Documentation forms an integral part of operations in almost every industry. Take logistics and transportation, for example, where companies process hundreds of thousands of documents daily to keep the goods in motion and the supply chain functional. So, what are logistics companies doing to handle such a vast number of documents? More importantly, how can they use the intelligent document processing (IDP) technology to manage their documents and extract the data they need?

Businesses across various sectors want to leverage AI to increase efficiency, reduce cost, enhance customer experience, or do all that in one go. The mortgage industry is feeling it, too, thanks to the several potential areas where AI technologies can impact. For instance, AI can help mortgage lenders by: In fact, according to a Fannie Mae survey, mortgage lenders believe compliance, underwriting, and property valuation are all ripe for AI integration.

Government organizations are under pressure to deliver more efficient, transparent, and responsive services. Digital transformation is accelerating the pace of change. Public sector organizations that rely on manual processes can no longer keep up with the demands of modern governance. This is where workflow automation comes in. Automation is a powerful way to streamline operations, enhance service delivery, and improve decision-making.

For many organizations, Workday is a core system housing vital data on HR, payroll, finance, and more. However, extracting and utilizing that data for analysis can be a challenge. The new Simba Workday ODBC and JDBC Drivers simplify this process, enabling seamless access to Workday data through standard database interfaces. With Simba Drivers, you can effortlessly integrate Workday data into your analytics tools, ETL workflows, or custom applications, unlocking its full potential for decision-making.

SmartBear is thrilled to announce our acquisition of QMetry, a leader in AI-enhanced test management solutions, into the family. This merger is not merely about expanding our already robust solutions—it’s about revolutionizing the delivery of software quality across industries. In today’s fast-paced tech landscape, addressing software quality issues is critical; they can significantly impact customer satisfaction and financial growth, while flattening the quality cost curve.

Many enterprises have heterogeneous data platforms and technology stacks across different business units or data domains. For decades, they have been struggling with scale, speed, and correctness required to derive timely, meaningful, and actionable insights from vast and diverse big data environments. Despite various architectural patterns and paradigms, they still end up with perpetual “data puddles” and silos in many non-interoperable data formats.

It would be amazing if everything in the digital world happened instantly. Unfortunately, we don’t live in an ideal world, and most of the time we need to wait for things to happen. From loading a dynamic web page to processing data, even the best implementations can have delays. Some delays happen because of the time it takes to process and transfer data. Other delays are intentional and enable applications to present visuals or data more elegantly.

The sed command in Linux is a stream editor and a versatile text-processing tool. It allows users to efficiently transform text by parsing and modifying data from files or input streams. Whether you need to replace words, remove lines, or handle patterns, sed simplifies repetitive tasks and boosts productivity. In this blog, we will explore the basics of sed command, understand its syntax, and share practical examples to show how it makes text processing easier.

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Local preview environments are transforming how developers test and validate code changes before merging them into the main codebase. Acting as temporary cloud environments, they provide a production-like setting where new features and bug fixes can be tested in isolation, catching issues early and streamlining the development code review process. These environments are crucial for enhancing development velocity, especially in CI/CD workflows used by DevOps engineers and QA teams.