What problem does HPCC Systems solve for organizations?
HPCC Systems solves the problem of high-speed data engineering, analytics, and machine learning for building and managing data lakes. It provides a platform designed for robust performance, secure processing, and seamless scalability. The platform is specifically built to lower cloud costs, improve performance, and provide a better user experience.
Who is the target audience for HPCC Systems?
HPCC Systems is primarily for developers and data engineers building data pipelines. It is categorized as a Developer & AI Platform, indicating its focus on technical professionals working with data engineering, analytics, and machine learning projects.
What key features, surfaces, or integrations does HPCC Systems offer?
HPCC Systems offers features including ultra-performance for high-speed data engineering, high-productivity programming with ECL, analytics and machine learning capabilities, third-party integrations, multi-language support, Kubernetes architecture, and open-source innovation. It is designed to be secure, reliable, and provide a low total cost of ownership.
How is HPCC Systems priced or packaged?
HPCC Systems is an open-source platform. The evidence mentions it has been an entrusted open-source solution for more than 10 years and is committed to open-source innovation, but no specific pricing details or commercial packages are provided.
What is HPCC Systems used for, and in what situations?
HPCC Systems is used as an open-source big data platform for high-speed data engineering, analytics, and machine learning. It is suitable for situations requiring building and managing data lakes with robust performance and secure processing. It is an entrusted solution with thousands of deployments over more than 10 years in open source.
What is HPCC Systems?
HPCC Systems is an open-source big data platform designed for high-speed data engineering, analytics, and machine learning. It provides robust performance, secure processing, and seamless scalability for building and managing data lakes.