Features & Tools


HPCC Systems Git support

Gavin Halliday is a longstanding member of the HPCC Systems core development team. In this blog Gavin outlines the new improvements added to GitHub and discusses the best way to implement them. Learn how to use these improvements to have the most optimized experience possible.

Advanced Python Embedding in ECL — A definitive guide

ECL Provides a powerful capability to combine the benefits of declarative programming (ECL) with those of procedural languages such as C++, Java, or Python. This is known as Embedding. This guide provides a comprehensive review of various methods and patterns for Python Embedding within ECL programs. It reviews elementary embedding techniques, and provides a guide to several more advanced embedding patterns.

Adopting HPCC Systems and ECL – A User’s Perspective

We know our big data analytics solution makes it easy for users to get up and running fast. While we demonstrate this through our training courses, documentation, tutorials and blog, what can be better than hearing it from a user who wants to share their experience and tips to the rest of our open source community. In this blog, Akhilesh Badhri talks about his HPCC Systems and ECL adoption journey.

Optimizing the Cloud Operation Cost of HPCC Systems using Azure Spot Instance

Processing data on the cloud using Virtual Machines can be expensive. Luckily, Roshan Bhandari has been researching how to save a considerable amount of money by running your VM using Azure Spot instances. Knowing the right times of day and types of data to best utilize this resource can be instrumental in cost savings. Read on to find out more.

Random Fourier Features accelerated Gaussian Process Regressor

Gaussian process regression is a powerful machine learning method to solve non-linear regression problems. However, because of the intensive computation, Gaussian process regression is not suitable for large-scale machine learning problems. Fortunately, researchers developed approximation methods to get a solution arbitrarily close as the original Gaussian process more rapidly and with better scaling. In this post, a Random Fourier Features accelerated Gaussian process regressor will be introduced.

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