Systems | Development | Analytics | API | Testing

Maximising iPaaS ROI: The business case

In today’s rapidly evolving digital environment, organisations face mounting pressure to make strategic technology investments that deliver immediate operational benefits and long-term competitive advantages. As businesses navigate this landscape, iPaaS has emerged as a transformative solution that addresses complex integration challenges while delivering substantial returns on investment.

Empowering Growth Through Training and Enablement

Throughout my career, I’ve had the privilege of working across the full spectrum of enablement: internal enablement, partner enablement and customer enablement. Each of these domains brings unique challenges, audiences and approaches, but a common thread unites them all: the goal of fostering growth. At its core, training and enablement are not just about imparting knowledge or improving skills. While these are vital components, the true purpose transcends the transactional.

How to automate SAP data and quickly see savings

Loading data quickly and efficiently to and from SAP is a challenge for most businesses. Whether you are an IT manager, a business user, or an SAP expert, getting data into SAP can often be a time-consuming task standing in the way of more strategic projects. Hours are devoted to entering, correcting, and managing data uploads. There are a few ways to speed up the process and streamline data management, but there’s one that will empower your internal teams while saving time and money right away.

The How and Why of Data Cleansing

Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to ensure its quality, accuracy, and reliability. This process is crucial for businesses that rely on data-driven decision-making, as poor data quality can lead to costly mistakes and inefficiencies. By cleansing data (removing duplicates, correcting inaccuracies, and filling in missing information), organizations can improve operational efficiency and make more informed decisions.

Tenstorrent Cloud Instances: Unveiling Next-Gen AI Accelerators

Today, we’re thrilled to announce the world premiere availability of Tenstorrent Instances via the Koyeb Serverless Platform. You can now access the Wormhole multi-chip solution in minutes to bring up and test frontiers of model inference performance. You've probably heard us say this: we're committed to bringing alternative accelerators to market to foster innovation in the AI infrastructure space.

Solving B2B Onboarding Challenges: Elevating User Experience with B2B Identity

In the digital age, we interact with countless applications, whether for personal, professional, or recreational purposes. Many of these applications appear consumer-oriented but are, in fact, B2B applications. By accessing healthcare services, payroll systems, or school management platforms, customers are using their provider's applications (such as GP or employer) which is a business customer of a larger B2B application. B2B applications form an integral part of daily operations across various industries.

Navigating Electric Vehicle Software Development

Electric vehicles are quickly becoming the vehicle of choice for many consumers. Globally, one in four new cars sold are now electric, which includes full battery-electric vehicles and plug-in hybrids, according to Our World in Data. This was a monumental leap since 2022, jumping from 26 million electric cars in use to over 40 million today. Gartner predicts that by the end of 2025, the number of electric vehicles in use will be up to 85 million.

How to Fix the "Unexpected End of zlib Input Stream" Error

The error message "unexpected end of zlib input stream" means that the zlib library, while trying to decompress data, reached the end of the input stream sooner than expected. Basically, zlib anticipated more data (or proper termination) to decompress the stream, but it didn't find it. This could be due to a few reasons, such as the data being incomplete, corrupted, or even because of mistakes in how the data stream was handled in the code.