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.

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.

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.

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.