NDA-friendly case studies

Practical cloud data engineering work, described without sensitive client details.

These examples are framed as founder experience from enterprise and consultancy environments. They show the type of work Riahi IT Solutions can support without exposing confidential names, metrics, or internal architecture.

Automotive enterprise

Automotive AWS Cost Visibility

AWS, Athena, Glue, Lambda, Step Functions, QuickSight, Terraform, Python, SQL

Problem

Cloud spend signals were spread across accounts and departments, making it hard to explain cost drivers or prioritize optimization work.

Work

Combined AWS account data, cost thresholds, data processing signals, and dashboard reporting into a cloud efficiency view with automated alerts.

Outcome

Improved cost ownership and gave technical and business teams a clearer way to discuss recommendations per account and department.

Industrial / manufacturing environment

Industrial Data Pipeline Reliability

AWS S3, Glue, Step Functions, Lambda, QuickSight, Terraform, GitHub Actions

Problem

Data flows needed stronger orchestration, repeatability, and visibility across ingestion, processing, and reporting layers.

Work

Built AWS data platform components using S3, Glue, Step Functions, Lambda, QuickSight, Terraform, and CI/CD patterns.

Outcome

Created more maintainable delivery paths for data workflows and reduced operational ambiguity for engineering teams.

Industrial IoT / manufacturing demos

Industrial Analytics Demos with HighByte and Litmus

HighByte, Litmus, AWS EC2, S3, Glue, Athena, QuickSight, Grafana, Lambda, Step Functions

Problem

Industrial analytics demos needed to connect edge/platform concepts with cloud analytics in a way that was understandable for technical and business stakeholders.

Work

Used HighByte for data modeling/integration and Litmus for edge connectivity/device management concepts, then connected the demo story to AWS analytics services and dashboards.

Outcome

Created clearer reference demos for industrial data platform conversations and helped translate shop-floor data concepts into cloud analytics workflows.

Data platform engineering

Table Format and Databricks-Oriented Data Engineering

Apache Iceberg, Apache Hudi, Delta Lake, Spark, Databricks, Scala, Java, AWS EMR

Problem

Teams needed practical comparison and implementation knowledge around modern lakehouse table formats and reusable data operations.

Work

Compared Apache Iceberg, Apache Hudi, and Delta Lake, built a unified API for common operations, and worked in a broader stack that included Spark and Databricks.

Outcome

Improved understanding of trade-offs between table formats and created more reusable patterns for data platform engineering work.

Consultancy / analytics delivery

Analytics Engineering Cleanup

dbt, SQL, GitHub Actions, AWS, QuickSight

Problem

Reporting logic and data models needed better structure, deployment automation, and documentation for dependable delivery.

Work

Improved dbt model workflows, deployment automation, SQL logic, documentation patterns, and reporting-ready datasets.

Outcome

Made analytics delivery easier to maintain and gave downstream reporting users more confidence in core datasets.

How to read these examples

The goal is credibility without risky claims. For public sales pages, "founder experience includes..." is more accurate than suggesting every enterprise example was contracted directly through Riahi IT Solutions.

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