Here in New York, at the AWS Summit, we are super excited to announce that Alluxio 2.0 is here, our most major release since the Alluxio launch. A couple months ago, we released 2.0 Preview – which included some of the capabilities, but 2.0 now includes even more, to continue building on to our data orchestration approach for the cloud.
In this meetup, Dipti and HY will present a new approach to hybrid analytical workloads using Alluxio, an open source data orchestration layer, which sits between compute and storage layer. Applications like Apache Spark or TensorFlow can then seamlessly access multiple disparate data sources with consistent performance using data locality and abstraction that the data orchestration tier brings.
Alluxio is a proud sponsor and exhibitor at the Presto Summit in San Francisco.
What’s Presto Summit? It’s the leading Presto conference co-organized by our partner Starburst Data and the Presto Software Foundation.
Haoyuan Li’s keynote at O’Reilly Beijing discusses open source data orchestration and the value of leveraging Alluxio with rising trends driving the need for a new architecture. Four big trends driving this need: Separation of compute & storage, hybrid-multi cloud environments, rise of object store and self-service data across the enterprise.
This webinar highlights a simple solution is to run Spark on Alluxio as a distributed cache for S3. Alluxio stores data in memory close to Spark, providing high performance, in addition to providing data accessibility and abstraction for deployments in both public and hybrid clouds.
Haoyuan Li presents at Beijing Meetup on open source data orchestration and the value of leveraging Alluxio with rising trends driving the need for a new architecture. Four big trends driving this need: Separation of compute & storage, hybrid-multi cloud environments, rise of object store and self-service data across the enterprise.
Cloud has changed the dynamics of data engineering as well as the behavior of data engineers in many ways. This is primarily because a data engineer on premise only dealt with databases and some parts of the hadoop stack.
In the cloud, things are a bit different. Data engineers suddenly need to think different and broader. Instead of being purely focused on data infrastructure, you are now almost a full stack engineer (leaving out the final end application perhaps). Compute, containers, storage, data movement, performance, network — skills are increasing needed across the broader stack. Here are some design concept and data stack elements to keep in mind.
Twitter SF is hosting 2019’s half yearly RocksDB Meetup with speakers from Twitter, Facebook and the community on July 11th.
Join us June 24 in Menlo Park for our next meetup! We’ll have 3 valuable talks, a delicious BBQ dinner and amazing summertime-themed raffle prizes! This free event is sponsored by GridGain Systems and Oracle.