Systems | Development | Analytics | API | Testing

Data analytics and business intelligence (BI) are key for driving informed decisions, and today's best solutions make use of artificial intelligence (AI) to help users along the way. AI chatbots, powered by natural language and machine learning (ML), are one such intelligent tool that can quickly analyze large datasets, helping your business users find helpful answers to queries, get recommendations, and even uncover valuable insights.

Imagine you are a software developer and you are buzzing with excitement because you've just deployed a new feature at work. But suddenly, you hear whispers of a critical bug in the old functionality. That gut-wrenching moment reminds us how important it is to know when regression testing should be performed.

Legacy applications pose multiple problems for data-driven organizations like yours. Outdated software might no longer receive support and maintenance from its vendor or might not integrate with modern technologies like cloud computing. Still, these applications are essential for day-to-day business tasks, and replacing them could impede productivity and profitability.

When developing an Elixir app, you'll often need to handle tasks in a way that does not interrupt the normal user request-response cycle. Tasks like sending emails are great examples of jobs that should be delegated to a capable background job processing service. In the Elixir ecosystem, Oban is one such background job processing library. In this article, we'll learn what Oban is, how it works, and how to instrument it using AppSignal.

Speed is everything in today's fast-moving business world. For software developers, this means the race is on to get new apps to market faster and better than competitors. In light of this constant pressure to deliver, it's no surprise that the demand for hybrid development continues to rise. After all, with one codebase that runs on multiple platforms, how can you go wrong? In reality the choice is not always so clear-cut.

Have you ever wondered why claim processing often takes so long? Or why so many errors happen in what should be a simple process? In fact, research shows that manual claim processing can lead to up to 30% errors and 50% higher costs due to inefficiencies. These delays don’t just frustrate customers- they also slow down business operations and impact revenue.

Your organization has decided to make the leap to SAP S/4HANA Cloud Public Edition, a strategic choice that offers improved performance, advanced analytics, and more efficient support for your business operations. While this cloud ERP offers streamlined processes and improved scalability, it also limits the level of customization that finance teams have come to rely on for tailored, actionable insights. The result post-migration?

In the modern digital landscape, data grows at an astonishing rate. Mid-sized enterprises struggle to manage its volume and velocity, let alone its value. Today’s complex data environments must span hybrid and multicloud setups. And as transformative technologies like generative AI (GenAI) matter more to business operations, they must manage skyrocketing demand for sophisticated, flexible and scalable data solutions.

Getting different teams, business units and even companies to work together toward a common goal not only maximizes efficiency, but drives innovation. Effective collaboration on data and AI has never been more closely tied to success. At Snowflake, we’re removing the barriers that prevent productive cooperation while building the connections to make working together easier than ever.