Systems | Development | Analytics | API | Testing

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3 pillars for supporting realtime update infrastructure in transportation and logistics apps

Amazon was founded in 1994, went public in 1997, and reached a market cap of $1.5 trillion in 2020. As a result of Amazon’s successes and a long tail of rapidly modernizing ecommerce businesses, consumers and businesses alike have transformed their expectations around transportation and logistics. Consumers, for example, expect up-to-the-minute updates on package delivery, and businesses require, among other features, realtime asset and vehicle monitoring.

12 Times Faster Query Planning With Iceberg Manifest Caching in Impala

Iceberg is an emerging open-table format designed for large analytic workloads. The Apache Iceberg project continues developing an implementation of Iceberg specification in the form of Java Library. Several compute engines such as Impala, Hive, Spark, and Trino have supported querying data in Iceberg table format by adopting this Java Library provided by the Apache Iceberg project.

Radiall transforms decision-making with Qlik Cloud

Radiall, a world leader in the electronic connectors industry, has completely redesigned its BI ecosystem to become a data-driven company. Among the objectives: reduce the number of tools and data quality issues and deploy a data culture within the group. Radiall chose Qlik for the power of its powerful associative engine and its embedded ETL capabilities. The first applications implemented were the sales and marketing analysis report and the budget planning applications but the platform was quickly extended to the finance R&D, quality, HR and IT divisions.

A Complete Guide To Testing With Appium 2.0

Appium 2.0 has officially been released after several years of updating the Appium 1.x, and this is the most major Appium release in the past 5 years. Many significant changes were introduced, including removing deprecated features, improvements, and most importantly, re-envisioning Appium as a platform. In this article, we’ll explore in-depth on how you as a tester can leverage this transformation in their projects.

An Introduction to Devise for Ruby on Rails

With over 20,000 GitHub stars and lots of integrations, the Devise gem is one of the most popular gems in the Ruby landscape. So why would we term it one of Ruby's "hidden" gems? Well, as popular as it is, most developers only scratch the surface of the library's capabilities. In this two-part series, we'll take a deep dive into Devise. In this first part, we'll learn some of the basics, including: In part two, we'll look at more advanced usages of Devise, including: Let's get started!

An Introduction to Playwright for Node.js

Test coverage plays a key role in providing bug-free experiences to users. At the same time, writing and maintaining test scripts for different web browsers is cumbersome and time-consuming. Fortunately, there is a solution! Playwright is a cutting-edge tool that makes it easy to automate modern web browsers. Through its powerful API, you can write end-to-end test scripts that run smoothly on different browsers. In this article, we'll cover: Let's dive straight in!

How To Use Dynamic Sampling in Moesif

Join Dylan as we show users how to create Dynamic Sampling rules within Moesif. In this tutorial, we will cover how to: Dynamic sampling is a fantastic cost-savings feature available to customers on our Enterprise plan. Dynamic sampling enables you to control which API calls are logged to Moesif based on customer or API behavior. Moesif will intelligently extrapolate metrics for accurate reporting even with multiple sample rates in place. That means that no matter what rules or sample rates you have set up you can be sure you are still seeing an accurate representation of your data.

Client Lifecycle Management Process: 5 Best Practices for Banks

If you’re worried about the potential for heightened regulatory scrutiny in financial services, you’re not alone. Business operations teams everywhere are focused on the end-to-end, client lifecycle management (CLM) process as they cope with ever-changing regulations governing how, when, and where client data can be stored and accessed. It’s hard to stay compliant when customer data is spread across multiple operational silos.