Table of Contents Python is one of the most popular programming languages used in data engineering, particularly in the ETL (Extract, Transform, Load) process. Its flexibility, extensive libraries, and ease of use make it a great choice for building and optimizing ETL pipelines.

Testing-as-a-Service (TaaS) is rapidly gaining popularity growing at a compound annual growth rate (CAGR) of 14% from 2024 to 2030. Organizations adopting TaaS report up to 30% cost savings compared to traditional in-house testing. Testlio October 5th, 2024 Discover Outsourced Testing Best Practices Engineers and product managers face the challenge of balancing speed, quality, and cost in software testing as technology continues to evolve rapidly.

Table of Contents Amazon Redshift has become one of the most popular data warehousing solutions due to its scalability, speed, and cost-effectiveness. As the data landscape continues to evolve, businesses are generating and data processing increasingly large datasets. Efficient analysis of these datasets is essential to making informed, data-driven decisions. Amazon Redshift allows companies to extract meaningful insights from vast amounts of structured and semi-structured data.

Transactions-per-Second (TPS) is a valuable metric for evaluating system performance and is particularly relevant for engineers overseeing Kubernetes environments.TPS, alongside average response time, provides critical insights into system performance during load testing. This post covers two approaches to calculating TPS; a manual approach applicable in all environments, and an automatic Kubernetes-specific solution using production traffic replication.

At some point, your development team may be considering implementing load testing (also known as stress testing) as part of your software testing process. Load testing validates that your web app is able to withstand a large number of simultaneous users, decreasing the chance that any traffic spikes will bring down your services once deployed. These stress tests can be highly granular, giving you the opportunity to test run virtually unlimited strategies before they are set into the wild.

As developers, one of the most important things we can consider when designing and building applications is the ability to know if our application is running in an ideal operating condition, or said another way: the ability to know whether or not your application is healthy. This is particularly important when deploying your application to Kubernetes. Kubernetes has the concept of container probes that, when used, can help ensure the health and availability of your application.

AI has entered many aspects of data integration, including data mapping. AI data mapping involves smart identification and mapping of data from one place to another. Sometimes, creating data pipelines manually can be important. The process might require complex transformations between the source and target schemas while setting up custom mappings.

At a high level, bad data is data that doesn’t conform to what is expected. For example, an email address without the “@”, or a credit card expiry where the MM/YY format is swapped to YY/MM. “Bad” can also include malformed and corrupted data, such that it’s completely indecipherable and effectively garbage.

As businesses continue to digitize, the importance of load testing has surged, with the market witnessing a significant rise in the adoption of load testing tools. According to a recent report, the global load testing market is expected to grow at a CAGR of 12.5% from 2021 to 2026, reflecting the increasing demand for reliable and scalable applications.

Year after year, Cyber 5 brings millions of online customers to retailers across North America. Customers expect a flawless digital experience, especially when countless competitors are vying for their business.