Systems | Development | Analytics | API | Testing

I consider myself pretty lucky: I love technology and get paid to pursue my hobby. I also get to say yes to opportunities that come my way that can increase value to my company and our clients. It was one of these opportunities that led us to adopt change data capture (CDC) technology, resulting in an added value proposition for our customers.

Qlik’s partnership with AWS provides customers with an end-to-end data integration and analytics solution for AWS. Our accelerates data delivery and readiness for analytics from a wide range of enterprise data sources. And our empowers users at any skill level to freely explore all your data and uncover hidden insights. Add to that Qlik Staige and you can find out how we can help modernize with AI.

Ever since we have entered the third decade of the 21st century, artificial intelligence has proven to be the driving force behind innovation. However, the growing need for technology and constant development demands access to rapid testing and quality assurance. Besides, the software testing landscape is undergoing a revolutionary transformation, as we hurtle into a tech-driven era. It means AI-powered tools are being tested using the power of AI automation testing tools.

BigQuery cross-region dataset replication allows you to replicate any dataset across regions, for additional geo-redundancy.

Secure File Transfer Protocol (SFTP) stands as a robust protocol at the disposal of businesses, offering enhanced collaborative capabilities, operational efficiency, and heightened security for confidential data. Using SFTP, organizations can confidently exchange files among users and locations, irrespective of device variations. This adoption contributes to increased productivity levels and improved security around sensitive data, shielding it from unauthorized breaches.

An MLOps platform enables streamlining and automating the entire ML lifecycle, from model development and training to deployment and monitoring. This helps enhance collaboration between data scientists and developers, bridge technological silos, and ensure efficiency when building and deploying ML models, which brings more ML models to production faster.

Private APIs vs. internal APIs – at first glance, they might seem synonymous, given their non-public nature. However, delving deeper reveals unique characteristics and purposes for each. As with any technical concept, a clear understanding is key to harnessing its full potential. To help navigate this intricate landscape, this article aims to demystify these terms, shedding light on their similarities and, more importantly, their differences.

In today's digital age, Portable Document Format (PDF) files have become an integral part of how we share and manage documents. Regardless of whether it's contracts, reports, or presentations, PDFs provide a consistent and reliable way to present information across different platforms and devices. As developers, being able to manipulate and generate PDFs programmatically is a valuable skill. This is where the pdf-lib library for Node.js comes into play.

GraphQL is a query language and runtime for APIs, developed by Facebook in 2012 and later open-sourced in 2015. And it has changed the way we fetch data from our server. Typically, most front-end clients – like React, Angular, Vue, or mobile apps like iOS and Android – use REST APIs to fetch data from the server. REST APIs require more HTTP calls than GraphQL, which leads to over and underfetching.

Large Language Models (LLMs) have now evolved to include capabilities that simplify and/or augment a wide range of jobs. As enterprises consider wide-scale adoption of LLMs for use cases across their workforce or within applications, it’s important to note that while foundation models provide logic and the ability to understand commands, they lack the core knowledge of the business. That’s where fine-tuning becomes a critical step.