Systems | Development | Analytics | API | Testing

Calling Ruby Methods in C: Avoid Memory Leaks

Memory leaks are a pain for gem users. They are hard to track and can lead to expensive infrastructure costs. Memory leaks within a C extension are even worse. You'll see a lot of tools and articles about finding leaks in Ruby. However, you don't have the same access to internals in C. A naive usage of rb_funcall can cause memory leaks: it's much better to use rb_protect instead. So, if you are a C extension writer, please read on for the sake of developers who will use your gem. Let's get started!

Why Column-Aware Metadata Is Key to Automating Data Transformations

Data, data, data. It does seem we are not only surrounded by talk about data, but by the actual data itself. We are collecting data from every nook and cranny of the universe (literally!). IoT devices in every industry; geolocation information on our phones, watches, cars, and every other mobile device; every website or app we access—all are collecting data. In order to derive value from this avalanche of data, we have to get more agile when it comes to preparing the data for consumption.

Scania Uses Data Mesh and Snowflake's Data Cloud to Drive Transport Sustainability

Scania is at the forefront of a more autonomous, connected, electric future for the transportation industry. Find out why its Head of Data and Information Management uses data mesh—and Snowflake—to make it a reality. Scania is a global truck, bus, and industrial engine manufacturer and offers an extensive range of related services so its customers can focus on their core business.

How To Instrument Your Elixir Application with AppSignal

Instrumentation is an essential part of monitoring and operating an application, especially for apps heavily used in production. Even in today's everchanging technology landscape, visibility and observability still challenge developers and system administrators. Metrics and logging are essential for monitoring and operating an application. Metrics measure an application's performance and system health, while logging records system health and application state.

An Overview of Traffic Mirroring Options in Kubernetes

Testing in production carries a lot of risk, like possibly causing downtime for users. However, the advantages of using real user traffic are many, which has led to the popularity of traffic mirroring. Mirroring can be implemented as part of pre-deployment testing, as well as other parts of the developer experience like the development itself. But, how do you get started with it?

Top 5 analytics and data engineer skills you should know in 2023

Analytics engineer is the latest role that combines the technical skills of a data engineer with the business knowledge of a data analyst. They are typically coding in SQL, building dbt data models, and automating data pipelines. You could say they own the steps between data ingestion and orchestration. Whether you are a seasoned analytics engineer or new to the field, it’s important to continually learn new things and improve the work you’ve already done.

CI/CD automated testing: How to release fast, with confidence

If you’ve got an agile team interested in shipping fast without breaking things, this post is for you. In this piece, I’m going to explain how we at Rainforest QA approach automated testing in a continuous integration / continuous delivery (CI/CD) pipeline, with a focus on end-to-end (e2e) functional testing. The aim of our testing and other DevOps methodologies is to maintain a healthy balance between speed and product quality.

5 Business Data Migration Best Practices

Data migration is something that all businesses will have to deal with at some point. Maybe a high level of growth has meant that you have too much data and need a larger server. Or maybe you want to move to a new cloud-based system. Regardless of the specifics, all modern businesses will know the power of unlocking data.

Eckerson Report: Data Observability for Modern Digital Enterprises

This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.