From time to time, I revisit the past just to ensure I am not choking on the same spin or hype bullshit I resent in this world of marketing the worlds perfect software (everyone claims to be it, noone is it)
From the very first Big Data conference ever (and kudos to Steve Wooledge at AsterData for coining the buzz word of the decade- BIG DATA)- a two year old interview.
Is it relevant- or outdated.
message from Amazing Amazon’s cloud team- this will also help for #rstats users given that revolution Analytics full versions on RHEL.
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on-demand instances of Amazon EC2 running Red Hat Enterprise Linux (RHEL) for as little as $0.145 per instance hour. The offering combines the cost-effectiveness, scalability and flexibility of running in Amazon EC2 with the proven reliability of Red Hat Enterprise Linux.
Highlights of the offering include:
For more information, please visit the Amazon EC2 Red Hat Enterprise Linux page.
which is
Amazon EC2 running Red Hat Enterprise Linux provides a dependable platform to deploy a broad range of applications. By running RHEL on EC2, you can leverage the cost effectiveness, scalability and flexibility of Amazon EC2, the proven reliability of Red Hat Enterprise Linux, and AWS premium support with back-line support from Red Hat.. Red Hat Enterprise Linux on EC2 is available in versions 5.5, 5.6, 6.0, and 6.1, both in 32-bit and 64-bit architectures.
Amazon EC2 running Red Hat Enterprise Linux provides seamless integration with existing Amazon EC2 features including Amazon Elastic Block Store (EBS), Amazon CloudWatch, Elastic-Load Balancing, and Elastic IPs. Red Hat Enterprise Linux instances are available in multiple Availability Zones in all Regions.
Pay only for what you use with no long-term commitments and no minimum fee.
On-Demand Instances let you pay for compute capacity by the hour with no long-term commitments.
| Standard Instances | Red Hat Enterprise Linux |
|---|---|
| Small (Default) | $0.145 per hour |
| Large | $0.40 per hour |
| Extra Large | $0.74 per hour |
| Micro Instances | Red Hat Enterprise Linux |
| Micro | $0.08 per hour |
| High-Memory Instances | Red Hat Enterprise Linux |
| Extra Large | $0.56 per hour |
| Double Extra Large | $1.06 per hour |
| Quadruple Extra Large | $2.10 per hour |
| High-CPU Instances | Red Hat Enterprise Linux |
| Medium | $0.23 per hour |
| Extra Large | $0.78 per hour |
| Cluster Compute Instances | Red Hat Enterprise Linux |
| Quadruple Extra Large | $1.70 per hour |
| Cluster GPU Instances | Red Hat Enterprise Linux |
| Quadruple Extra Large | $2.20 per hour |
Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed are billed as full hours.
and
Instances of this family are well suited for most applications.
Small Instance – default*
1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small
Large Instance
7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large
Extra Large Instance
15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge
Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.
Micro Instance
613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro
Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.
High-Memory Extra Large Instance
17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge
High-Memory Double Extra Large Instance
34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge
High-Memory Quadruple Extra Large Instance
68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge
Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.
High-CPU Medium Instance
1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium
High-CPU Extra Large Instance
7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge
Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.
Cluster Compute Quadruple Extra Large Instance
23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge
Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.
Cluster GPU Quadruple Extra Large Instance
22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge
To get started using Red Hat Enterprise Linux on Amazon EC2, perform the following steps:
The AWS Management Console is an easy tool to start and manage your instances. If you are looking for more details on launching an instance, a quick video tutorial on how to use Amazon EC2 with the AWS Management Console can be found here .
A full list of Red Hat Enterprise Linux AMIs can be found in the AWS AMI Catalog.
All customers running Red Hat Enterprise Linux on EC2 will receive access to repository updates from Red Hat. Moreover, AWS Premium support customers can contact AWS to get access to a support structure from both Amazon and Red Hat.
Red Hat, the world’s leading open source solutions provider, is headquartered in Raleigh, NC with over 50 satellite offices spanning the globe. Red Hat provides high-quality, low-cost technology with its operating system platform, Red Hat Enterprise Linux, together with applications, management and Services Oriented Architecture (SOA) solutions, including the JBoss Enterprise Middleware Suite. Red Hat also offers support, training and consulting services to its customers worldwide.
also from Revolution Analytics- in case you want to #rstats in the cloud and thus kill all that talk of RAM dependency, slow R than other softwares (just increase the RAM above in the instances to keep it simple)
,or Revolution not being open enough
http://www.revolutionanalytics.com/downloads/gpl-sources.php
Revolution Analytics uses an Open-Core Licensing model. We provide open- source R bundled with proprietary modules from Revolution Analytics that provide additional functionality for our users. Open-source R is distributed under the GNU Public License (version 2), and we make our software available under a commercial license.
Revolution Analytics respects the importance of open source licenses and has contributed code to the open source R project and will continue to do so. We have carefully reviewed our compliance with GPLv2 and have worked with Mark Radcliffe of DLA Piper, the outside General Legal Counsel of the Open Source Initiative, to ensure that we fully comply with the obligations of the GPLv2.
For our Revolution R distribution, we may make some minor modifications to the R sources (the ChangeLog file lists all changes made). You can download these modified sources of open-source R under the terms of the GPLv2, using either the links below or those in the email sent to you when you download a specific version of Revolution R.
| Product | Version | Platform | Modified R Sources |
| Revolution R Community | 3.2 | Windows | R 2.10.1 |
| Revolution R Community | 3.2 | MacOS | R 2.10.1 |
| Revolution R Enterprise | 3.1.1 | RHEL | R 2.9.2 |
| Revolution R Enterprise | 4.0 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.0.1 | RHEL | R 2.11.1 |
| Revolution R Enterprise | 4.1.0 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.2 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.2 | RHEL | R 2.11.1 |
| Revolution R Enterprise | 4.3 | Windows & RHEL | R 2.12.2 |
so you think u like R, huh. Well it is time to pay it forward.
Message from a dear R blogger, Tal G from Tel Aviv (creator of R-bloggers.com and SAS-X.com)
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Calling R lovers and bloggers – to work together on “The R Programming wikibook”
Posted: 20 Jun 2011 07:05 AM PDT
This post is a call for both R community members and R-bloggers, to come and help make The R Programming wikibook be amazing:
Dear R community member – please consider giving a visit to The R Programming wikibook. If you wish to contribute your knowledge and editing skills to the project, then you could learn how to write in wiki-markup here, and how to edit a wikibook here (you can even use R syntax highlighting in the wikibook). You could take information into the site from the (soon to be) growing list of available R resources for harvesting.
Dear R blogger, you can help The R Programming wikibook by doing the following:
Write to your readers about the project and invite them to join.
Add your blog’s R content as an available resource for other editors to use for the wikibook. Here is how to do that:
First, make a clear indication on your blog that your content is licensed under cc-by-sa copyrights (*see what it means at the end of the post). You can do this by adding it to the footer of your blog, or by writing a post that clearly states that this is the case (what a great opportunity to write to your readers about the project…).
Next, go and add a link, to where all of your R content is located on your site, to the resource page (also with a link to the license post, if you wrote one). For example, since I write about other things besides R, I would give a link to my R category page, and will also give a link to this post. If you do not know how to add it to the wiki, just e-mail me about it (tal.galili@gmail.com).
If you are an R blogger, besides living up to the spirit of the R community, you will benefit from joining this project in that every time someone will use your content on the wikibook, they will add your post as a resource. In the long run, this is likely to help visitors of the site get to know about you and strengthen your site’s SEO ranking. Which reminds me, if you write about this, I always appreciate a link back to my blog
* Having a cc-by-sa copyrights means that you will agree that anyone may copy, distribute, display, and make derivative works based on your content, only if they give the author (you) the credits in the manner specified by you. And also that the user may distribute derivative works only under a license identical to the license that governs the original work.
———-
Three more points:
1) This post is a result of being contacted by Paul (a.k.a: PAC2), asking if I could help promote “The R Programming wikibook” among R-bloggers and their readers. Paul has made many contributions to the book so far. So thank you Paul for both reaching out and helping all of us with your work on this free open source project.
2) I should also mention that the R wiki exists and is open for contribution. And naturally, every thing that will help the R wikibook will help the R wiki as well.
3) Copyright notice: I hereby release all of the writing material content that is categoriesed in the R category page, under the cc-by-sa copyrights (date: 20.06.2011). Now it’s your turn!
———-
List of R bloggers who have joined: (This list will get updated as this “group writing” project will progress)
R-statistics blog (that’s Tal…)
Decisionstats.com (That’s me)
……………………………………………………………………………….
3) Copyright notice: I hereby release all of the writing material content of this website, under the cc-by-sa copyrights (date: 21.06.2011). Now it’s your turn!
https://decisionstats.com/privacy-3/
Content Licensing-
This website has all content licensed under
http://creativecommons.org/licenses/by-sa/3.0/
You are free:
to Share — to copy, distribute and transmit the work
to Remix — to adapt the work
From the nice shiny blog at http://blog.rstudio.org/, a shiny new upgraded software (and I used the Cobalt theme)–this is nice!
awesome coding!!!
I have been pondering on this seemingly logical paradox for some time now-
1) Why are open source solutions considered technically better but not customer friendly.
2) Why do startups and app creators in social media or mobile get much more press coverage than
profitable startups in enterprise software.
3) How does tech journalism differ in covering open source projects in enterprise versus retail software.
4) What are the hidden rules of the game of enterprise software.
Some observations-
1) Open source companies often focus much more on technical community management and crowd sourcing code. Traditional software companies focus much more on managing the marketing community of customers and influencers. Accordingly the balance of power is skewed in favor of techies and R and D in open source companies, and in favor of marketing and analyst relations in traditional software companies.
Traditional companies also spend much more on hiring top notch press release/public relationship agencies, while open source companies are both financially and sometimes ideologically opposed to older methods of marketing software. The reverse of this is you are much more likely to see Videos and Tutorials by an open source company than a traditional company. You can compare the websites of Cloudera, DataStax, Hadapt ,Appistry and Mapr and contrast that with Teradata or Oracle (which has a much bigger and much more different marketing strategy.
Social media for marketing is also more efficiently utilized by smaller companies (open source) while bigger companies continue to pay influential analysts for expensive white papers that help present the brand.
Lack of budgets is a major factor that limits access to influential marketing for open source companies particularly in enterprise software.
2 and 3) Retail software is priced at 2-100$ and sells by volume. Accordingly technology coverage of these software is based on volume.
Enterprise software is much more expensively priced and has much more discreet volume or sales points. Accordingly the technology coverage of enterprise software is more discreet, in terms of a white paper coming every quarter, a webinar every month and a press release every week. Retail software is covered non stop , but these journalists typically do not charge for “briefings”.
Journalists covering retail software generally earn money by ads or hosting conferences. So they have an interest in covering new stuff or interesting disruptive stuff. Journalists or analysts covering enterprise software generally earn money by white papers, webinars, attending than hosting conferences, writing books. They thus have a much stronger economic incentive to cover existing landscape and technologies than smaller startups.
4) What are the hidden rules of the game of enterprise software.
and the following with slight changes from a comment I made on a fellow blog-
I have been pondering on this seemingly logical paradox for some time now-
1) Why are open source solutions considered technically better but not customer friendly.
2) Why do startups and app creators in social media or mobile get much more press coverage than
profitable startups in enterprise software.
3) How does tech journalism differ in covering open source projects in enterprise versus retail software.
4) What are the hidden rules of the game of enterprise software.
Some observations-
1) Open source companies often focus much more on technical community management and crowd sourcing code. Traditional software companies focus much more on managing the marketing community of customers and influencers. Accordingly the balance of power is skewed in favor of techies and R and D in open source companies, and in favor of marketing and analyst relations in traditional software companies.
Traditional companies also spend much more on hiring top notch press release/public relationship agencies, while open source companies are both financially and sometimes ideologically opposed to older methods of marketing software. The reverse of this is you are much more likely to see Videos and Tutorials by an open source company than a traditional company. You can compare the websites of Cloudera, DataStax, Hadapt ,Appistry and Mapr and contrast that with Teradata or Oracle (which has a much bigger and much more different marketing strategy.
Social media for marketing is also more efficiently utilized by smaller companies (open source) while bigger companies continue to pay influential analysts for expensive white papers that help present the brand.
Lack of budgets is a major factor that limits access to influential marketing for open source companies particularly in enterprise software.
2 and 3) Retail software is priced at 2-100$ and sells by volume. Accordingly technology coverage of these software is based on volume.
Enterprise software is much more expensively priced and has much more discreet volume or sales points. Accordingly the technology coverage of enterprise software is more discreet, in terms of a white paper coming every quarter, a webinar every month and a press release every week. Retail software is covered non stop , but these journalists typically do not charge for “briefings”.
Journalists covering retail software generally earn money by ads or hosting conferences. So they have an interest in covering new stuff or interesting disruptive stuff. Journalists or analysts covering enterprise software generally earn money by white papers, webinars, attending than hosting conferences, writing books. They thus have a much stronger economic incentive to cover existing landscape and technologies than smaller startups.
4) What are the hidden rules of the game of enterprise software.
and the following with slight changes from a comment I made on a fellow blog-
Here is an announcement from Predictive Analytics World, the worlds largest vendor neutral conference dedicated to Predictive Analytics alone. Decisionstats has been a blog partner of PAWCON since inception. This is cool stuff!
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DECISION STATS 