big data

Big Data

Many organizations struggle to manage and mine data that comes from modern technology platforms. The data arriving into the organization may be in the form of a small amount of very large files, or in the form of millions of very small files arriving every day, or even every minute. Platforms such as Apache Spark™ are seen by data scientists as preferred solutions to manage and process these 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), modern processing and storage platforms, and cloud environments. Don’t let difficult data be a barrier to making informed decisions.

Big Data and HPC

Altair® Unlimited™ is a turnkey, state-of-the-art private appliance, available in both on-premises and cloud-based formats. Altair Unlimited delivers unlimited use of a wide range of Altair® HyperWorks® solver software for simulating mechanics, fluids, electromagnetics, and more — including modeling, visualization, and optimization. To keep it all working at maximum efficiency, HPC resource management and user-friendly web portal software comes included with every system in the industry-leading Altair® PBS Works™ package.

Altair Unlimited boxes up software, system administration, and infrastructure as a service into a single, intuitive platform.

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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, 是无与伦比的,其他的解决方案吗.

One workflow is used to transform big data formats and to build and deploy many different types of predictive machine learning 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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Altair Knowledge Studio for Apache Spark™

Knowledge Studio for Apache Spark™ is unique because it allows users scale up, scale-wide and scale-down. It not only leverage’s Apache Spark’s ability to operate on datasets with very large numbers of records, it is also capable of generating improved SparkSQL queries on datasets that have thousands of columns. Further, Knowledge Studio for Apache Spark provides the ability to scale down by avoiding the overhead costs of parallelization when datasets are very small.


Redefine What’s Possible with HPC

We are seeing a mass increase in data, coming at us from all directions. Whether you are running AI-enhanced HPC applications on-premises or HPC workloads in the cloud, you need platforms and solutions that will offer the performance, scalability, and flexibility to redefine what’s possible with HPC.


Overcome Big Data Access Challenges with Knowledge Studio for Apache Spark™

In combination with its market-leading data visualization approach for building, exploring and segmenting data, and building explainable predictive models, Altair Knowledge Studio for Apache Spark&tradel enables data science teams to build machine learning models from data located in distributed file systems without having to code. Data preparation and profiling allows for easy data extraction and manipulation. Knowledge Studio’s efficient use of compute resources, especially in cloud environments, shortens processing cycles and reduces costs. Large datasets with billions of rows and thousands of columns can be efficiently mined for better decision intelligence.


Sporting Goods Chain Fuels Results with Data

In order to compete in the fast-paced retail industry, a chain of more than 100 sporting goods stores operating across the United States needed a more efficient and accurate method for preparing and analyzing data to enable management teams to quickly make strategic operational decisions.

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