Today’s current pace of innovation is hindered by the necessity of reinventing the wheel in order for applications to efficiently access data. When an engineer or scientist wants to write an application to solve a problem, he or she needs to spend significant effort on getting the application to access the data efficiently and effectively, rather than focusing on the algorithms and the application’s logic.
Many organizations have taken advantage of the scalability and cost-savings of cloud computing as well as cloud storage services to meet their data-powered workload demands. In addition, as data is increasingly siloed and lives everywhere, there’s a need for data orchestration to bring the needed data closer to compute. With Alluxio’s data orchestration platform, bring back data locality for your compute with in-memory & tiered data access.
This whitepaper details how to leverage any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) to scale analytics workloads directly on on-prem data without copying and synchronizing the data into the cloud. We will show an example of what it might look like to run on-demand Starburst Presto, Spark, and Hive with Alluxio in the public cloud using on-prem HDFS.
The paper also includes a real world case study on a leading hedge fund based in New York City, who deployed large clusters of Google Compute Engine VMs with Spark and Alluxio using on-prem HDFS as the underlying storage tier.
Cloud has changed the dynamics of data engineering in many ways, from changing expectations of on-demand platform services to the popularity of the object store to the emergence of a flexible, separated data stack. And as a data engineer venturing into this cloudy world, the understanding of specific architectural approaches coupled with knowledge in some data stacks has proven useful.
Instead of being purely focused on data infrastructure, today’s data engineer is now a full stack engineer. Compute, containers, storage, data movement, performance, network – skills are increasingly needed across the broader stack.
This white paper attempts to discuss some design principles as well as high priority elements of the stack that a data engineer should think about.
Tags: data engineering
The AWS EMR service has made it easy for enterprises to bring up a full-featured analytical stack in the cloud that elastically scales based on demand.
The EMR service along with S3 provides a robust yet flexible platform in the cloud with the click of a few buttons, compared to the highly complex and rigid deployment approach required for on-premise Hadoop Data platforms. However, because data on AWS is typically stored in S3, an object store, you lose some of the key benefits of compute frameworks like Apache Spark and Presto that were designed for distributed file systems like HDFS.
In this white paper, we’ll share some of the challenges that arise because of the impedance mismatch between HDFS and S3, the expectations of analytics workloads of the object store, and how Alluxio with EMR addresses them.
This datasheet introduces the Presto + Alluxio Solution. Alluxio enables caching for Presto as well as hybrid deployments.
Proven at global web scale in production for modern data services, Alluxio is the developer of open source data orchestration software for the cloud. Alluxio moves data closer to big data and machine learning compute frameworks in any cloud across clusters, regions, clouds and countries, providing memory-speed data access to files and objects.
The data engineering team at Bazaarvoice, a software-as-a-service digital marketing company based in Austin, Texas, must handle data at massive Internet-scale to serve its customers. Facing challenges with scaling their storage capacity up and provisioning hardware, they turned to Alluxio’s tiered storage system and saw 10x acceleration of their Spark and Hive jobs running on AWS S3.
In this whitepaper you’ll learn:
- How to build a big data analytics platform on AWS that includes technologies like Hive, Spark, Kafka, Storm, Cassandra, and more
- How to setup a Hive metastore using a storage tier for hot tables
- How to leverage tiered storage for maximized read performance
Organizations like Baidu and Barclays have deployed Alluxio with Spark in their architecture, and have achieved impressive benefits and gains. Recently, Qunar deployed Alluxio with Spark in production and found that Alluxio enables Spark streaming jobs to run 15x to 300x faster. In their blog post, they described how Alluxio improved their system architecture, and mentioned that some existing Spark jobs would slow down or would never finish because they would run out of memory.