Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
For many latency-sensitive SQL workloads, Presto is often bound by retrieving distant data. In this talk, Rohit Jain from Facebook and Bin Fan from Alluxio will introduce their teams’ collaboration on adding a local on-SSD Alluxio cache inside Presto workers at Facebook to improve queries with unsatisfied latency.
This talk will overview two projects at Electronic Arts (EA) that address the mismatch by data orchestration: One project automatically generates configurations for all components in a large monitoring system, which reduces the daily average number of alerts from ~1000 to ~20. The other project introduces Alluxio for caching and unifying address space across ETL and analytics workloads, which substantially simplifies architecture, improves performance, and reduces ops overheads.
The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. While this enables scaling elasticity, it introduces new problems – how do you co-locate data with compute, how do you unify data across multiple remote clouds, how do you keep storage and I/O service costs down and many more.
This talk will guide the audience on how Alluxio can greatly simplify the data preparation phase in with remote and possibly multiple data sources. We will share the lessons and benchmark from Bill Zhao an engineer led in Apple when building a Machine Learning platform using Tensorflow, NFS, DC/OS and Alluxio.
Presto is widely used for data science, business analytics, and operations. Presto’s SQL is a main driver for this, as it is ANSI-compliant, easy to ramp-up, and has rich functionality. Given the versatility and flexibility of this software, there is also a huge demand to develop interfaces for other critical data domains like real-time dashboards, stream processing, and large-scale batch computations. We will explore some interesting systems and prototypes to bring Presto to these new domains.
Running Spark with Alluxio is a popular stack particularly for hybrid environments. In this session, Dipti will briefly introduce Alluxio, share the top 10 tips for performance tuning for real-world workloads, and demo Alluxio with Spark.
In this presentation, Ryte’s Chapter lead engineer, Danny Linden, shows why & how we solve some challenging technical issues, improve the speed, and reduce costs of our AWS EMR Hadoop & Presto -Backend with Alluxio to an awesome level!