HomeBlock ChainHarnessing the Potential of Presto: A Case Study of Uber

Harnessing the Potential of Presto: A Case Study of Uber

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The magic behind Uber’s data-driven success lies in its role as a data and analytics powerhouse. While many recognize Uber as a ride-hailing service, behind the scenes, it relies heavily on data analysis to make informed decisions. This blog explores the world of Uber’s analytics and the significant role played by Presto, an open-source SQL query engine, in driving their success.

At its core, Uber’s business model is simple – connecting customers with drivers for transportation. However, every transaction, no matter how small, has a significant impact on their bottom line. Uber’s ability to leverage data effectively is crucial to its success as a transportation, logistics, and analytics company.

To handle the scale of their analytical needs, Uber relies on high-performance data platforms. With operations in over 10,000 cities and more than 18 million trips per day, Uber stores a massive amount of data. They process 35 petabytes of data daily, supporting 12,000 monthly active users running over 500,000 queries each day. To handle this enormous undertaking, Uber chose Presto, an open-source SQL query engine developed by Facebook.

Presto is a distributed SQL query engine designed for data analytics that excels in scalability and supports a wide range of analytical use cases. It separates analytical processing from data storage, allowing for efficient query optimization. Its distributed nature enables scalability for petabytes and exabytes of data.

Uber’s journey with analytics started with a traditional analytical database, but as their business grew, they needed a more flexible solution to handle increasing data volumes and decision-making requirements. They implemented a file-based data lake alongside their existing analytical database, but realized that it wasn’t fast enough for near-real-time engagement. This led them to choose Presto due to its scalability and ANSI-SQL compatibility.

As Uber’s usage of Presto grew, they joined the Presto Foundation, contributing to its growth and scalability. They focused on areas such as automation, workload management, and security to support their evolving analytical needs. Uber has leveraged Presto’s capabilities to analyze complex data types, extend analytical capabilities with out-of-the-box and custom-defined functions, and even push the boundaries of real-time analytics.

In conclusion, Uber’s data-driven success relies on its ability to effectively leverage analytics. Presto, as an open-source SQL query engine, plays a critical role in Uber’s analytical journey, providing scalability, flexibility, and real-time capabilities necessary to handle the immense volume of data generated by Uber’s operations.

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