Tech Talk: Interactive Analytics with the Starburst Presto + Alluxio stack for the Cloud

As data analytic needs have increased with the explosion of data, the importance of the speed of analytics and the interactivity of queries has increased dramatically. 

In this tech talk, we will introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3, and others in public cloud, hybrid cloud, and multi-cloud environments.

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TalkingData Case Study: Leading Data Broker in China Leverages Alluxio to Unify Terabytes of Data Across Disparate Data Sources

TalkingData’s largest data broker, provides data intelligence solutions and processes over 20 terabytes of data and more than one billion session requests per day. TalkingData deployed Alluxio to unify disparate cloud, on-premise, and hybrid data sources for a range of analytics applications. The architecture provides self-service data access for data scientists and engineers, eliminating the need for ETL or manual IT assistance.

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Myntra Case Study: Accelerating Analytics in the Cloud for Mobile E-Commerce

Myntra, a division of Flipkart, is a leading fashion retailer in India offering customers a wide range of merchandise through a mobile application. An analytics pipeline in Amazon Web Services (AWS) cloud processes customer data to make recommendations, present ads, and deliver other aspects of a tailored experience. Myntra deployed Alluxio to provide a virtual data layer connecting AWS S3 to the analytics pipeline to accelerate data access and enable faster customer response and interactive business intelligence.

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Myntra Case Study: Accelerating Analytics in the Cloud for Customized Mobile E-Commerce

While looking for ways to streamline our data pipeline, we learned about Alluxio, an open source, memory speed, virtual distributed file system. We deployed Alluxio as the shared data layer for all of the intermediate stages in the data pipeline. By reading and writing data in Alluxio, the data can be read concurrently and stay in memory for the next stage of the pipeline. This increased the performance by speeding up the entire pipeline, and increased overall throughput of the pipeline allowing us to provide interactive response to our app users.

Whitepaper: MOMO – Accelerating Ad Hoc Analysis with Spark SQL and Alluxio

From our friends at MOMO The hadoop ecosystem makes many distributed system/algorithms easier to use and generally lowers the cost of operations. However, enterprises and vendors are never satisfied with that, so higher performance becomes the next issue. We considered several options to address our performance needs and focused our efforts on Alluxio, which improves performance … Continued

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Lenovo Analyzes Petabytes of Smartphone Data from Multiple Locations and Eliminates ETL with Alluxio

Lenovo is the world’s largest personal computer vendor and one of the world’s largest smartphone vendors. The company has invested extensively in global information technology infrastructure, including ten data centers worldwide collecting petabytes of smartphone data. Analyzing data located in multiple data centers world-wide is critical for Lenovo to understand and improve the usability and reliability of their products.

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Lenovo Case Study: Analytics on Data from Multiple Locations and Eliminating ETL

Lenovo is an Alluxio customer with a common problem and use case in the world of data analytics. They have petabytes of data in multiple data centers in different geographic locations. Analyzing it requires an ETL process to get all of the data in the right place. This is both slow, because data has to be transferred across the network, and costly because multiple copies of the data need to be stored. Freshness and quality of the data can also suffer as the data is also potentially out of date and incomplete because regulatory issues prevent certain data from being transferred.