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Cloud computing has risen from relative obscurity to the No. 1 tech area of interest in the world, according to the 2017 Global Knowledge IT Skills and Salary Report.
In this one-hour webinar, AWS consultant and instructor Ryan Dymek, will discuss how to optimize your AWS architecture design.
Cloud adoption continues to soar. In fact, worldwide Infrastructure-as-a-Service (IaaS) public cloud services grew by 29.5 percent in 2017, according to Gartner. If it’s not in a company’s current plans to utilize cloud technology, it most certainly will be (or should be) in the next couple of years.
The idea of building and running applications without thinking about the servers (AKA serverless computing) is a developers dream come true. Watch expert Rich Morrow to understand: The benefits of using serverless computing Different architectures that use serverless computing How AWS services, like Lambda, S3, API Gateway and DynamoDB work together to enable faster and more flexible application deployment and management. This video also features a live demo: Building a web app with only serverless components.
This Certification Prep Guide provides an overview of the current MCSE: Cloud Platform and Infrastructure certification and offers helpful tips that you can use when preparing for your MCSE certification exam.
This Certification Prep Guide provides an overview of the current CompTIA Cloud+ certification and offers helpful tips that you can use when preparing for your CompTIA Cloud+ certification exam.
As far as modern architectures go, there are few more complicated than an IoT pipeline. You’ve got to consider an ingestion layer (typically streaming) that may undergo manic load. You’ve got to think of data tagging, storage (probably across multiple engines), archival and access—both internal and external. And all of it has to scale like crazy, be as cost effective as possible, and use automation wherever it can. Oh, and your boss needs the IoT pipeline built by tomorrow. Short timelines? Tight budget? Unrealistic expectations? Unfortunately, these asks are realities for many cloud professionals. AWS knows this and is here to help.
In the digital age, people and intellectual property have supplanted physical assets as the most important criteria for determining the value of an organization. It is the employees who develop the next big product or improve the practices, processes, services and internal culture that add significant value to an organization.
Behind various Google services lies the powerful Cloud AI, a neural net-based, machine learning technology that Google has perfected for use with services like image search and voice recognition. Here are five ways you could use Cloud AI to improve your business.
A longtime leader in data analytics, Google continues to earn their position by continually improving their data analytics offerings. Now, with Google Cloud Platform (GCP), you can capture, process, store, and analyze your data in one place, allowing you to change your focus from infrastructure to analytics that informs business decisions. However, you can also use GCP Big Data tools in combination with other cloud-native and open-source solutions to meet your needs. Below is an overview of GCP Big Data Tools and how you might utilize them to improve analytics.