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.
Learn how to set up EMR Spark with Alluxio so Spark jobs can seamlessly read from and write to S3. See the performance comparison between Spark on S3 with Spark, and Alluxio on S3.
Alluxio is an open-source data orchestration system widely used to speed up data-intensive workloads in the cloud. Alluxio v2.0 introduced Replicated Async Write to allow users to complete writes to Alluxio file system and return quickly with high application performance, while still providing users with peace of mind that data will be persisted to the chosen under storage like S3 in the background.
Joint meetup in Hangzhou discusses: An introduction to new features of big data storage system Alluxio and optimization of cache performance, Practice & exploration of Spark & Alluxio, and the Interactive query system Impala.
Joint hosted Alluxio New York meetup with talks to include: Embracing hybrid cloud for data-intensive analytic workloads and Alluxio on AWS EMR (fast storage access and sharing for Spark).
Alluxio maintainer and founding engineer Calvin Jia presents on Scalable Filesystem Metadata Services with RocksDB at the RocksDB meetup at Twitter.
The Alluxio-Presto sandbox is a docker application featuring installations of MySQL, Hadoop, Hive, Presto, and Alluxio. The sandbox lets you easily dive into an interactive environment where you can explore Alluxio, run queries with Presto, and see the performance benefits of using Alluxio in a big data software stack.
This talk shares our design, implementation and optimization of Alluxio metadata service to address the scalability challenges, focusing on how to apply and combine techniques including tiered metadata storage (based on off-heap KV store RocksDB), fine-grained file system inode tree locking scheme, embedded state-replicate machine (based on RAFT), exploration and performance tuning in the correct RPC frameworks (thrift vs gRPC) and etc.
As the data ecosystem becomes massively complex and more and more disaggregated, data analysts and end users have trouble adapting and working with hybrid environments. The proliferation of compute applications along with storage mediums leads to a hybrid model that we are just not accustomed to.
With this disaggregated system data engineers now come across a multitude of problems that they must overcome in order to get meaningful insights.