Systems | Development | Analytics | API | Testing

The definition of good code varies for each person, but most define it as code that is fully tested. There are those who define that the performance and speed of this product are incredible, moreover, it runs perfectly on old hardware. Though precisely, a clean code focuses on adaptability and extensibility for an organization and lasts beyond its original timeframe. Coding is the language in which all computers intercommunicate and comprehend.

Is your data warehouse modern enough? Learn the differences, benefits and available tools and strategies for easy migration.

Stack Overflow’s annual Developer Survey confirmed our experience; Node.js continues to grow its use across the globe due to its scalability and performance as well as its ability to integrate seamlessly with a wide range of technologies and databases make it an ideal technology for businesses of all sizes.

Every year analysts, vendors, thought leaders, and everyone in between like to surmise the upcoming trends for the year. I am going to do something a little different this year. I am discussing some trends, just like everyone else, but basing them on what we are seeing with customers and how they are succeeding with the Integrate.io platform. Not just succeeding, but levering complex and diverse data sets to enable better business decisions and support growth.

Data warehousing is the process of collating data from multiple sources in an organization and store it in one place for further analysis, reporting and business decision making. Typically, organizations will have a transactional database that contains information on all day to day activities. Organizations will also have other data sources – third party or internal operations related. Data from all these sources are collated and stored in a data warehouse through an ELT or ETL process.

Five things to know about this topic: Just about every process used within a business generates some form of data. While some may see this information as useless, data analysis tools can turn it into a resource that helps your brand make better decisions in every aspect of its operations. Not all analytical tools are equal. However, the ones on this list can help you generate incredible insights that result in better decision-making.

Data teams and their business-side colleagues now expect—and need—more from their observability solutions than ever before. Modern data stacks create new challenges for performance, reliability, data quality, and, increasingly, cost. And the challenges faced by operations engineers are going to be different from those for data analysts, which are different from those people on the business side care about. That’s where DataOps observability comes in.

We have spoken in a previous blog post about documentation about how we believe that if you are building performance tests that support either Continuous Integration or Continuous Delivery then having to produce performance testing documentation before and after each test does not fit with this methodology.

Remember way back around 2016, when “IoT” was just entering the lexicon? The technology behind the “Internet of things” was starting to be used across industries. In the energy space, for example, companies used it to capture data being sent from tens of thousands of sensors from various equipment, like inverters, controllers, anemometers (wind speed detectors), cloud-watching cameras, and more.

A recent study on call center statistics found that 91% of consumers reported poor customer service in 2021. Providing high-quality service is essential, especially today, to retain customers and drive more business. Quality service is only one important metric in running a profitable call center. No matter your goal, the first step is understanding what's going on in your call center.