April 2017 - Seite 140 von 178 - Cloud Computing Köln

We are pleased to announce the expansion of HDInsight Application Platform to include Dataiku.

Azure HDInsight is the industry leading fully-managed cloud Apache Hadoop & Spark offering which allows customers to do reliable open source analytics with an industry-leading SLA. Dataiku develops Data Science Studio (DSS), a collaborative data science platform that enables companies to build and deliver their analytical solutions more efficiently.

This combined offering of DSS on HDInsight enables customers to easily use data science to build big data solutions and run them at enterprise grade and scale.

Microsoft Azure HDInsight – Reliable Open Source Analytics at Enterprise grade & scale

HDInsight is the only fully-managed cloud Hadoop offering that provides optimized open source analytical clusters for Spark, Hive, Interactive Hive, MapReduce, HBase, Storm, Kafka, and R Server backed by a 99.9% SLA. Each of these big data technologies are easily deployable as managed clusters with enterprise-level security and monitoring.

The ecosystem of applications in Big data has grown with the goal of making it easier for customers to solve their big data and analytical problems faster. Today, customers often find it challenging to discover these productivity applications and then in turn struggle to install and configure these apps.

To address this gap, the HDInsight Application Platform provides an experience unique to HDInsight where Independent Software Vendors (ISV’s) can directly offer their applications to customers – and customers can easily discover, install and use these applications built for the Big data ecosystem.
As part of this integration, Dataiku is bringing DSS to make collaborative data science much easier.

Dataiku Data Science Studio (DSS) – Prototype, deploy and run at scale

Dataiku provides Data Science Studio, the collaborative data science platform that enables professionals (data scientists, data engineers etc.) to collaborate on building analytical solutions. DSS has an easy to use team-based interface for data scientists and beginner analysts. A user can use DSS to implement a complete analytical solution – which could range from data ingestion (all data types, sizes, format etc.), data preparation, data processing, training and applying machine learning models, visualization and operationalizing the solution.

A user can use DSS to implement a complete analytical solution – which could range from data ingestion (all data types, sizes, format etc.), data preparation, data processing, training and applying machine learning models, visualization and operationalizing the solution.

DSS on HDInsight – Data science at enterprise grade & scale

A customer can install DSS on HDInsight using Hadoop or Spark clusters. They can install DSS on existing clusters which are running, or while creating new clusters. DSS 4.0 also added support for using Azure Blob Storage as a connector for reading data from.

When a user installs DSS on HDInsight, the user can make use of the benefits of Hadoop or Spark on HDInsight. Users can utilize DSS to build projects; the projects can generate MapReduce or Spark jobs, which makes DSS a great compliment to your HDInsight cluster. These jobs are executed as regular MapReduce or Spark jobs, and hence they get all the benefits of running these jobs on an enterprise grade platform. Since these jobs are running on HDInsight, customers can scale the cluster on demand, which allows a customer to run DSS at scale on HDInsight.

Getting started with DSS on HDInsight

Let us show a quick walkthrough of installing and getting started with DSS on HDInsight: The following screen shot shows a Spark cluster in the Microsoft Azure portal. A user can click the Applications tile to see the list of applications installed.

A user can select DSS, agree to the terms of agreement and install DSS. This is the simplicity associated with a one-click deployment experience. After the user has selected DSS, DSS is installed on the edge node, which is part of the cluster.

After DSS is installed, a customer can launch DSS using the “WEBPAGE” link. (This is the link to the DSS product.) A user must first authenticate with the cluster user credentials and then they can login with their DSS credentials

The following screenshot shows what a typical data science project’s landing page would look like in DSS. This shows both the summary of the project, as well as the timeline of the changes made to the project.

Resources

Following are some resource on learning more on this integration along with tutorials and videos.

Learn more about Azure HDInsight
Learn more about Dataiku DSS
Getting started with DSS on HDInsight
Use HDInsight and DSS to predict credit default
Dataiku and Microsoft HDInsight Integration
Video recording between Microsoft & Dataiku on using DSS on HDInsight
Install DSS on HDInsight from Azure marketplace
Ask HDInsight questions on stackoverflow
Dataiku Q&A

Summary

We are pleased to announce the expansion of HDInsight Application Platform to include Dataiku’s Data Science Studio. By deploying DSS on HDInsight, customers can easily build analytical solutions and run them at enterprise grade and scale.
Quelle: Azure

7. April 20177. April 2017

da Agency

Azure Stack Technical Preview 3 refresh with Azure PaaS services

This post was authored by the Azure Stack Team.

Today, we are excited to announce preview releases of Azure PaaS services for Azure Stack and a refresh to Azure Stack TP3. Last month, we released Azure Stack TP3 and provided additional information about hybrid use cases, the pay-as-you-use business model for Azure Stack, and roadmap updates. If you haven’t already, read Jeffery Snover’s Azure Stack TP3 blog post for more context. Additionally, we put together a whitepaper with an even more detailed roadmap of Azure services, integrated systems details, and initial geo-availability.

This update continues delivering Azure Services on premises so customers can create innovative applications for the hybrid cloud. Specifically, this release includes:

Azure App Service (Web apps, API apps, and Mobile apps)
Azure Functions
Updated versions of SQL/MySQL database services

New to App Service this release:

Azure Functions preview for AAD based deployments
Deployment in disconnected environments
Deployment on ADFS authenticated Azure Stack
Installation and deployment improvements
Azure Resource Manager (ARM) API version 2016-03-01 support for App Services
Synchronization of SKUs with Azure – i.e. Free (F1), Shared (D1), and Standard (S1, S2, S3)
Service reliability improvements

Azure Stack TP3 refresh: Based on feedback and several ongoing improvements/bug fixes, we’ve also updated the Azure Stack TP3 software for a better deployment and operational experience. A list of the latest features and improvements to Azure Stack TP3 is now available.

Important Note: If you have already deployed Azure Stack TP3, you will need to redeploy using the updated software before deploying the Azure PaaS services.

Visit our Azure Stack technical documentation page to guide your deployment efforts and look at the documentation for App Service and Functions, SQL, and MySQL.

Visit the Azure Stack forum for troubleshooting help and User Voice if you’d like to provide feedback. Learn more and see the current list of known issues.

We’d love to hear from you!
Quelle: Azure

7. April 20177. April 2017

da Agency

Facebook Wants To Teach You How To Spot Fake News On Facebook

What the new educational tool will look like in News Feed.

Facebook

In its latest move to help blunt the flow of misinformation on its platform, Facebook today rolled out a new initiative to educate users on how to spot “false news.”

Starting tomorrow, people in 14 countries will begin seeing a link to a “Tips for spotting false news” guide at the top of their News Feed. Clicking it brings users to a section offering 10 tips as well access to related resources in the Facebook Help Center. Facebook is also collaborating with news and media literacy organizations in several of countries to produce additional resources.

“Improving news literacy is a global priority, and we need to do our part to help people understand how to make decisions about which sources to trust,” Adam Mosseri, Facebook's VP of News Feed, wrote in a blog post about the initiative. “False news runs counter to our mission to connect people with the stories they find meaningful. We will continue working on this, and we know we have more work to do.”

Facebook&039;s 10 tips for spotting false news.

Facebook

Facebook has now announced several initiatives to try and stop the spread of misinformation and to support trustworthy information. It&039;s working with third-party fact checking organizations to flag false content in the News Feed, the company recently announced the Facebook Journalism Project to work with news organizations on products and business models, and it&039;s one of the funders of the new News Integrity Initiative, a $14 million project “focused on helping people make informed judgments about the news they read and share online.”

These moves come in response to outcry about the platform&039;s role in spreading fake news stories during the recent US election, and to public pressure it faced after CEO Mark Zuckerberg was initially dismissive of the issue. Now he, COO Sheryl Sandberg, and other top executives talk frequently about the responsibility Facebook has to help provide accurate information to its more than 1.8 billion users.

“We know that seeing accurate news on Facebook is really important to people on all sides,” Sandberg recently said on the PBS NewsHour. “No matter who you are, seeing the accurate story and seeing a diversity of opinions is really important. We know we have a responsibility, along with news rooms and classrooms and academic and other companies, to make sure people see accurate news.”

Quelle: <a href="Facebook Wants To Teach You How To Spot Fake News On Facebook“>BuzzFeed

7. April 20177. April 2017

da Agency

Red Hat joins the DPDK Project

Today, the DPDK community announced during the Open Networking Summit that they are moving the project to the Linux Foundation, and creating a new governance structure to enable companies to engage with the project, and pool resources to promote the DPDK community. As a long-time contributor to DPDK, Red Hat is proud to be a founding Gold member of the new DPDK Project initiative under the Linux Foundation.
“Open source communities continue to be a driving force behind technology innovation, and open networking and NFV are great examples of that. Red Hat believes deeply in the power of open source to help transform the telecommunications industry, enabling service providers to build next generation efficient, flexible and agile networks,” said Chris Wright, Vice President and Chief Technologist, Office of Technology at Red Hat. “DPDK has played an important role in this network transformation, and our contributions to the DPDK community are aimed at helping to continue this innovation.”
DPDK, the Data Plane Development Kit, is a set of libraries and drivers which enable very fast processing of network packets, by handling traffic in user space or on specialized hardware to provide greater throughput and processing performance. The ability to do this is vital to get the maximum performance out of network hardware under dataplane intensive workloads. For this reason, DPDK has become key to the telecommunications industry as part of Network Functions Virtualization (NFV) infrastructure, to enable applications like wireless and wireline packet core, deep packet inspection, video streaming, and voice services.

Open source projects like DPDK have taken a leadership role in driving the transition to NFV and enabling technology innovation in the field of networking by accelerating the datapath for network traffic across virtual switching and routing infrastructure.
It is opportune that this move is announced during the Open Networking Summit, an event which celebrates the role of open source projects and open standards in the networking industry. DPDK is a critical component to enable projects like OPNFV, Open vSwitch and fd.io to accelerate the datapath for network traffic across virtual switching and routing infrastructure, and provide the necessary performance to network operators.
Quelle: RedHat Stack