Business intelligence (BI) platforms analyze and present all your data in a visually friendly way on reports, graphs, charts, and dashboards, making it easier to view insights about your operations. But how do you get data into these platforms in the first place? The easiest way is by using BI ETL tools that extract data from its source, transform that data into the correct format, and load it into a central repository like a data warehouse — a process known as Extract, Transform, and Load (ETL).

Cloud logging services have long been plagued by limitations and high costs, hindering companies’ ability to achieve true flexibility in their operations. One of the primary obstacles is the lack of flexibility in traditional cloud logging services, which often require companies to make upfront decisions about log levels and storage capacity, locking them into fixed plans for extended periods.

Data practitioners should bring best practices from BI, especially automated data integration, to data science.

Smartphones have advanced greatly since their introduction in the 2000s. From faster GPUs for gaming to advanced instrumentation, these devices are now more computer than phone. Given the limitations of simulators and the complexity of real devices, they've also become a challenge for app developers to test. Biometric authentication, a fast-growing smartphone technology, introduced testing challenges.

Can you test 100% of your application? Or can you cover every possible testing scenario? And how much time and resources should you allocate to make the software bug-free? There are multiple questions, and all their answers lie in Test coverage techniques. In this blog, we will discuss the basics of test coverage.

Logs are essential to any application's development. Most Ruby logs are verbose and chunky, so digging for exactly what you need can be difficult. Even though they contain useful information, you might not get as much value as you should from logs if you don't know how to use them effectively. In this article, we'll explore: Let's get started.

Node.js 12.x onwards introduced async stack traces. Async stack traces allow developers to view the call stack of asynchronous code, making it easier to trace and debug code issues. In this post, we’ll see how async stack traces work, how you can use them to debug code, and how to track them using AppSignal. Ready? Let's get going!

The shift from single public cloud architectures to hybrid multi-cloud continues at enterprises around the globe, as organizations look for greater control, lower costs, and improved performance. I had the opportunity recently to speak with Vaughn Eisler, director of business development at Equinix, about this massive shift and some of the complex reasons behind them. Eisler sees several reasons for the movement.

The Head of Business Intelligence (BI) oversees the use of software for data analytics and decision-making. Finding the right person for this role is critical because it's likely a key part of your business's broader BI implementation and team-building strategy.

Absinthe is a toolkit for building a GraphQL API with Elixir. It has a declarative syntax that fits really well with Elixir’s idiomatic style. In today’s post — the first of a series on Absinthe — we will explore how you can use Absinthe to create a GraphQL API. But before we jump into Absinthe, let’s take a brief look at GraphQL.