Today, real-time computation platform is becoming increasingly important in many organizations. In this article, we will describe how ctrip.com applies Alluxio to accelerate the Spark SQL real-time jobs and maintain the jobs’ consistency during the downtime of our internal data lake (HDFS). In addition, we leverage Alluxio as a caching layer to dramatically reduce the workload pressure on our HDFS NameNode.
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.
Today, I’m thrilled to announce the GA of Alluxio 2.0.0, Alluxio’s biggest release to date (see our Release Notes & Release Blog) with over 900 commits.
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.
This article aims to provide a different approach to help connect and make distributed files systems like HDFS or cloud storage systems look like a local file system to data processing frameworks: the Alluxio POSIX API. To explain the approach better, we used the TensorFlow + Alluxio + AWS S3 stack as an example.
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.
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.
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.
Over the years of working in the big data and machine learning space, we frequently hear from data engineers that the biggest obstacle to extracting value from data is being able to access the data efficiently. Data silos, isolated islands of data, are often viewed by data engineers as the key culprit or public enemy №1. There have been many attempts to do away with data silos, but those attempts themselves have resulted in yet another data silo, with data lakes being one such example. Rather than attempting to eliminate data silos, we believe the right approach is to embrace them.