Data-driven retailers are using visual analytics to address the 3 major shifts surrounding omni-channel retail. Three major shifts have shaken the very foundations of retail: Consumers are empowered and more demanding; supply chains must accommodate omni-channel shopping and services; and the role of stores goes far beyond converting show-roomers.
When it comes to APIs, performance is just as important as accuracy. No matter how good the features and functionality of your application might be, if it performs poorly by crashing or loading slowly, your users will lose interest quickly. With this in mind, we've put together a comprehensive guide to the fundamentals of API load testing.
Enterprises have realized that leveraging their data to make data-driven decisions is critical to innovation and staying ahead of their competition. But often organizations are not able to efficiently integrate and process enterprise data for fast analytics, due to reliance on legacy ETL solutions and data silos.
Every action a healthcare provider, payer, administrator, or patient takes creates new data. The challenge in healthcare is no longer finding data - but making it usable and actionable. This requires knowing what to do with the data you have, and understanding the relationships that exist within it.
Where do performance testing and monitoring fit into the Agile and DevOps development process? Implementing a process for improving the performance of your applications requires the right tools to help you do it. These tools go beyond the responsibilities of your development team to ensure that applications are tested in pre-deployment and monitored after your application is live in production. This eBook will provide the tactical advice you need to implement a strategy that works for your organization.
This handbook takes a deep dive into the IT architectures developed by 3 different enterprises for big data, real-time analytics, and ETL projects on Amazon Web Services (AWS) using Talend.
Read this ebook to know about common tactical and strategic mistakes to avoid when implementing Hadoop, which are identified by executives and IT teams.