Both containers and virtual machines are virtual environments that comprise a number of computing components and are independent in nature, thereby allowing developers to scale applications in isolated runtimes. Both of these concepts aim at providing independent sets of resources to individual computing environments to ensure quick and reliable application performance.
Modern developers are under ever increasing pressure to deliver software applications to the business in record time. This means shorter development cycles and a push to have code production ready as early as possible. In addition, many development teams no longer throw the code over the metaphorical wall to be handled by operations and production support teams, but rather oftentimes own the entire end to end delivery chain.
The COVID-19 pandemic has forced government organizations to reassess their strategies, plans, and aspirations for digital transformation. Despite the uncertainty, IT leaders must quickly identify and act on strategies and plans that lead to positive outcomes. In many cases, governments will expand the role of digital technologies in service and program delivery.
One of the biggest failings of old-school, custom software development is its inability to help large organizations survive against unexpected challenges and quickly adapt to change. It could hardly be otherwise. For decades, companies were limited by inflexible business applications that took so much time to architect, develop, test and deploy. Not to mention the time it took to maintain, upgrade and enhance them. All of which set the stage for a revolution.
Replication (covered in this previous blog article) has been released for a while and is among the most used features of Apache HBase. Having clusters replicating data with different peers is a very common deployment, whether as a DR strategy or simply as a seamless way of replicating data between production/staging/development environments.
An exceptional embedded analytics offering is underpinned by the right strategy and framework - and this starts with a clear vision. To maximize the value of data assets, you may need to recognize and then address where your product may need to improve it’s BI maturity level. To do this, it’s time to focus on where your analytics development capability and tooling is today.
Unravel Data helps a lot of customers move big data operations to the cloud. Chris Santiago is Global Director of Solution Engineering here at Unravel. So Unravel, and Chris, know a lot about what can make these migrations fail. Chris and intrepid Unravel Data marketer Quoc Dang recently delivered a webinar, Reasons why your Big Data Cloud Migration Fails and Ways to Overcome. You can view the webinar now, or read on to learn more about how to overcome these failures.
Automated UI testing is a daily struggle for efficiency and reliability. A single misconfigured line of code can cost teams in hours of lost feedback time and test error triaging—potentially costing companies hundreds of thousands of dollars. In this case study we will see how interactions with only two web elements led to a 34% degradation in the test execution time.
Incredibly excited about today’s news that Qlik has acquired Blendr.io. Blendr.io’s easy-to-use, scalable and secure embedded integration and automation platform (iPaaS) will expand our ability to deliver on our vision of Active Intelligence, where real-time, up-to-date data triggers immediate action to accelerate business value across the entire data and analytics supply chain.
As businesses increasingly leverage data-driven decision making, the ability to use and understand data at the company-wide level becomes mission critical. While tech behemoths like Netflix, Airbnb, and Spotify have strong data cultures built over the last decade, most companies often face challenges getting up and running with data.
