Git has emerged a strong contender for SCM. Previous commonly used SCM are:CVS SVN Mercurial Bazaar Perforce SourceSafe
Git was another brain-child of Linus Tovalds, and was created to support linux development. Previously, linux development used xxxx. (mercurial).
The features that make Git exceptional are:Branching Local Repository Distributed SCM Fast Small Footprint Staging Area Workflow – caters for various types of development WF. Easy to Learn Lots of Tools
Here are some updates on VMware. Also sometimes called “The Cloud”, it’s the current fad in IT infrastructure.
1) Virtualisation is going to happen whether we like it or notThis was driven initially by under-utilized servers, but ease of management and configuration has taken over as the leading reasons for virtualisation. Currently only 30% of organisational server infrastructure is in the virtualised environment. If an organisation doesn’t reach 80% virtualised, it doesn’t gets the efficiency benefits of virtual infrastructure, but ends up with large overheads managing both virtual infrastructure and traditional infrastructure. VMware hopes to push this to 50% in 2011-12 The issues with adoption are confidence levels in application-infrastructure interoperability, and security. VMware has notoriously low security, and is itself a gateway to accessing the entire virtualised infrastructure. (Search Google for “vmware hack”)
2) OverheadsVirtualisation comes with overheads. If installing Vista, or Windows 7 was not enough, virtualisation can help by adding 10-20% overhead to CPU usage. VMs also generate alot more network traffic.
3) ConfigurationVM configuration is going to be crucial as “the server” as it is spread over a VM, SAN storage, and network “bus”, and actual physical locations. So when we have slow VMs, it could be the result of alot of different factors now. A clone of VM for failover/failback scenarios can also generate alot of network traffic. So virtualisation increases network overheads.
4) The Virtual DesktopVMWare hopes to bring back thin-client computing with virtualised downloadable profiles from VM infrastructure. Personally, I think this is a shot in the dark, as the PC-era is gone, and computing is already transitioning to the fragmented plethora of thin-clients (eg mobile devices, ipads, netbooks) with profiles stored in SaaS applications. The benefits of centralized profiles is supposedly in data security, however, with SaaS, fragmenting application, platforms, I doubt the virtual desktop will make it to the enterprise before iDesktops.
5) CapabilityVMWare ESX 5 now supports upto 32 cores, and upto 1TB RAM per VM. These are called the “Big VMs” (or “Monster VMs” if you were a VMware sales person) that VMware has now released. This may support the more computationally intensive applications, but only if the virtual infrastructure has been upgraded.
6) VisibilityFrom an application development point-of-view, understanding the performance and capability of an application in the virtual infrastructure is less transparent as performance issues are less transparent. (eg. is a network, or disk bottleneck? or over-utilisation of the CPU?) Processor CPU utility within a Windows/Unix VM is not an accurate reflection of the actual processing capability available to your application. So VM infrastructure performance statistics needs to be actively shared (in real-time) with application teams. Using SPEC CPU benchmarking tools is another way to measure application-infrastructure performance. However let’s hope for an open environment with open information sharing.
7) Super-Computing / Grid ComputingAlthough there has not been any noted implementation of supercomputing in VM infrastructure, there are no reasons why this is not possible. Grid Computing, and maybe some aspects of super-computing is probably possible on VM infrastructure with the appropriate HPC software in place.
8) The Carbon FootprintThe Carbon Footprint is now the new driver for VM infrastructure. Running un-optimised / under-utilitzed servers kills the environment. If electricity prices go up by 30% in the next 2-5 years, what will organisations have to do to mitigate that?
Orange is a component based machine learning library for Python developed at Laboratory of Artificial Intelligence, Faculty of Computer and Information Science, University of Ljubljana, Slovenia.
We can compare Orange to the Trident Platform from Microsoft. The only difference is that its open source and works better.
Orange is free software; you can redistribute it and/or modify i under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or Orange is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
The Agile Director <a href=”http://theagiledirector.com/content/4-things-twitter-can-give-business-intelligence” target=”_blank”>recently commented</a> on using Social Media feeds as a form of data to give organisations insight through Business Intelligence initiatives formed on social media. This is very true. If companies realise that their businesses are built on their customers, all their internal systems should align accordingly. This is applicable to retail, property, media, communications, telcos, etc.., and the end-results are forward thinking, pro-active, customer-centric organisations. <div>
The Data Chasm represents the gap between those who realise this paradigm. It’s as fundamental as the <a href=”http://www.catb.org/~esr/writings/homesteading/” target=”_blank”>manifesto </a>of “<a href=”http://en.wikipedia.org/wiki/The_Cathedral_and_the_Bazaar” target=”_blank”>The Cathedral and the Bazaar</a>”.
Data – A large portion of the corporate future will be driven by those who have it, and those who don’t. Then its driven by those who know what to do with it, and those who don’t.
The gap between the haves and have nots is growing, where even governments, and corporations fall under the have nots.
Open data is the way forward to close the chasm. Supplying data alone is only the first step. As in economics, banking, media, supply chain, logistics, there are eco-systems of data analysts that churn out information. But yes, the common denominator across all these diverse industries is digital media. That is the key to bridge the data chasm.
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Born in 1781, <a href=”http://en.wikipedia.org/wiki/Charles_Joseph_Minard” target=”_blank”>Charles Joseph Minard</a> is noted for his “inventions” in the infomation visualisation. Some of his visualisation include: <ul> <li style=”text-align: left;”>The progress if Napoleon’s Army vs Distance vs Temperature in the Russian Campaign of 1812</li> <li style=”text-align: left;”>The Origin of Cattle destined for Paris</li> </ul> Charles was trained as a civil engineer. <a href=”http://cartographia.wordpress.com/” target=”_blank”>Cartographia</a> has a good list of Minard’s work.
One of the biggest problems of delivering value in a business intelligence project is providing insight around a dataset. Delivering insight about any particular dataset is not about successfully processing the data in question and analysing it. In today business intelligence (BI) world, the expectations are alot higher. Valuable insight is derived from co-relating a particular dataset with sometimes a very different abstract perspective/dataset.
You have a dataset on radiation levels. (thanks to fallout from nuclear powerstations). A very quick and common question that demands immediate answers would be “What is the impact of increased radiation?”. That is a very broad question, and even with skillful narrowing of the scope of the question, this question still needs to be answered. Even the basic remaining key perspectives on the question may be:Effect on population? Effect within a radius of 100km? Effect on transportation within 100km? Effect on travel? Effect on tourism? Effect on agriculture?
All these questions will require the custodians of co-related datasets to make their data available. The negotiations to acquire the data would probably take time. Followed by the data modeling, loading and analysis. The final outcomes would still be achieved, but under the strain of time and effort.
We can reduce some of this time by having open data, and configured data. Consider plug and play data. Consider being able to draw data from established datasets with minimal processing, and be able to derive results quickly. This is where Glitchdata would advocate data by convention.
The OSI Model has been around for several decades now. It remains especially relevant when extending the concepts of n-tiered application design. The application layer of the OSI model, can be expanded into:The App Presentation Layer The App Web Services Layer The App Business Logic Layer The App Database Layer
As database systems have evolved rapidly over the last decade, we see database systems providing features like foreign key enforcement, indexing, view, triggers, data transformation, fulltext indexing, spatial capabilities, and more.
The problem here that databases start getting bloated, and they no longer focus on the key value that they provide. Data storage and retrieval.
So it stands to reason why Amazons Web Services have offered SimpleDB has its key database offering for Cloud services. Of course they also offer other relational database services.
So why does Amazons prefer SimpleDB? Scalability, and lower costs/GB of data stored.
Data Warehousing (DW) is a common term used business intelligence (BI) projects and systems. The data warehouse has traditionally been the overhead, a large storeroom which aggregated and staged data from multiple sources into at single point. Analytics could then be conducted on this, and provide valuable insights for management.
Now, the problem with the data warehouse is that its huge, and expensive. The processes to populate the data warehouse consume large computing resources, and the outcomes after a lengthy project might be inaccurate or off-focus.
Within modern applications, and data analytics, we should consider analytics as part of an application’s design, performing smaller analytics projects on smaller datasets before engaging in larger ones. We should also consider incremental processing of data by actively managing data state in a similar way in which we manage application states.
This fits well with the Agile methodology.
So just like abandoned warehouse along the rivers and docks of modern cities, data warehouses will be abandoned with JIT Analytics, Agile BI, and better application designs.
Have you seen a bag full of mustard seeds. Small, little, round seeds that if you accidentally dropped a handful, the seeds scatter on the floor, and roll into hidden, tiny places. More concerning than this, is the ability of a single mustard seed to grow much larger. A bit like Katamari.
Moving data is a bit like moving people. In most organisation, people are frequently involved in the generation, the transformation, the curation, the classification, and analysis of data. And if any of these facets of data management fail, there will be trouble.
The most reliable aspect of such Herculean efforts is the truck, or platform. That is why many organisation prefer to depend on a platform instead of the myriad of parts to make a data project work.
However, most platforms do look like this truck. Rigid, low on flexibilty, and probably not customised for your organisations needs.