Big Data

Today's organizations store mountains of data, which means routinely analyzing massive files and million-file data sets — and doing it fast and within budget. Apache Spark is seen by data scientists as a preferred platform to manage and process vast amounts of data to quickly generate insight from data found in distributed file systems. Its ability to work in-memory with extremely large datasets is in part why Spark is included in big data architectures. Altair enables organizations to work efficiently with big data in high-performance computing (HPC) and Apache Spark environments so your data can enable high performance, not be a barrier to achieving it.

Big Data and HPC

HyperWorks Unlimited – Virtual (HWUL-VA) is a turnkey HPC solution that brings SaaS, PaaS, and IaaS to users within a single portal. It integrates unlimited use of the Altair HyperWorks™ computer-aided engineering (CAE) suite with PBS Professional®, Altair’s HPC workload manager, as well as application-aware portals for HPC access and remote visualization of big data. Customers can also orchestrate HPC workloads between containers, big data workloads, and the cloud.

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Big Data and Data Analytics

As a productivity tool, Altair® Knowledge Studio® for Apache Spark allows users to interact with Spark using an interactive and intuitive interface to generate error-free code for use in production scripts. The ability to easily manipulate data in distributed storage architectures, including large datasets that have billions of rows and thousands of columns, is unmatched by any other solution. After data transformation tasks are complete, the same workflow is used to build and deploy many different types of predictive models.

Rapid visualization of data and easily explaining insight found in extremely large amounts of data allow enterprise data analytic teams to make informed decisions from data sources such as Hadoop HDFS, Amazon S3, and other storage supported by Spark.

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