Building a high-performance data lake analytics engine at Alibaba Cloud with Presto+Alluxio

Data Lake Analytics(DLA) is a large scale serverless data federation service on Alibaba Cloud. One of its serverless analytics engine is based on Presto. The DLA Presto engine supports a variety of data sources and is widely used in different application scenarios in the cloud. In this session, we will talk about the system architecture of DLA Presto engine, as well as the challenges and solutions. In particular, we will introduce the use of alluxio local cache to solve performance issues on OSS data sources caused by access delay and OSS bandwidth limitation. We will discuss the principle of alluxio local cache and some improvements we have made.

Tags: , , , , ,

Accelerate Analytics and ML in the Hybrid Cloud Era

Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.

Tags: , , , , , , ,

Accelerating Queries on Cloud Data Lakes

ITPro Today Webinar *

Join us for this webinar where Alex Ma of Alluxio, an open source data orchestration platform, will discuss how a data orchestration approach offers a solution for connecting traditional on-prem data centers with the cloud, data centers with other data centers, and clouds with other clouds. With Alluxio’s “zero-copy” burst solution, companies can bridge remote data centers with computing frameworks in other locations, enabling them to offload compute and leverage the flexibility, scalability, and power of the cloud for their remote data.

Bursting Spark or Presto Jobs to AWS using Alluxio

In this office hour, we demonstrate how a “zero-copy burst” solution helps to speed up Spark and Presto queries in the public cloud while eliminating the process of manually copying and synchronizing data from the on-premise data lake to cloud storage. This approach allows compute frameworks to decouple from on-premise data sources and scale efficiently by leveraging Alluxio and public cloud resources such as AWS.

Tags: , , , , , , , , ,

Bursting Spark or Presto Jobs to AWS using Alluxio

Community Online Office Hour *

In this office hour, we demonstrate how a “zero-copy burst” solution helps to speed up Spark and Presto queries in the public cloud while eliminating the process of manually copying and synchronizing data from the on-premise data lake to cloud storage. This approach allows compute frameworks to decouple from on-premise data sources and scale efficiently by leveraging Alluxio and public cloud resources such as AWS.

Accelerate and Scale Big Data Analytics with Alluxio and Intel® Optane™ Persistent Memory

International Data Corporation (IDC) reported that the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes by 20251. This trend becomes more and more complicated with the variety and velocity of data growth, and it continuously changes the ways data is collected, stored, processed, and analyzed. New analytics solutions, including machine learning, deep learning, and artificial intelligence (AI), and new architectures and tools are being developed to extract and deliver value from the huge datasphere. 

Tags: , , , , , ,