Alluxio 2.0 is the most ambitious platform upgrade since the inception of Alluxio with greatly expanded capabilities to empower users to run analytics and AI workloads on private, public or hybrid cloud infrastructures leveraging valuable data wherever it might be stored. This preview release, now available for download, includes many advancements that will allow users to push the limits of their data-workloads in the cloud.
This webinar reviews: The observation and analysis of trends of separation of Storage and Compute in Big Data ecosystem; Why and how to build a new data access layer between compute and storage in this data stack; Alluxio open source: history, overview, design, and architecture; Production Use case with Spark, Presto, Tensorflow and etc; A demo of running Presto on Alluxio on S3
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.
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.
Learn more about use cases with Alluxio leveraged in MOMO, JD.com, and TalkingData.
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.
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.
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.
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