← Back to case studies
Professional Support Technologies
End-to-End AWS Data Platform: ETL, ML, and BI for a Cloud Consulting Company
Sources unified
CRM · ERP · IoT · APIs
KPI visibility
per-system
→
real-time, one place
Professional Support Technologies provides solution design, consultancy, and software implementation across cloud infrastructure, big data, and machine learning.
What changed
Before
Sources
CRM
ERP
IoT
APIs
Integration
None
reported separately
After
Extract
ETL pipeline
all four sources
Lake
AWS S3
structured + unstructured
Model
Forecasting
Anomaly detection
Consume
AWS QuickSight
sales · marketing · ops
Four disconnected source systems consolidated into an S3 lake, with forecasting and BI on top of the same data.
What we built
The client needed a unified data platform to support multiple business departments — from raw data ingestion through ETL to machine learning models and executive-level BI dashboards. The platform needed to run reliably on AWS, cover the full data lifecycle from ingestion to prediction, and be maintainable by a lean internal team.
Key Features
ETL: gathered data from multiple sources and systems into a centralized data lake, ready to be consumed by different departments.
DevOps: designed and automated infrastructure pipelines for downstream data integration on AWS.
ML: trained models on historical and daily-updated data; deployed models for real-time and batch predictions.
BI: built dashboards for different departments to visualize data and model predictions.
Monitoring: set up monitoring and alerting for the entire infrastructure and applications.
Security: ensured data and applications met security and compliance requirements.
Automated the full model lifecycle — training, testing, and deployment — using CI/CD pipelines.
Results
KPIs generated and published to AWS QuickSight, providing real-time visibility across sales, marketing, and operations.
Designed and implemented a robust ETL pipeline to extract, transform, and load data from multiple sources including CRM, ERP, IoT systems, and APIs.
Consolidated all structured and unstructured data into an AWS S3-based data lake.
Developed and trained machine learning models using historical and daily-updated data for accurate forecasting and anomaly detection.
Deployed models using AWS SageMaker, enabling real-time predictions and batch processing.
Created interactive dashboards and reports using QuickSight, providing insights for sales, marketing, and operations teams.
Stack
Engagement: Ongoing · Real-time & Industrial Data Platforms, Product Engineering, Platform & Cloud Engineering