• Home
  • Services
  • Our work
  • Case Studies
  • Contact Us
  • Blog
  • Privacy Policy
  • More
    • Home
    • Services
    • Our work
    • Case Studies
    • Contact Us
    • Blog
    • Privacy Policy
  • Home
  • Services
  • Our work
  • Case Studies
  • Contact Us
  • Blog
  • Privacy Policy

AI-Integrated Lifestyle Data Pipeline

A modern, privacy-aware data pipeline that ingests fitness and nutrition data, applies AI-driven recommendations, and powers real-time dashboards.
Built to showcase scalable, production-grade architecture for analytics, machine learning, and compliance. 

 

Core Capabilities

  • Ingests fitness and nutrition data from multiple personal tracking sources.
  • Supports dashboards with trend analysis and alerting.

 

Architecture & Modularity

  • Modular design decouples ingestion, transformation, and storage layers. 
  • Swap data warehouse easily (BigQuery → Snowflake → Azure Synapse). 
  • Dimensional reporting model with views for flexible visualization (Power BI → Streamlit → Looker).
     

Data Quality & Governance

  • Data contracts enforce schema and column-level constraints.
  • Automated testing and routing of error rows.
  • Schema drift checks before deployment.
  • All models are tagged, documented, and contract-enforced.
  • Supports multiple quality levels for ML, analytics, and reporting.
     

Privacy & Compliance

  • Automatically detects and redacts suspect PII.
  • Includes consent-aware logic for ethical data modelling and GDPR alignment.
     

AI-Enhanced Features

  • Generates synthetic data to validate pipeline logic without exposing sensitive user data.
  • Provides anomaly detection for incoming data.
     

Deployment & Tooling

  • CI/CD for automated, controlled deployments. 
  • All changes flow through Dev → Prod unless overridden under strict protocol. 
  • Built on modern stack.


This platform is part of an ongoing project to demonstrate high-quality, AI-integrated data architecture.

Full Case study here!

Azure SQL Transformation Pipeline - Proof of Concept

Client: Insurance (confidential)
Sector: Insurance Technology
Scope:
Designed and delivered a proof of concept (POC) pipeline to validate a scalable transformation approach using Azure-native tools and modern data architecture patterns.

The main question being whether semi-structured data could be transformed into a specific format with minimum custom logic. 

 

Key Contributions

  •  Defined and implemented a two-path transformation workflow.
  •  Built prototype logic for automated structure generation and data population.
  •  Implemented lightweight logging and observability for early-phase validation.
  •  Deployed storage, compute, and orchestration components in the client's Azure tenant.

 

Result

Enabled the client to confidently proceed toward full implementation, informed by technical findings and validated architectural decisions.

Data Warehouse Strategy

Client: Insurance (confidential)
Sector: Insurance Technology
Scope:
Participated in a focused strategic session to assist with planning an upcoming data warehouse implementation. Reviewed early-stage materials and contributed architectural input ahead of a scheduled demo and proposal.

 

Key Contributions

  • Assessed the client’s current data infrastructure and upcoming needs.
  • Provided recommendations on tooling, data modelling approach, and  integration layers. 
  • Outlined a scalable architecture suitable for insurance analytics. 
  • Contributed technical recommendations that informed tooling choices, data model scope, and demo readiness. 

 

Result

Enabled the client to refine their proposal and demo architecture with confidence, reducing ambiguity and aligning stakeholders on technical direction.

Copyright © 2025 SchemaNest LTD All rights reserved. 

  • Privacy Policy

Powered by

This website uses cookies.

We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.

DeclineAccept