Big data Analytics Services

The advent of sharing economy has brought a sea change in the way urban populace commute locally. The Ubers, Lyfts and many other local players have made taxi riding convenient, affordable and safe. These rides have emerged as a strong alternative to the public transport clocking millions of rides per month in some cities. The emergence of hyper-local delivery models to optimize the supply chain has also led to a large number of daily trips by these vehicles. These developments have mandated the installations of either standalone or smartphone app-based GPS devices to keep track of and better regulate these rides and a fleet of taxis. These GPS systems spew a ton of data generating up to GBs of data per second. With the automobile & technology experts predicting that self-driving cars would replace human-driven cars in no more than a decade, the volume and velocity of GPS data is only set to increase. With that context in mind, it becomes imperative to understand the GPS data

The sudden increase in the volume of data from the order of gigabytes to zettabytes has created the need for a more organized file system for storage and processing of data. The demand stemming from the data market has brought Hadoop in the limelight making it one of biggest players in the industry. Hadoop Distributed File System (HDFS), the commonly known file system of Hadoop and Hbase (Hadoop’s database) are the most topical and advanced data storage and management systems available in the market. What are HDFS and HBase? HDFS is fault-tolerant by design and supports rapid data transfer between nodes even during system failures. HBase is a non-relational and open source Not-Only-SQL database that runs on top of Hadoop. HBase comes under CP type of CAP (Consistency, Availability, and Partition Tolerance) theorem. HDFS is most suitable for performing batch analytics. However, one of its biggest drawbacks is its inability to perform real-time analysis, the trending requireme

Price discrimination and downward demand spiral are widely used analytical concepts/practices in the Airlines and Hospitality industries respectively, long before the term Big Data Analytics was even coined. Incidentally, these concepts have been taught in global elite b-schools for decades. So, how come Analytics, which has been there in practice for decades experience a meteoric rise suddenly? To answer this question, we need to get the Big Picture. Given below are key factors that led to huge buzz around analytics today. Proliferation of Data Sources:  Every day we create  5 quintillion bytes  of data. This comes from digital footprints left on social media platforms, IoT sensors, wearables, transactions to name a few. Interesting fact is that  only 1% of data collected is ever analyzed . To put into perspective all that innovation and insights driven by analytics are from analyzing just 1% of the data collected globally. Change in Customers’ expectations: