A service mesh is a software infrastructure layer for controlling communication between services and usually tasked with handling all the network logic required to make microservice applications work seamlessly in a cloud-native environment. It’s the perfect solution to your communications problems.

As you develop your software, you want it to work well, not only on paper but also as the actual users interact with the application. The way to create quality software that is easy to use and bug-free is with end-to-end testing.

For data analysts, business intelligence professionals, and CTOs to optimize and scale business operations, they must first understand the business data that is available to them. One of the best platforms to turn complicated data points from multiple platforms into a singular, coherent data set is Microsoft Power BI. However, you must first prepare the data sets to eliminate fragmentations and create structural consistency. This article explores how to prepare data for Microsoft Power BI.

Hortonworks DataFlow (HDF) 3.5.2 was released at the end of 2020. The new releases will not continue under HDF as Cloudera brings the best and latest of Apache NiFi in the new Cloudera Flow Management (CFM) product. Getting the latest improvements and new features of NiFi is one of many reasons for you to move your legacy deployments of NiFi on this new platform. To that end, we released a few blog posts to help you migrate from HDF to CFM.

Testing any software project is an important step in order to find out how the software functions. Learning when the project acts as expected (and when it does not) is the ultimate goal of the testing process. Testing stops design errors from reaching production code. However, testing should not only happen before code is deployed.

In my previous blog posts, I’ve talked about how you can aggregate data depending on the data type, as well as how you can re-express your data to get more value from it. For this post, let’s look at some of the different ways of measuring your data.

Running an eCommerce store is very much like flying a plane - you can reach unprecedented heights, but you won't be able to do it blindfolded. You have to see where you are going to touch the skies. E-commerce analytics gives you the guidance to make the right choice and scale your online store to new heights. In this article, we will take a deep dive into Shopify Analytics Shopify offers analytics as an out-of-the-box default service to all Shopify store owners and admins.

Ask any analyst how they spend the majority of their work day and they’ll tell you: Performing remedial tasks that provide no analytics value. 92% of data workers report that their time is being siphoned away performing operational tasks outside of their roles. Data teams waste an inordinate amount of time maintaining the delicate data-to-dashboards pipelines they’ve created, leaving only 50% of their time to actually analyze data.

Having a coded approach to test automation has its benefits. It certainly does give you the power to be extra flexible in terms of test case creation. But with it, it also requires a lot of investments from you – in terms of time and resources. Hence, you need to decide if that is really the power you need because, in the end, you need automation that gives you returns on your investment(ROI).

“If you have bad tests, automation can help you do bad testing faster.”— James Marcus Bach How do we ensure that the test automation is working as a saviour and not adding to the testing woes? One wrong step in this direction and be assured to lose precious time and energy. Additionally, wrong automation will make the quality of the software suffer badly. Organizations are spending on automation for better software quality and a good ROI(Return on Investment) with time.