We are excited to present Alluxio 2.0 to our community. The goal of Alluxio 2.0 was to significantly enhance data accessibility with improved APIs, expand use cases supported to include active workloads as well as better metadata management and availability to support hyperscale deployments. Alluxio 2.0 Preview Release is the first major milestone on this path to Alluxio 2.0 and includes many new features.
Tag: <span>apache hive</span>
Over the past two decades, the Big Data stack has reshaped and evolved quickly with numerous innovations driven by the rise of many different open source projects and communities. In this meetup, speakers from Uber, Alibaba, and Alluxio will share best practices for addressing the challenges and opportunities in the developing data architectures using new and emerging open source building blocks. Topics include data format (ORC) optimization, storage security (HDFS), data format (Parquet) layers, and unified data access (Alluxio) layers.
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
In this article, Thai Bui from Bazaarvoice describes how Bazaarvoice leverages Alluxio to build a tiered storage architecture with AWS S3 to maximize performance and minimize operating costs on running Big Data analytics on AWS EC2.