In the last one decade, the growth of Android was whopping which is very much evident if you look at the stats. Back in 2009, Android shared only 3.9% of the total market which has grown to over 72% in the last 10 years. Talking about the current trend, almost everyone who is planning to develop a website goes for developing an Android app.

DevOps merges operational and development teams to boost the speed of software development and ensure that the final product is delivered as clean and consistent as possible. Ultimately, DevOps allows teams to increase product quality and speed of delivery in even the most highly competitive environments. The idea of weaving together disparate teams to accelerate development might appeal to many organizations, but some common challenges may come up before implementing a DevOps environment.

This is the story of how Appian helped organizations worldwide get thousands of people back to work during the worst global pandemic in over a century, safely and legally. The Appian Workforce Safety Solution COVID-19 threw people worldwide into what seemed like a black hole of chaos. As infection rates rapidly grew, and related concerns grew even faster, communities struggled to respond.

Performance optimization is a basic need for software development. When it comes to optimizing app performance, tracking frequency, maintaining production, or perpetuation method calls, profilers play a vital role. Learn why Python cProfile is a recommended profiling interface and how it enhances your software performance.

There are things your in-house QA team can do extremely well. This might include collaborating with engineering leaders and product managers, strategizing test cases, and converting former test cycles into automated scripts. And then there are things that an enterprise organization’s QA team will always struggle with, regardless of how smart, organized, and empowered they are.

When it comes to data storage, there is almost as much diversity in the types of databases as there is in the data that they contain. Designing and implementing a strong enterprise data strategy means that you need to be aware of the different databases and how you might best apply them within your organization. In IT, the term "flat file" means something very different from the heavy-duty steel construction file cabinets that you might buy from Safco.

Last month, we were proud to launch our Hitachi Kubernetes Service, a true storage-as-a-service (SaaS) offering to improve the performance and management of multiple Kubernetes environments. By enabling users to manage their clusters simply and securely across any major cloud provider and on premises, Kubernetes can play an instrumental role in businesses’ modernization efforts. It’s for this reason that we are always working to get it on the radar of our existing clients.

One of the changes that we've seen happening in the analyst space recently is a huge shift in thinking. Gartner in particular is now talking about augmented consumers and multi-experience analytics. To me, this is really interesting because they’re talking about the business user and how they want to work and consume data. In the past it was all about the data analyst, but focusing on users opens up an entirely new level of thinking.

From the Wright Brothers and Ada Lovelace, to Elon Musk and Steve Jobs, when we consider who is behind the most celebrated innovations and industry transformations, we often think about individual bright thinkers and disruptors. However, over the years, studies have shown that the greatest potential lies in the “power of many," fostered by a shift in how new generations work.

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. While model training on small data can typically take minutes, doing the same on large volumes of data can take hours or even weeks. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads.