- QA Project Execution with Healthcare Company
- Data Migration with Pharmaceutical Company
- Data Quality Management with Hospitality Company
- Data Governance with Healthcare Company
- Meta Data Management with Financial Company
- Data Risk Management with Travel Company
- Data Privacy Management with Transporation Company
- AI-Driven Services with Healthcare Company
- Advisory Services with Logistics Company
PROJECTS
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Real-time Customer 360 Dashboard with Predictive Churn
What we did: We developed a sophisticated Real-time Customer 360 Dashboard by building robust hybrid data pipelines. This involved ingesting customer interaction data from various sources via Azure Event Hubs, feeding into Azure Data Lake Storage Gen2. Azure Databricks was central to our solution:
We utilized Databricks' Spark Streaming capabilities to process high-volume, real-time clickstream and application usage data, unifying it with historical customer information.
Delta Lake on Databricks served as the unified data layer, ensuring ACID transactions and data quality for the "Customer 360" profile.
We developed and deployed a customer churn prediction model using Databricks Machine Learning (MLflow), leveraging historical data processed within Databricks.
Databricks SQL Endpoints were configured to provide high-performance access to the aggregated customer profiles for Power BI and other analytical tools. The platform was architected as scalable microservices, with real-time RESTful APIs developed using Azure Function Apps to serve dynamic customer profiles and churn predictions, directly querying or triggering processes in Databricks.
Key Technologies: Azure Data Factory, Azure Data Lake Storage Gen2, Azure Databricks (Spark Streaming, Delta Lake, MLflow, SQL Endpoints), Azure Event Hubs, Azure Function Apps, RESTful APIs, Power BI.
Impact: This solution provides immediate, holistic insights into customer behaviour, enabling targeted marketing campaigns, proactive retention strategies, and enhanced customer satisfaction through data-driven personalization.
Team Size: 5 Data Engineers, 2 ML Engineers, 1 Product Manager, 1 UI/UX Designer.
- Intelligent Inventory Optimization System
What we did:
Our team engineered an Intelligent Inventory Optimization System designed to revolutionize supply chain management. We established comprehensive hybrid ETL data pipelines using Azure Data Factory to integrate diverse data sources including historical sales, current stock levels, logistics data, and external factors like weather. Azure Databricks formed the analytical backbone:
Spark on Databricks was used for large-scale data cleaning, transformation, and complex feature engineering from disparate sources into a unified Delta Lakehouse.
We developed and deployed demand forecasting models (e.g., time-series models) and supply chain anomaly detection models within Databricks, utilizing MLflow for experiment tracking and model management.
Complex optimization algorithms were run in Databricks Spark to calculate optimal reorder points and safety stock levels based on forecasts and real-time conditions. These insights and recommendations were then exposed securely and efficiently via high-performance RESTful APIs, built using Azure Functions and managed by Azure API Management, allowing integration with enterprise resource planning (ERP) systems and internal dashboards.
Key Technologies: Azure Data Factory, Azure Data Lake Storage Gen2, Azure Databricks (Spark, Delta Lake, MLflow, complex optimization algorithms), Azure Functions, Azure API Management, RESTful APIs.
Impact: The system significantly improves inventory turnover, reduces carrying costs by minimizing excess stock, mitigates stockouts, and enhances overall supply chain resilience and efficiency through accurate forecasting and actionable recommendations.
Team Size: 4 Data Engineers, 2 ML Engineers, 1 Business Analyst, 1 Solution Architect.
- Enterprise Hadoop-to-Azure Cloud Migration
What we did:
We successfully executed a comprehensive Enterprise Hadoop-to-Azure Cloud Migration, facilitating a seamless transfer of petabytes of historical data, intricate Hive schemas, and analytical workloads from on-premises Hadoop infrastructure to a scalable, cloud-native Azure environment.
Azure Data Factory was used for orchestrating the large-scale data transfer from HDFS to Azure Data Lake Storage Gen2.
Azure Databricks played a crucial role in the re-platforming and modernization of existing Hadoop workloads:
Complex Hive queries were re-engineered into optimized Spark SQL jobs on Databricks.
Existing MapReduce and custom Spark jobs were migrated and optimized to leverage Databricks' high-performance runtime and Auto-scaling capabilities.
We utilized Delta Lake on Databricks to create reliable, ACID-compliant data lakes, replacing traditional HDFS and Hive table structures.
Databricks notebooks were instrumental in data validation, transformation, and schema evolution during the migration process. The project culminated in the creation of new RESTful APIs to provide modern, flexible data access layers for various downstream applications, directly sourcing from the newly migrated and transformed data in Azure.
Key Technologies: Azure Data Factory (for orchestration and data movement), Azure Data Lake Storage Gen2, Azure Databricks (Spark, Delta Lake, SQL Endpoints, workload re-platforming), Azure Synapse Analytics (for cloud data warehousing where applicable), RESTful APIs.
Impact: This migration modernized our client's data capabilities, resulting in superior scalability, significantly enhanced analytical performance, reduced operational overhead by moving from on-prem hardware, and greater agility for future data initiatives, including the integration of advanced analytics and generative AI applications.
Team Size: 6 Data Engineers, 2 Cloud Architects, 1 Project Manager, 1 QA Engineer.
- Cross-Channel Marketing Attribution Platform
What we did:
We developed a Cross-Channel Marketing Attribution Platform to provide a unified view of customer journeys across various marketing touchpoints and accurately attribute conversions. This involved establishing complex hybrid data pipelines to ingest marketing campaign data (from CRM, ad platforms – both on-premises and cloud), website analytics, and customer interaction data. Azure Databricks was foundational:
We leveraged Databricks Delta Live Tables (DLT) to build robust, medallion-architecture ETL pipelines, ensuring data quality and lineage from raw marketing data to conformed gold-layer attribution models.
Spark on Databricks performed sophisticated graph analytics and machine learning models to identify optimal attribution paths and calculate conversion credit across channels.
MLflow within Databricks was used to track experiments for different attribution models (e.g., Shapley value, Markov chains) and manage their deployment. The platform's analytical insights were exposed via highly performant RESTful APIs, enabling marketing teams to retrieve real-time attribution scores and campaign performance metrics, driving more effective budget allocation.
Key Technologies: Azure Data Factory, Azure Data Lake Storage Gen2, Azure Databricks (Delta Live Tables, Spark GraphFrames, MLflow, Delta Lake), Azure Event Hubs, Azure Function Apps, Azure API Management, RESTful APIs, Power BI/Custom Dashboards.
Impact: The platform provides a clear understanding of marketing ROI across channels, optimizes budget allocation, improves campaign effectiveness, and drives higher conversion rates by identifying the true impact of each customer touchpoint.
Team Size: 5 Data Engineers, 2 Data Scientists, 1 Marketing Analyst, 1 Solution Architect.
- IoT Device Telemetry Analytics & Anomaly Detection
What we did:
We engineered an IoT Device Telemetry Analytics & Anomaly Detection solution for real-time monitoring and predictive maintenance of connected devices. This involved designing a high-throughput hybrid ETL pipeline to ingest massive volumes of streaming telemetry data from global IoT devices (some routed through on-premises gateways) into Azure. Azure Databricks was at the heart of the processing:
Azure Stream Analytics provided initial real-time filtering and aggregation for immediate alerts, while Azure Event Hubs acted as the ingestion point for the raw stream.
Databricks Spark Streaming was crucial for continuous ingestion and transformation of high-velocity IoT data, writing to Delta Lake for historical analysis and serving.
We developed and deployed anomaly detection models (e.g., using unsupervised learning techniques) within Databricks, which continuously analysed device telemetry to identify abnormal behaviour indicating potential failures. These models were managed and deployed using MLflow.
Critical device health data and anomaly alerts were exposed via RESTful APIs developed with Azure Functions, allowing integration with operational dashboards, enterprise asset management systems, and automated alert systems for field technicians.
Key Technologies: Azure IoT Hub/Event Hubs, Azure Stream Analytics, Azure Data Factory, Azure Data Lake Storage Gen2, Azure Databricks (Spark Streaming, Delta Lake, MLflow, Anomaly Detection Algorithms), Azure Function Apps, Azure API Management, RESTful APIs.
Impact: This solution enables predictive maintenance, reduces costly unplanned downtime, optimizes asset utilization, and improves overall operational efficiency by transforming raw telemetry into actionable insights and automated alerts.
Team Size: 4 Data Engineers, 2 ML Engineers, 1 IoT Solution Architect, 1 Operations Specialist.
