CloudPhysics Unveils Cost Calculator

CloudPhysics has introduced the Cost Calculator for Private Cloud with the Public Cloud Comparison tool. This data-driven solution automates the process of determining the accurate costs of a customer’s currently resourced on-premises Virtual Machines (VMs). Customers can compare those amounts to the costs for the same VMs if they were migrated to a public cloud.

The Cost Calculator for Private Cloud allows the customer to rightsize VMs by comparing a VM’s current resources, such as CPU and storage, with the amount the VM actually requires to perform its functions. Because many VMs are overprovisioned with resources, rightsizing helps a customer save costs per workload, whether on-premises or in the cloud. By rightsizing workloads, customers are assured that VM provisioning fits actual usage. 

The calculator also offers customers the unique ability to conduct “apples-to-apples” comparison of virtual workloads in a private cloud model, where resources are shared — vs. the public cloud model, where resources are subscribed from a cloud service provider. Users can create scenarios that compare their private cloud costs vs. public cloud estimates with utilization levels at Peak, 95th, and 99th percentiles. They can then accurately determine what these workloads cost to operate in the public cloud at those respective levels. 

“Organizations typically do not have a current cost-per-workload model in their private cloud, and have very poor tools to allow for the pricing and comparison of private vs. public cloud costs,” said Chris Schin, VP, Products at CloudPhysics. “Our Cost Calculator for Private Cloudensures that IT decision makers have real, actionable data regarding the savings a public cloud can potentially provide vs. their current operational costs.”

The Cost Calculator for Private Cloud determines cost based on selected workloads, hosts, clusters, or data centers and calculates the cost per workload/virtual machine (VM). This allows IT administrators to understand the cost of a workload based on size and resource utilization in a private cloud environment. Once private cloud costs are known, workloads can accurately be compared against public cloud hosting costs to determine if savings can be achieved on a workload-by-workload basis.

Source: CloudStrategyMag

Review: Google Bigtable scales with ease

When Google announced a beta test of Cloud Bigtable in May 2015, the new database as a service drew lots of interest from people who had been using HBase or Cassandra. This was not surprising. Now that Cloud Bigtable has become generally available, it should gain even more attention from people who would like to collect and analyze extremely large data sets without having to build, run, and sweat the details of scaling out their own enormous database clusters.

Cloud Bigtable is a public, highly scalable, column-oriented NoSQL database as a service that uses the very same code as Google’s internal version, which Google invented in the early 2000s and published a paper about in 2006. Bigtable was and is the underlying database for many Google services, including Search, Analytics, Maps, and Gmail.

Source: InfoWorld Big Data

Big data hits billion in revenue — and counting

Big data has been a big buzzword for more than a few years already, and it has solid numbers to back that up, including $46 billion in 2016 revenues for vendors of related products and services. But the big data era is just beginning to dawn, with the real growth yet to come.

So suggests a new report from SNS Research, which predicts that by the end of 2020, companies will spend more than $72 billion on big data hardware, software, and professional services. While revenue is currently dominated by hardware sales and professional services, that promises to change: By the end of 2020, software revenue will exceed hardware investments by more than $7 billion, the researcher predicts.

“Despite challenges relating to privacy concerns and organizational resistance, big data investments continue to gain momentum throughout the globe,” the company said in a summary of the report, which was announced Monday.

Others echo the same sentiment.

“Sooner rather than later, big data will become table stakes for enterprises,” said Tony Baer, a principal analyst at Ovum. “It will not provide unique competitive edge to innovators, but will add a new baseline to the analytics and decision support that enterprises must incorporate into their decision-making processes.”

It is indeed still early days for such initiatives, said Frank Scavo, president of Computer Economics.

“Business intelligence and data warehousing are top areas for technology spending this year, but only about one-quarter of organizations are including big data in their investment plans,” said Scavo, citing his own company’s research. “So, what we are seeing today is just the tip of the iceberg.”

Cloud storage and services are making big data affordable for most organizations, but realizing the benefits can be a challenge. That’s in large part due to the current shortage of business analysts and IT professionals with the right skills, particularly data scientists, he said.

“If you’re planning to invest in big data, you’d better be ready to invest in your people to develop the needed skills,” Scavo said. “At the same time, if you’re an IT professional just starting out in your career, big data would be a great area to focus on.”

Source: InfoWorld Big Data

IDG Contributor Network: How to answer the top three objections to a data lake

We’ve all seen the marketing hype surrounding the data lake. Data lakes are much like Michael Corleone at the end of The Godfather. Data lakes will answer all your questions and solve all your problems. However, as with Michael’s pronouncement(s) at the end of The Godfather, there is a downside to this “offer” that marketers may think we cannot refuse. There is usually a set of stakeholders out there who are unfamiliar with Hadoop or the concept of a data lake or perhaps just not interested in changing the status quo of their organizations.

As a data architecture, you are pitching a data lake like you do one of those mountain lakes on travel websites or George Clooney movies … lakes are cool, clear, and usually have the reflection of a snow-tipped mountain peak on their surface to show the purity of the contents within. Everyone wants to drink water from this source. However, when some people hear the concept — data from many sources being stored without a schema for some possible future benefit — they will think more about the concept of a data swamp rather than a pristine data lake.

Data swamps are places where unknown data sits in a Hadoop cluster. You don’t know where the data came from. You don’t know how old the data is. You have no idea what you might use the data for. Heck, the first use of this type of data before a skeptical executive more concerned with the status quo than organizational change will evoke the classic, “what’s your data source? How can you verify this information? I have different experiences….”

But before you can even get to that meeting where people start to question the data from your data lake, you need to propose, build, and populate one. Here are the top three (3) objections that I often hear to “discourage” any budding data architect from attempting start their data lake initiative, and how you might answer those objections:

Aren’t data lakes just another silo to get in the way? Just like the name implies, data lakes provide the opportunity to put all that pure data into a single location. This allows for information from those new, and often voluminous, data sources to share an environment with traditional data sets and each other. This allows for data-driven organizations to discover links between data sets such as mobile and social, make new insights from the data, and potentially create new business models such as how Uber changed the personal transportation business. I would answer this objection with the advances in data integration technologies such as data virtualization and ETL/ELT/ET/ETLT, as well as the ability to share data between data management architectures. The day of “data silos” is more about “want to” than “can’t do.”

Data lakes aren’t robust enough for our needs…Hadoop isn’t even 10 years old! I would say that the above objection is provided by someone who is invested in the care, feeding, and maintenance of a data warehouse. The types of “needs” that this objection is attempting to address are data governance, quality, stewardship, and lineage. True, the data governance practices of data lakes lags behind those other data architectures based on the concept of ‘schema on write’ where you predetermine the questions before you create and populate the structure. I would answer that a data lake attempts to solve a different set of requirements. Instead of assuring the quality of the data for “regulatory quality reporting” (i.e., someone goes to jail if the numbers are wrong), data lakes are designed to allow for discovery and then the potential use for new business models. A data lake’s data quality practices are less about the syntactic quality of the data (are all the fields perfect?) and more about the semantic quality of the data (can we use this well?).

Data lakes threaten the established data management structures such as the data warehouse More often than not, I hear this one coming out the mouth of someone who sells proprietary data warehouse storage components…yes. Some in the EDW world find the presence of the data lake to be a threat to the “single version of the truth” component of the enterprise data warehouse. However, more often than not, the data that exists within a data lake isn’t the type of curated structured data that data warehouses are known for. I would answer that the data that exists within the data lake is more often the type of atomic level event data with lots of extra fields that haven’t proven themselves yet “worthy” of placement in the data warehouse. Part of this is the concept of separating the signal from the noise. Another is the concept that pouring potential petabytes of data into the EDW will cause to two things to happen. One, the data quality people will become “concerned” (okay, have a heart attack) over the data coming into the platform. Two, the storage vendor will retire early to some golf course with the purchase agreement to handle all that information

After you hear the objections a couple hundred of times, the question then becomes: is a data lake worth the time, trouble, and effort if it might devolve from the pure data sources high in the mountains if this is the type of resistance that you encounter? The answer to that is “most certainly!” The advantages of the data lake outweigh the risks. The data lake is how data-driven organizations will validate and power their new businesses.

Does your organization want to be part of the future or part of the past?

This article is published as part of the IDG Contributor Network. Want to Join?

Source: InfoWorld Big Data

Google Analytics just got a new AI tool to help find insights faster

Services like Google Analytics are great for amassing key data to help you make the most of your web efforts, but zeroing in on the parts that matter most can be a time-consuming challenge. On Friday, Google added a new feature to its analytics service that taps AI to surface insights automatically.

Now available in the Assistant screen in the Google Analytics mobile app, the new automated insights feature “lets you see in 5 minutes what might have taken hours to discover previously,” wrote Ajay Nainani, product manager for Google Analytics, in a blog post.

The tool taps Google machine intelligence to find key insights from among the thousands of metric and dimension combinations that can be reported in Google Analytics. More specifically, it combs through data and offers relevant insights and recommendations.

If you’re a retailer trying to get ready for the holiday season, for instance, the tool can instantaneously surface opportunities and anomalies hiding in your data, such as which products are experiencing higher-than-normal sales growth, which advertising channels are driving the most conversions and the best returns, and what devices customers are using to engage with your brand.

The tool also offers quick tips on how to improve your Google Analytics data. And because it’s based on artificial intelligence, it gets smarter over time as it learns more about your business and how you use the software, Google says.

The new automated insights feature is now available with the official Google Analytics mobile app on Android and iOS for English-speaking users. Google’s now working on bringing it to the web version of the software and to other languages as well, Nainani said. Meanwhile, Google invites users to suggest insights they’d like to see automated and is collecting those ideas through an online form.

Source: InfoWorld Big Data

SaaS Growth Driven By ERP & Collaboration

New Q2 data from Synergy Research Group shows that the worldwide enterprise SaaS market grew 33% year on year to reach well over $11 billion in quarterly revenues. While not the largest segment, ERP grew the most rapidly achieving 49% growth, while the largest segment, collaboration, grew by 37%. Microsoft has now overtaken Salesforce to become the overall enterprise SaaS market leader, though the market remains somewhat fragmented with each major segment featuring a different leader. Microsoft is dominant in collaboration while Salesforce dominates the CRM segment. Other leading SaaS providers include SAP, Oracle, Adobe, ADP, IBM, Workday, Intuit and Cisco. Among the top ten companies Oracle achieved the highest growth rate followed by Microsoft.

The enterprise SaaS market is somewhat mature compared to other cloud markets like IaaS and PaaS. Nonetheless, Synergy forecasts that it will more than triple in size over the next five years, with strong growth across all segments and all geographic regions. Meanwhile the consumer SaaS market is much smaller than the enterprise market and is not growing as strongly.  However, in this segment there is huge growth for Microsoft, which has more than doubled its revenues on a rolling annualized basis.

“In SaaS a big battle is playing out between the traditional broad-based software vendors and companies that are focused on a specific application area or industry sector, many of which are entirely cloud based,” said John Dinsdale, a chief analyst and research director at Synergy Research Group. “It might be tempting to assume that the latter camp are leading the charge, but in fact the traditional software vendors are growing their SaaS revenues more rapidly, helped by their huge base of on-premise software customers that can be aggressively targeted for conversion to a SaaS consumption model.”

Source: CloudStrategyMag

Red Hat Releases OpenStack Platform 9

Red Hat, Inc. has announced the general availability of Red Hat OpenStack Platform 9, its highly scalable, open Infrastructure-as-a-Service (IaaS) platform designed to deploy, scale and manage private cloud, public cloud, and Network Functions Virtualization (NFV) environments. Based on the OpenStack community “Mitaka” release, Red Hat OpenStack Platform 9 offers customers a more secure, production-ready automated cloud platform integrated with Red Hat Enterprise Linux 7.2, Red Hat Ceph Storage 2, and Red Hat CloudForms for a hybrid cloud management and monitoring.

“As customers continue to require more advanced workloads capabilities on top of their OpenStack deployments, we have updated Red Hat OpenStack Platform to go beyond just providing a secure, flexible base to build a private cloud. With this release of Red Hat OpenStack Platform 9, we continue to add capabilities to meet the production requirements of enterprises rolling our private clouds and service providers deploying NFV,” said Radhesh Balakrishnan, general manager, OpenStack, Red Hat.

Red Hat OpenStack Platform has emerged as a proven solution to power private clouds across hundreds of customers worldwide, such as BBVA; Cambridge University; FICO; NASA’s Jet Propulsion Laboratory; Paddy Power Betfair; Santander Bank; and Verizon. It is backed by a robust ecosystem of partners, including Cisco, Dell, Intel, Lenovo, Rackspace and more. Red Hat co-engineers and integrates its OpenStack platform with Red Hat Enterprise Linux, and the KVM (Kernel-based Virtual Machine) virtualization layer from the recently updated Red Hat Virtualization. Earlier this month, Red Hat was named a “Visionary” in the 2016 Gartner Magic Quadrant for x86 Server Virtualization.

Red Hat OpenStack Platform 9 builds on the proven, trusted foundation of Red Hat Enterprise Linux to provide critical dependencies needed in production OpenStack environments centered around service functionality, third-party drivers, and system performance and security. It is among the only production-ready OpenStack distributions that offers automated upgrade and update paths for mission-critical operations. Red Hat OpenStack Platform 9 brings significant updates from the upstream Mitaka version to nearly every OpenStack service:

• Automated updates and upgrades with Red Hat OpenStack Platform Director: Red Hat to enables users to upgrade their OpenStack deployments through the automation and validation mechanisms of the Red Hat OpenStack Platform Director, based on the upstream community project TripleO (OpenStack on OpenStack). This in-place upgrade tool offers a simplified means to take advantage of the latest OpenStack advancements, while preventing downtime for production environments.
• Live migration improvements and selectable CPU pinning from OpenStack Compute (Nova): The Compute component now offers a faster and enhanced instance of the live migration process, helping system administrators to observe its progress and even pause and resume the migration task. A new CPU pinning feature can dynamically change the hypervisor behavior with latency-sensitive workloads such as NFV, enabling more fine-grained performance control.
• Tech Preview of Google Cloud Storage backup driver in OpenStack Block Storage (Cinder): As part of Red Hat’s continued collaboration with Google, new disaster recovery policies in Red Hat OpenStack Platform 9 now extend to the public cloud using integrated drivers created for Google Cloud Storage. This new feature enables more secure backups of critical data across the hybrid cloud.

Management for OpenStack

To help accelerate service delivery and enable self-service for system administrators building OpenStack private clouds, Red Hat OpenStack Platform 9 connects with Red Hat CloudForms to provide a consistent, automated cloud deployment environment. Included with a Red Hat OpenStack Platform subscription, Red Hat CloudForms provides inherent discovery, monitoring, and deep inspection of OpenStack resources, enabling policy-based operational and lifecycle management over all OpenStack infrastructure components, as well as virtualized workloads running on OpenStack.

Additionally, the updated Red Hat OpenStack Platform Director can also deploy Red Hat Ceph Storage, the industry-leading, integrated software-defined storage solution for Red Hat OpenStack Platform private clouds. Red Hat OpenStack Platform 9 includes 64TB of free object and block storage for customers evaluating a robust, scale-out cloud storage solution.

Source: CloudStrategyMag

Facebook taps deep learning for customized feeds

Serving more than a billion people a day, Facebook has its work cut out for it when providing customized news feeds. That is where the social network giant takes advantage of deep learning to serve up the most relevant news to its vast user base.

Facebook is challenged with finding the best personalized content, Andrew Tulloch, Facebook software engineer, said at the company’s recent @scale conference in Silicon Valley. “Over the past year, more and more, we’ve been applying deep learning techniques to a bunch of these underlying machine learning models that power what stories you see.”

Applying such concepts as neural networks, deep learning is used in production in event prediction, machine translation models, natural language understanding, and computer vision services. Event prediction, in particular, is one of the largest machine learning problems at Facebook, which must serve the top couple of stories out of thousands of possibilities for users, all in a few hundred milliseconds. “Predicting relevance in and of itself is a very challenging problem in general and relies on understanding multiple content modalities like text, pixels from images and video, and the social context,” Tulloch said.

The company must also deal with content posted in more than 100 languages daily, thus complicating classic machine learning, Tulloch said. Text must be understood at a deep level for proper ranking and display. In its deep learning efforts, Facebook has gone with its DeepText text understanding engine, which reads and understands users’ posts and has been open-sourced in part.

Big data salaries set to rise in 2017

Starting salaries for big data pros will continue to rise in 2017 as companies jockey to hire skilled data professionals.

Recruiting and staffing specialist Robert Half Technology studied more than 75 tech positions for its annual guide to U.S. tech salaries, including 13 jobs in the data/data administration field.

In the big picture, starting salaries for newly hired IT workers are forecast to climb 3.8 percent next year. (See also: 14 hot network jobs/skills for 2017)

In the data world, the highest paying title is big data engineer; these specialists can expect starting salaries ranging from $135,000 to $196,000. The biggest raise is projected for data scientists, who can expect a 6.4 percent boost in 2017.