Our Projects
We take pride in the solutions we've delivered. Below are some examples of projects our team has successfully completed.
Enterprise Data Warehouse Migration
EnterpriseMigrated and consolidated critical business data from a siloed legacy system into a centralized cloud data warehouse.
Challenge
The client had multiple departmental data silos with inconsistent reporting, making it difficult to get a unified view of business metrics. Weekly business reviews were time-consuming and often based on outdated information.
Solution
We designed and implemented a comprehensive data migration strategy, moving multiple departmental data tables into a unified schema on AWS. Built automated ETL pipelines using Apache Airflow to ensure continuous data synchronization.
Outcome
Achieved zero downtime during migration, improved reporting accuracy by 95%, and established a single source of truth for weekly business reviews. Decision-making speed increased by 3x with real-time data access.
Technologies Used
Real-Time Analytics Platform
Financial ServicesModernized a legacy data warehouse for a leading financial services firm by implementing a real-time big data analytics platform.
Challenge
The financial firm relied on batch processing that ran overnight, causing delays in fraud detection and limiting their ability to respond to market changes quickly. Their legacy system couldn't handle the volume of real-time transactions.
Solution
Implemented a streaming data architecture using Apache Kafka for real-time ingestion and Snowflake as the cloud data warehouse. Built real-time dashboards with Tableau and created automated alerts for suspicious activities.
Outcome
Enabled real-time fraud detection, reducing fraudulent transactions by 40%. Improved market responsiveness with up-to-the-minute analytics. The platform now processes over 1 million transactions per hour.
Technologies Used
Cloud Data Platform for Investment Firm
Venture CapitalDeveloped an end-to-end data platform on Google Cloud for a venture capital organization to streamline investment analytics.
Challenge
The VC firm struggled with manual data collection from various sources including portfolio companies, market data providers, and internal systems. Due diligence and portfolio analysis were time-consuming and error-prone.
Solution
Built a comprehensive data platform on GCP using BigQuery as the central warehouse. Implemented Airbyte for data ingestion from multiple sources and DBT for transformation. Created automated pipelines with Airflow and interactive dashboards with Power BI.
Outcome
Reduced time for portfolio analysis from days to hours. Analysts gained self-service access to unified data, improving investment decision speed by 60%. Cost-effective solution saved $200K annually compared to traditional BI tools.
Technologies Used
Telecom Data Lake & BI Transformation
TelecommunicationsBuilt a scalable data lake platform for a telecommunications company to unify and analyze data from multiple sources.
Challenge
The telecom company had data scattered across customer systems, network logs, and sales platforms. Their traditional OLAP warehouse on SQL Server couldn't scale to handle the volume of network performance data and customer interactions.
Solution
Designed a hybrid architecture with AWS S3 as the data lake for raw data and Snowflake as the analytics engine. Implemented Matillion ETL for data transformation and built comprehensive BI reports in Power BI covering customer churn, network performance, and sales analytics.
Outcome
Enabled advanced analytics including customer churn prediction with 85% accuracy. Improved network performance monitoring led to 30% reduction in downtime. Unified platform now supports both operational and strategic decision-making.
Technologies Used
E-commerce Product Recommendation System
E-commerceDeveloped a cloud-based machine learning pipeline for an e-commerce client to deliver real-time product recommendations.
Challenge
The e-commerce platform had millions of users but low engagement rates. Generic product displays led to poor conversion rates, and they lacked the infrastructure to personalize the shopping experience at scale.
Solution
Built a complete ML pipeline capturing user activity data with Apache Spark, implementing collaborative filtering algorithms. Deployed models using AWS SageMaker as REST APIs integrated with their website. Implemented strict data security with encryption and access controls.
Outcome
Increased sales conversion by 25% through personalized recommendations. Customer engagement improved by 40% with relevant product suggestions. The system scales to handle millions of users with sub-second response times.
Technologies Used
FinTech Fraud Detection Platform
Financial TechnologyImplemented a comprehensive data platform and ML models for a fintech company to detect fraudulent transactions and assess credit risk.
Challenge
The fintech startup was experiencing rapid growth but lacked sophisticated fraud detection capabilities. Manual review processes were slow and couldn't scale with transaction volume, leading to both false positives and undetected fraud.
Solution
Created a real-time fraud detection system using AWS services. Built ML models with TensorFlow for transaction scoring and deployed them via SageMaker. Implemented real-time dashboards with QuickSight for fraud analysts to monitor and investigate suspicious activities.
Outcome
Reduced financial losses from fraud by 65% within the first quarter. Real-time alerts enabled immediate action on suspicious transactions. Improved legitimate transaction approval rates by reducing false positives by 40%.
Technologies Used
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