Big Data Lake for Iot
Saving and processing a large amount of sensor data for complex machines with thousands of components is a challenge. We built a data lake to integrate the batch, real-time IoT data with other key data required by a large equipment manufacturer, enabling deeper insights and predictive maintenance.
Customer Churn Analytics
For any consumer-facing company such as banks, retail e-commerce, telecom, etc. one of the key factors to revenue and profitability is customer retention. An e-commerce company wanted to understand the factors that led to customer churn and predict which customers may churn
Customer Knowledge Solution
Companies like Amazon serve dynamic content, send personalized recommendations in email campaigns and tailor ads based on our browsing behavior; but this hides the fact that 80% of marketers fail at personalization. A Big Data based Customer Knowledge Solution helps solve the problem.
Data Warehouse Augmentation
A large manufacturing company wanted to improve the reliability of their products by predicting component failure and taking proactive action to repair/replace it. This would result in a saving of millions of dollars for their business operations. Solving the problem required receiving and processing very large amounts of sensor data sent by the components.
Fast SQL Queries
Running complex SQL queries is essential for business analysts. When the data sets are very large, performance can be a challenge. We reduced query times from minutes to seconds for a large retailer trying to run multi-dimensional queries on Hadoop.
Off-load Data Warehouse
A large bank wanted to reduce the load on their Enterprise Data Warehouse as it was reaching its capacity and upgrading it was prohibitively expensive. Maintaining multiple copies for backup and high availability added to the cost of the warehouse. At the same time, it was important that the business users have access to the same analytics and reports using the same BI tools.
Real-time Social Analytics Platform
A Silicon Valley startup building a social analytics platform ran into performance issues as they started scaling the amount of social media data to analyze. They needed an architecture that could ingest a large amount of social media data in real-time.