Systems | Development | Analytics | API | Testing

Large-scale enterprise modernization initiatives are taking place across the public sector, with agencies striving to gain efficiencies through better use of more modern technologies, such as low-code platforms, and employing data in novel ways in the decision-making process. These modernization efforts disrupt the business processes that have developed around the outdated solutions these efforts aim to replace.

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.

Any organisation wanting to pursue digital transformation understands the value of good-quality data. Data is akin to digital gold, and it is immensely important to strategic decision-making. Ensuring that your data or BI team has everything they need is part of the challenge.

Data warehousing improves access to information, speeds up query-response times, and allows businesses to fetch deeper insights from big data. Previously, companies had to invest a lot in infrastructure to build a data warehouse. The advent of cloud technology has significantly reduced the cost of data warehousing for businesses.

Python is a popular programming language known for its simplicity and versatility. It's commonly used for web development, data analysis, and automation. While it's not usually associated with client-side development, it can be useful in scenarios where server-side applications need to exchange data in realtime. This is why we’ve released a new Python Realtime SDK component that enables developers to integrate Ably's realtime functionality into their Python applications.

The final iteration of our series on ASCII files; how to combine dbt and Fivetran to integrate ASCII files.

There are many components to a successful web testing strategy, but one of the most often overlooked is the importance of visual UI testing in addition to functional testing. Most teams will focus on one over the other, but to truly catch as many bugs as possible, you’ll need to incorporate both. First, you need to understand what the difference is and why they’re both needed.

As generative AI continues to captivate attention with its transformative potential, there is a danger that traditional AI and ML become overshadowed. But as I mentioned in my last blog, this would be a mistake as traditional AI methods still hold immense value and relevance, and likely more so than generative AI in the near term.

In today's "Big Data" world, data integration is a critical business process. With the exponential growth of information seen in enterprises all over the globe and increasingly competitive marketplaces, there is a real need to consolidate and analyze business data. Big Data is made up of large amounts of information from many sources: customer interactions, Internet of Things (IoT) devices, mobile apps, SaaS cloud services, and more.

Apache Impala and Apache Kudu make a great combination for real-time analytics on streaming data for time series and real-time data warehousing use cases. More than 200 Cloudera customers have implemented Apache Kudu with Apache Spark for ingestion and Apache Impala for real-time BI use cases successfully over the last decade, with thousands of nodes running Apache Kudu.