Are you using SQL engines, such as Presto, to query existing Hive data warehouse and experiencing challenges including overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications?
This article goes through a simple example to illustrate how Structured Data Management available in the latest Alluxio 2.2.0 release to help SQL and structured data workloads.
This article introduces Structured Data Management available in the latest Alluxio 2.2.0 release, a new effort to provide further benefits to SQL and structured data workloads using Alluxio.
Alluxio, as a data orchestration layer provides the physical data independence, for Presto to interact with the data more efficiently. In addition to caching for IO acceleration, Alluxio also provides a catalog service to abstract the metadata in the Hive Metastore, and transformations to expose the data in compute-optimized way. In this talk, we describe some of the challenges of using Presto with Hive, and introduce Alluxio data orchestration for solving those challenges.
Alluxio 2.0 release was the biggest update since the birth of the project “Tachyon” from UC Berkley’s AmpLab. Gathering feedback from our Open Source Community and enterprise users, Alluxio 2.0 expands the system in three major directions including improving the operability of the system, having more advanced data management, as well as re-architecting the system to be able to scale to 1 billion + file. The system is now cloud native on AWS, Google Cloud, and allow users to enable native deployment with K8s. The new advanced data management enables data migration and replication from diff storage systems.
Tags: cloud, office hour, release, storage, structured data management
Alluxio 2.0 expands the system in three major directions including improving the operability of the system, having more advanced data management, as well as re-architecting the system to be able to scale to 1 billion + file. The system is now cloud native on AWS, Google Cloud, and allow users to enable native deployment with K8s. The new advanced data management enables data migration and replication from diff storage systems.
Users deploy Alluxio in a wide range of use cases from analytics to AI platforms, for Alluxio’s unified access to data and transparent caching for acceleration. However, many frameworks are SQL engines, like Presto, Apache Spark SQL, or Apache Hive, and consume data structured as tables of rows and columns.
Tags: presto, release, structured data management
In this office hour, we will go over an introduction and motivation of Alluxio Structured Data Management, an overview of the different services in Alluxio 2.1, and a demo using Alluxio Structured Data Management with Presto.
This talk describes a stack of open-source projects to serve high-concurrent and low-latency SQL queries using Presto with Alluxio on big data in the cloud. Deploying Alluxio as a data orchestration layer to access cloud storage object storage (e.g., AWS S3), this architecture greatly enhances the data locality of Presto with distributed and cross-query caching, thus avoids reading the same data repeatedly from the cloud storage.
Tags: alluxio engineering, cloud storage, data orchestration, presto, sql, structured data management