Systems | Development | Analytics | API | Testing

When we think of the various people and teams making use of ML and DBMS, we can place them on a spectrum based on the composition of their work.

It’s hard to believe enterprise BI platforms have been around for three decades. In that time, they have served the purpose of collecting and analyzing large amounts of data to help businesses make more informed decisions. But in today’s data-driven economy, analysts struggle to keep up with the myriad of business intelligence reports from traditional BI tools – which fail to effectively and efficiently analyze and interpret data in real-time.

As big data continues to grow exponentially, enterprises are discovering that legacy data environments (e.g. data warehouse or data mart) were never designed to efficiently process and extract insights from the vast volumes of data they generate today. In turn, enterprises are shifting investments away from legacy data environments and searching for future-proof alternatives (e.g., data lakes, data lakehouse, data fabric, or data mesh) to support data-driven, new-generation initiatives.

Web applications are necessary to enhance the visibility of a business organization and help them achieve better ROI. Therefore, firms need to choose the right web development language to get the best results. Node.js has emerged as a leading programming language for developing web applications. But in hindsight, there are some complications that developers face with Node.js app maintenance.

Machine learning (ML), more than any other workflow, has imposed the most stress on modern data architectures. Its success is often contingent on the collaboration of polyglot data teams stitching together SQL- and Python-based pipelines to execute the many steps that take place from data ingestion to ML model inference.

At times, Node.js can feel like a black box. Shifting to an asynchronous programming model changes how developers are required to handle and interpret existing data. There are many solutions out there to help the users gain more visibility; however, it has been proved that all those solutions out there to capture such critical information come with a high toll on the performance of every application implementing them.

Hello to the Testhouse family! This is my second blog in a series of posts aimed at sharing insights into things I have learned or experienced in my career in testing to date. If you find anything that sparks your interest or have follow up questions, then please do let me know. In my last blog, I touched on mentorship, conferences, and the value of networking. This blog continues the mentoring theme and focuses on the benefits of having a mentor, and how you can go about finding one.

Embedded analytics has become a priority for many companies today. It means building data visualizations and business intelligence (BI) features right into public-facing apps. However, some businesses don't like this idea. They don't want to feature a third-party solution in their first-party applications - leading to a lack of data and analytics used.

Test-driven development, or TDD, is a testing method where you first convert the feature requirements to tests and then write code to make the tests pass. In this article, we take an objective look at test-driven development, its benefits, downsides, and best use cases.