalog
Azure Functions: Event-driven serverless compute with 200+ connectors
Google Cloud Functions: Lightweight event handlers with automatic scaling
Pay only for actual compute time (millisecond billing)
Built-in versioning, monitoring, and error handling
Native integration with cloud storage and databases

Cloud Storage & Analytics Platforms

Scalable, fully-managed data storage and warehousing optimized for analytics workloads with pay-as-you-go pricing.

Key Capabilities:

AWS S3 + Athena: Serverless queries on data lake with SQL
Snowflake: Cloud data platform with unlimited scaling and zero-copy cloning
Google BigQuery: Serverless data warehouse with sub-second queries on petabytes
Azure Synapse Analytics: Unified analytics with integrated data warehousing
Automatic compression, encryption, and lifecycle management
Integration with BI tools (Power BI, Tableau, Looker)
Separation of compute and storage for cost optimization

AI/ML & Advanced Analytics Services

Managed machine learning platforms and specialized analytics services that leverage cloud compute for industrial AI.

Key Capabilities:

AWS SageMaker: End-to-end ML platform with AutoML and model deployment
Azure Machine Learning: Enterprise MLOps with drag-and-drop designer
Google Vertex AI: Unified ML platform with pre-trained models
Databricks: Collaborative data science with managed Spark and MLflow
Pre-built models for anomaly detection, forecasting, computer vision
Distributed training across GPU clusters
One-click model deployment to production endpoints

Multi-Cloud Flexibility

Our solutions work across AWS, Azure, and Google Cloud. Choose the platform that best fits your organization's strategic direction, or implement a hybrid approach that leverages the strengths of each provider.

How We Deploy at Your Site

Our proven methodology delivers production-ready cloud data pipelines with enterprise security and scalability.

1. Cloud Ingestion Infrastructure Setup

3-5 days

Activities:

Select cloud provider and IoT ingestion service (AWS IoT Core, Azure IoT Hub, or Google Cloud IoT)
Set up cloud account, VPC networking, and security groups
Create IoT device registry and generate device certificates
Configure edge gateways (N3uron, MQTT clients) to publish to cloud endpoints
Establish VPN or Direct Connect for secure hybrid connectivity
Test connectivity and validate message throughput and latency

Deliverable: Cloud IoT ingestion receiving production data from factory edge devices

2. Stream Processing Pipeline Configuration

4-6 days

Activities:

Deploy streaming service (Kinesis, Event Hubs, or Kafka on cloud)
Configure data streams with appropriate partitioning and retention
Set up stream processing jobs for real-time transformations (filtering, enrichment, aggregation)
Implement dead-letter queues for error handling
Configure monitoring and alerting for pipeline health
Test failure scenarios and validate data recovery

Deliverable: Real-time streaming pipeline processing and routing data

3. Serverless Transformation & Routing

3-5 days

Activities:

Deploy serverless functions (Lambda, Azure Functions) for data transformation
Implement business logic: data validation, enrichment with external APIs, format conversion
Configure event triggers from IoT ingestion and streaming services
Set up error handling, retries, and logging
Create data routing rules to send processed data to appropriate destinations
Optimize function performance and cold-start times

Deliverable: Serverless functions transforming and routing data automatically

4. Cloud Storage & Lakehouse Setup

4-5 days

Activities:

Create cloud storage infrastructure (S3 buckets, Azure Data Lake, Google Cloud Storage)
Design data lake zones: raw/bronze, curated/silver, analytics/gold
Configure data warehouse or lakehouse (Snowflake, BigQuery, Synapse)
Set up automated data loading from streams and functions
Implement data partitioning and lifecycle policies for cost optimization
Create initial schemas and tables for structured data

Deliverable: Cloud lakehouse receiving and storing processed data

5. Analytics & ML Platform Integration

4-6 days

Activities:

Connect BI tools (Power BI, Tableau, Looker) to cloud data warehouse
Deploy ML platform (SageMaker, Azure ML, Vertex AI) with access to data lake
Set up Jupyter notebooks or data science workspaces
Create example dashboards and analytics queries
Train initial ML models on historical data for proof-of-concept
Configure model deployment pipelines for real-time inference

Deliverable: Analytics and ML platforms consuming cloud data for insights

6. Monitoring, Cost Optimization & Documentation

3-4 days

Activities:

Set up comprehensive monitoring dashboards (CloudWatch, Azure Monitor, Stackdriver)
Configure cost alerts and optimization recommendations
Implement data quality monitoring and anomaly detection
Document architecture diagrams and data flow
Create runbooks for common operations and troubleshooting
Train teams on cloud platform access and self-service analytics
Establish governance policies for data access and resource provisioning

Deliverable: Production cloud pipeline with monitoring, documentation, and trained team

Typical Implementation Timeline

21-31 Days

From initial cloud setup to production-ready data pipelines with AI/ML integration

Business Benefits

Elastic Scalability

Scale from gigabytes to petabytes without infrastructure changes—cloud services automatically provision resources as data volume grows.

Rapid Time-to-Value

Deploy new analytics capabilities in days instead of months—no hardware procurement or data center buildouts required.

Zero Infrastructure Management

Cloud providers handle patching, backups, high availability, and disaster recovery—your team focuses on business value, not servers.

Access to Advanced AI/ML

Leverage cutting-edge AI services and GPU clusters that would be cost-prohibitive to build and maintain on-premise.

Pay-as-You-Go Economics

Pay only for resources actually consumed—no capital expenditure on hardware that sits idle during off-peak periods.

Enterprise Security & Compliance

Built-in encryption, compliance certifications (SOC 2, ISO 27001, HIPAA), and enterprise-grade security by default.

On-Premise vs. Cloud Economics

Small Deployment (1-10 sites)

On-Premise Approach

High initial capex ($50K-$200K), fixed costs regardless of usage

Cloud-Native Approach

Low starting costs ($500-$2K/month), scales with actual usage

Medium Deployment (10-50 sites)

On-Premise Approach

Significant hardware refresh cycles, dedicated IT staff required

Cloud-Native Approach

Predictable opex, automatic scaling, no hardware refresh needed

Large Deployment (50+ sites)

On-Premise Approach

Multiple data centers, expensive storage arrays, complex DR setup

Cloud-Native Approach

Global presence included, unlimited storage, built-in redundancy

Advanced Analytics (ML/AI)

On-Premise Approach

Prohibitively expensive GPU clusters sitting idle most of the time

Cloud-Native Approach

Pay-per-use GPU compute, access to latest AI services and pre-trained models

Common Use Cases

Real-Time Production Analytics at Scale

Stream production data from 50+ global factories to AWS IoT Core. Kinesis processes millions of events per day, Lambda functions enrich with ERP context, and Snowflake provides unified analytics across all sites.

Cloud Services:

AWS IoT Core → Kinesis → Lambda → S3 → Snowflake → Power BI

Outcome: Global production visibility in real-time with 90% reduction in infrastructure management overhead

Predictive Maintenance with Cloud ML

High-frequency vibration data streamed to Azure IoT Hub, processed through Stream Analytics, stored in Azure Data Lake. Azure ML trains predictive models on GPU clusters to forecast equipment failures weeks in advance.

Cloud Services:

Azure IoT Hub → Stream Analytics → Data Lake → Azure ML → Power Apps

Outcome: 40% reduction in unplanned downtime, models retrained automatically as new patterns emerge

Energy Optimization with Serverless Analytics

Power meter data from 100+ sites flows to Google Cloud IoT Core. Cloud Functions aggregate by facility and time period. BigQuery enables SQL analysis of consumption patterns. Vertex AI optimizes HVAC and production schedules.

Cloud Services:

Google IoT Core → Pub/Sub → Cloud Functions → BigQuery → Vertex AI

Outcome: 12% energy cost reduction through ML-driven optimization with zero infrastructure management

Quality Analytics & Root Cause Analysis

In-line quality measurements, process parameters, and material batches streamed to cloud. Databricks lakehouse unifies data for data scientists to perform root cause analysis using Spark SQL and MLflow models.

Cloud Services:

AWS IoT Core → Kinesis → S3 → Databricks → MLflow → Tableau

Outcome: Root cause analysis time reduced from weeks to hours, 25% reduction in quality defects

Ready to Modernize Your Data Architecture?

Stop managing infrastructure and start generating insights. Build cloud-native data pipelines that scale infinitely, leverage cutting-edge AI/ML, and accelerate your digital transformation.

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