A powerful, open-source, enterprise-proven big data analytics platform.

HPCC Systems® helps businesses of all sizes find the answers they need by making data easier to process, analyze, and understand.

Born from the deep data analytics history of LexisNexis® Risk Solutions, HPCC Systems provides high-performance, parallel processing and delivery for applications using big data.

The open-source platform incorporates a software architecture implemented on commodity shared-nothing computing clusters for resilience and scalability. It is configurable to support both parallel batch data processing and high-performance data delivery applications using indexed data files. The platform includes a high-level, implicitly parallel data-centric declarative programming language that adds to its flexibility and efficiency.

Developers, data scientists and technology leaders adopt HPCC Systems because it is cost-effective, comprehensive, fast, powerful and scalable.

Ultimately, it makes managing big data easier.

The HPCC Systems Platform

The HPCC Systems platform is a set of easy-to-use software features, enabling developers and data scientists to process and analyze data at any scale. With a strong commitment to the open source community, the HPCC Systems platform is available free of licensing and service costs. Download the HPCC Systems Brochure.

HPCC Systems provides all the functionality to execute a data project. Specifically, the HPCC Systems stack comprises of:

Processing clusters using commodity hardware and high-speed networking
Linux operating system
Supports SOAP, XML, HTTP, REST and JSON
Enterprise Services Platform (ESP) enables end-user access to ROXIE queries via common web services protocols

Thor and ROXIE are both fault-resilient, based on replication within the cluster.
The systems store file part replicas on multiple nodes to protect against disk or node failures.
Both are designed for resiliency and continued availability in event of hardware failures.

Administrative tools for environment configuration, job monitoring, system performance management, distributed file system management, and more.
Extension modules for web log analytics, natural language parsing, machine learning, data encryption, and more.

Declarative, modular, extensible Enterprise Control Language (ECL) is designed specifically for processing big data.

Highly efficient — accomplish big data tasks with far less code.
Flexible — can be used both for complex data processing on a Thor cluster and for query and report processing on a ROXIE cluster.
Graphical IDE for ECL simplifies development, testing and debugging.
ECL compiler is cluster-aware and automatically optimizes code for parallel processing.
ECL code compiles into optimized C++ and can be easily extended using C++ libraries.

The two main systems — Thor and ROXIE — work together to provide an end-to-end solution for big data processing and analytics. Data and indexes to support queries are pre-built on Thor and then deployed to ROXIE.

Thor, the Data Refinery, is the extraction, transformation and loading engine.

Thor uses a master-slave topology in which slaves provide localized data storage and processing power, while the master monitors and coordinates the activities of the slave nodes and communicates job status information.
Middleware components provide name services and other services in support of the distributed job execution environment.

ROXIE, the Data Delivery Engine, provides high-performance online processing and data warehouse capabilities.

Each ROXIE node runs a Server process and an Agent process. The Server process handles incoming query requests from users, allocates the processing of the queries to the appropriate Agents across the Roxy cluster, collates the results, and returns the payload to the client.
Queries may include joins and other complex transformations, and payloads can contain structured or unstructured data.

Thor DFS is record-oriented and optimized for big data ETL (extract-transform-load). A big data input file containing fixed or variable length records in standard or custom formats is partitioned across the cluster’s DFS, with each node getting approximately the same amount of record data and with no splitting of individual records.
ROXIE DFS is index-based and optimized for concurrent query processing. Based on a custom B+ tree structure, the system enables fast, efficient data retrieval.

Horizontal scalability from one node to thousands of nodes.
Thor Data Refinery can process up to billions of records per second.
ROXIE Data Delivery Engine can support thousands of users with sub-second response time, depending on the application.

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Overview videos

Free videos are available to help you get started quickly and get the most from HPCC Systems. The videos cover a variety of topics, from beginners trying to solve their first problem to advanced users looking to tune their programs to meet performance requirements.

View getting started videos