Client Vision
Our client, a technology-driven utility solutions company, is building a next-generation analytics platform to support their expanding network of smart water meters across North America. With real-time data pouring in from thousands of distributed devices, they needed a cloud-native architecture capable of ingesting, transforming, and analyzing telemetry data at scale.
The challenge wasn’t just about storage — it was about creating a maintainable, scalable data platform that could power both daily reporting and long-term analytics, while also preparing for future growth and advanced use cases like real-time alerting, customer dashboards, and predictive maintenance.
Our Approach
To demonstrate our ability to meet the client’s goals, we designed and developed an end-to-end MVP (Minimum Viable Product) that turns raw telemetry into structured, analytics-ready datasets — using only modern, scalable, serverless AWS services.
We focused on three goals:
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Architecting a future-proof, modular data pipeline
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Balancing real-time and batch data processing needs
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Designing for rapid insights via visualization tools or APIs
Our approach emphasized separation of concerns, observability, and cost-effective scalability.
Solution Highlights :
Data Ingestion
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Devices transmit meter data (JSON format) to a secure entry point.
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AWS Lambda receives data or pulls from an upstream API/SFTP source.
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AWS Kinesis Firehose is optionally used for high-throughput, real-time ingestion.
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Raw telemetry is stored in Amazon S3 (Landing Zone).
Data Processing & ETL
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AWS Glue Crawlers scan incoming files and infer schema for cataloging.
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Glue Jobs convert JSON to partitioned Parquet format, enriching and cleansing data.
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Cleaned data is stored in a separate S3 bucket (Clean Zone).
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Glue Workflow coordinates ETL dependencies and scheduling.
Data Storage & Modeling
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Amazon Redshift stores curated datasets for heavy analytical querying.
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Glue Crawler syncs Redshift table metadata for external access via Athena if needed.
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DynamoDB stores real-time meter alerts (e.g., leaks, pressure anomalies).
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Aurora PostgreSQL supports device onboarding, metadata storage, and application-layer queries.
Data Monitoring
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AWS CloudWatch monitors Lambda timeouts, Glue job failures, Redshift performance, and ingestion health.
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Alerts are configured to detect pipeline bottlenecks or long-running jobs early.
Data Access & Visualization
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Amazon QuickSight and/or a Custom Full-Stack App (React, API Gateway) connect to Redshift for dashboards.
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The app supports tenant-aware dashboards, operational KPIs, and interactive alert history for end-users.
Outcome
While this was a proactive MVP developed without a formal assignment, it reflects how we approach real client work:
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Start from the business goals
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Design with clarity and scale in mind
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Implement fast, iterate smart, and monitor everything
We believe this MVP can serve as a strong foundation for any utility provider or IoT platform looking to take control of their data and unlock real-time visibility and strategic insights.