Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.

Today, I’m excited to announce a partnership between Logz.io and Microsoft Azure. With this partnership, Logz.io is now offering Azure customers a fully managed, scalable machine data analytics platform built on ELK and Grafana. What does that mean? Azure customers can now easily deploy, run, and scale ELK without the hassle and pain of maintaining and managing the stack themselves.

What is the most important feature your Node.js application can have? Do you think it’s having fancy fuzzy logic for your full-text search, or maybe using sockets for real-time chats? You tell me. What’s the fanciest, most amazing and sexy feature you can add to your Node.js application?

Over the last year or so, I experimented with different ways of getting customer feedback for Checkly. This post is about what worked for us and how I was totally wrong about chat widgets.

The growing threat of attacks and data breaches on IT systems has made security monitoring more crucial now than ever before. Organizations of all sizes face risks to their data, and without the proper tools in place, a single attack could pose a severe threat to your operations.

The Internet of Things has transformed people’s lives, enabling everything from “smart homes” to remote health monitoring. And now a startup called AMMP Technologies is using IoT – and Grafana – to help bring electricity to rural Africa. Hendrik Broering, COO and cofounder of AMMP, told the audience at GrafanaCon L.A. about a village in Tanzania called Changombe, with a population of about 2,000 and no access to electricity until 2017.

Two weeks ago we released the public API for Checkly. This post is about what parts we needed to refactor, what parts we added and how we handled generating API documentation.

As industries shift to a microservices approach of deploying applications using containers, data scientists can reap the benefits. Data Scientists use specific frameworks and operating systems that can often conflict with the requirements of a production system. This has led to many clashes between IT and R&D departments. IT is not going to change the OS to meet the needs of a model that needs a specific framework that won’t run on RHEL 7.2.