In data engineering and analytics, effectively wrangling data is not just a skill but a necessity. Large volumes of complex data have grown exponentially as businesses and technologies evolve. This surge has brought two critical processes in data management to the front line: Data Wrangling and Extract, Transform, Load (ETL). Understanding these processes is pivotal for any organization leveraging data for a strategic advantage.

Explore the architectural concepts that power BigQuery’s support for semi-structured JSON, which eliminates the need for complex preprocessing and provides schema flexibility, intuitive querying and the scalability benefits, at large scale.

The 2023 banking industry crisis left a lot of us unsettled and more worried than ever about customer retention. We all want to feel confident our customers will continue to choose us, but how? Start by giving them a great experience. Business process management (BPM) can help you meet and exceed customer expectations while improving operational efficiency and managing regulatory compliance. If you're Googling "bpm in banking," you're not alone. It's an approach to improving organizational processes.

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.

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.

The latest release of Kong Mesh (version 2.5) brings many new features that push the envelope and make Kong Mesh the logical choice of a service mesh to meet your objectives. In this blog, we'll break down these new features and provide tailored use cases to illustrate how Kong Mesh 2.5 can elevate your service mesh experience. We'll focus on three main categories: flexibility, resiliency, and security.

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.

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.

If you’re building an application today, one of your top product priorities for 2024 is almost certainly adding an AI copilot to your application. AI copilots – AI assistants powered by large language models (LLMs) and deeply embedded into applications – offer one of the most compelling opportunities to reimagine applications since the dawn of the internet.

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.