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Alluxio Blog

2.0 is here! Embrace silos, orchestrate data, accelerate innovation!

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.

Recap: Presto Summit SF 2019

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.

Hybrid Environments for Data Analytics is a Possibility

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.

Effective Data Engineering in the Cloud World

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.

Embracing Data Silos — the journey through a fragmented data world

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.

Alluxio on EMR: Fast Storage Access and Sharing for Spark Jobs

Traditionally, if you want to run a single Spark job on EMR, you might follow the steps: launching a cluster, running the job which reads data from storage layer like S3, performing transformations within RDD/Dataframe/Dataset, finally, sending the result back to S3. You end up having something like this.
If we add more Spark jobs across multiple clusters, you could have something like this.

Building a cloud-native analytics MPP database with Alluxio

This article walks through the journey of a startup HashData in Beijing to build a cloud-native high-performance MPP shared-everything architecture leveraging object storage as the data persistence layer and Alluxio as a data orchestration layer in the cloud.
we will illustrate how HDW leverages Alluxio as the data orchestration layer to eliminate the performance penalty introduced by object storage while benefiting from its scalability and cost-effectiveness.