Distributed Data Analytics Made Easy with Omnia
Thu, 20 Jul 2023 17:03:25 -0000
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The Challenge
Cleaning up a few fields in a data file or replacing a few free-form fields with a standardized format. Running a few basic statistics on the responses of your user survey or creating a regression model based on the data from a few sensors. These types of operations are commonplace in the data analytics space. Grab your laptop, request a VM from your IT team or a cloud instance from your favorite service provider, and you’re ready to go!
Maybe at first. But data is growing exponentially, and eventually, one computer simply doesn’t provide enough raw performance to get the job done. That’s when it’s time for distributed data analytics, where you process different chunks of data on different computers and bring all the results together at the end. And one of the most common tools for doing distributed data analytics is Apache Spark.
Apache Spark
Apache Spark (https://spark.apache.org/) was first created in 2009 and is primarily used for analyzing batch and streaming data. It is a good option when there are large data processing tasks that need to be completed in the most efficient way possible. Spark is compatible with common data science/engineering languages such as Python, R, Scala, SQL, and Java. The Spark analytical engine can be installed on Kubernetes or Mesos. Spark can also be used for graph processing through tools such as GraphX and machine learning through MLlib.
Omnia makes Spark on Kubernetes easy
Omnia is an open-source, community-driven project with the goal of deploying clusters optimized for workloads that users need to run. While Omnia was created within the Dell Technologies HPC Community, it can be used by anyone, anywhere. Omnia allows its customers to deploy one platform for all their needs, or to easily deploy many platforms if needed. Omnia’s collection of automatically deployed capabilities is always expanding to fit the needs of the community. Instead of having to use multiple platforms and numerous servers, Omnia solves that problem and allows deployment to be done at the push of a button. And that ability now extends to deploying Spark on Omnia-deployed Kubernetes clusters.
Now, any IT team deploying clusters with Omnia will get Spark enabled automatically. No extra configuration, no additional work. Omnia deploys the spark operator as a standalone deployment, which can be leveraged from within an Omnia-deployed JupyterHub instance or an Omnia-deployed KubeFlow instance. It’s that easy!
Learn More
Learn more about Omnia
Learn more about Spark