As the amount of data that needs to be processed continues to increase, more and more IT teams are turning to cloud computing to help manage their large workloads. Workload Automation plays a vital role in managing virtual and cloud resources and can mean the difference between successful, cost-efficient cloud computing, and hidden-cost ridden operations. A Workload Automation solution that offers automated provisioning and deprovisioning of virtual and cloud-based resources, based on both historical and predictive analytics, can introduce a form of machine learning into your cloud environment and help you optimize your resource usage. The EMA Radar Report commends ActiveBatch Workload Automation on its standout cloud features, such as Smart Queue and Managed Queue, and its prebuilt integrations with VMware, Amazon EC2, Microsoft Azure, and System Center Virtual Machine Manger. The report states that these features and capabilities "make ActiveBatch a strong choice for anyone relying on hybrid or multi-cloud to optimize resource usage."
We support a lot of different hypervisor platforms from VMware to OpenStack to Hyper-V," explained Dan Florea, Director of Product Management at Tintri, in this SYS-CON.tv With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend @CloudExpo @ThingsExpo, October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-4, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation. Join Cloud Expo @ThingsExpo conference chair Roger Strukhoff (@IoT2040), October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, for three days of intense Enterprise Cloud and'Digital Transformation' discussion and focus, including Big Data's indispensable role in IoT, Smart Grids and (IIoT) Industrial Internet of Things, Wearables and Consumer IoT, as well as (new) Digital Transformation in Vertical Markets. Accordingly, attendees at the upcoming 21st Cloud Expo @ThingsExpo October 31 - November 2, 2017, Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, will find fresh new content in a new track called FinTech, which will incorporate machine learning, artificial intelligence, deep learning, and blockchain into one track.
Data consumers need a "data supermarket," whereby all data, regardless of source, format, or volume, is easily accessible; what they need is data virtualization. Data virtualization forms a virtual data layer, just like a supermarket, that lies between the data sources and the consuming applications. Instead of working with copies of the data itself, data virtualization works only with the metadata (the information needed to access each source) in a virtual data layer. In an increasingly data-driven world, fast access to data is key for making real-time business decisions, so why waste precious time, money, and resources using outdated data integration tools, when you can "shop" with ease using data virtualization?
Did You Want a Side of SLBS (Serverless BS) with Your Software or Hardware FUD? A few years ago a popular industry buzzword term theme included server less and hardware less. It turns out, serverless BS (SLBS) and hardware less are still trendy, and while some might view the cloud or software-defined data center (SDDC) virtualization, or IoT folks as the culprits, it is more widespread with plenty of bandwagon riders. To me what's ironic is that many purveyors of of SLBS also like to talk about hardware. What's the issue with SLBS?
The Microsoft Data Science Virtual machine (VM) is a custom Azure VM based on Windows Server 2012 with several popular tools for data science modeling/development like: * SQL Server 2016 Developer Edition * Microsoft R server Developer Edition * Anaconda Python with Juypter notebooks * Visual Studio 2015 Community edition with language and Azure tools and * ML and Deep Learning tools like xgboost, CNTK, mxnet More information on how to use the VM can be found on the [documentation page](http://aka.ms/dsvmdoc). If are wondering about things you can do with the DSVM read this [How-To Guide to the Data Science Virtual Machine](http://aka.ms/dsvmtenthings). Here is a list of key software on the Data Science Virtual Machine and comparison between the Windows and Linux editions of the product.
As the Internet of Things (IoT) revs up the automotive industry, connected cars are becoming "devices on wheels" with in-vehicle systems connected to the Internet. Therefore, car manufacturers must develop new services and applications to provide consumers with more personalized driving options. Collecting and analyzing this data will help carmakers understand user preferences, develop new applications, and give consumers a wider, more personalized range of driving choices. To get new automotive IT services to market quickly, carmakers may wish to partner with application developers, IT security companies, and enterprise companies with established cloud-based infrastructures and data analysis experience.