You may have booked a ride with Uber and realized that the process was simple. Within a few minutes, you had already taken the ride. However, what you didn’t know is that a lot happens behind the scenes to make that process smooth. Did you know that Uber is one of the giant ride-hailing companies across the world?
Uber serves approximately 8 million users in its platform. It makes over 1 billion trips across 449 cities spread over the globe. Therefore, this ride-sharing firm must be experiencing several challenges. For instance, Uber has to deal with poor transportation infrastructures in certain cities, poor fulfillment, and unsatisfactory customer experience, among others.
The company generates big data every day, relies on Data Science and Artificial Intelligence to make a decision and give you a pleasant experience.
Uber relies on big data since its survival depends on how good its data is. The more the data, the greater the information it’s able to generate. This helps it to understand its customers’ behaviors and patterns. These are the two factors that influence its profits.
Some of Uber’s big data infrastructures in the public domain are:
- Hadoop Data Lake; Uber leverages big data processing analytic capabilities to tackle its operational challenges that can’t be addressed using conventional data warehousing technologies.
- Spark and Hadoop; the two data frameworks can be used as alternatives. Both of them are mutually exclusive, but they work better when they are paired. Thus, Uber uses the two to process its data.
- Uber generates its data from databases and data types like database tables, SOA, event messaging system, or Apache Kafika and schema less data stores.
Uber uses machine learning algorithms to analyze its multiple data and forecast where the highest demand will be, so that it can re-direct its drivers there. This insight will monitor demand and supply to ensure that it doesn’t implement surge pricing. Machine learning teaches Uber’s technologies to serve you and improve your experience.
Uber has hired data analysts to study its back-end extract obtained from its application. They are behind its predictive models on its product front. These models can predict the exact time the driver will be at your door, or the estimated fares. The statistical data analysis can give Uber’s customers a positive user-experience.
Data Science Tools
Uber team uses Python in its programming language. Other third-party modules that the cab sharing company uses are SciPy, NumPy, Pandas, and Matplotlib. The data science team uses R programming language, Matlab and Octave intermittently for its single data science projects. It also uses data visualization tools like D3 and SQL frameworks such as Postgres.
Uber has a massive database of its customers and drivers. The firm uses algorithms to match a customer’s request with the closet driver. It also stores data of other drivers without customers so that it can use it to predict demand and supply. Therefore, Uber uses data science and machine learning to support its operation.
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