Creator of WebSphere: WebSphere was the biggest technology mistake I ever made


@BBC: What’s the biggest technology mistake you ever made – either at work or in your own life? 

Dr Donald FergusonCreator of WebSphere: When I was at IBM, I started a product called Websphere [which helps companies to operate and integrate business applications across multiple computing platforms]. Because I had come from working on big mission-critical systems, I thought it needs to be scalable, reliable, have a single point of control … I tried to build something like a mainframe, a system that was capable of doing anything, that would be able to do what might be needed in five years. I call it the endgame fallacy. It was too complex for people to master. I overdesigned it. Because we were IBM, we survived it, but if we’d been a start-up, we’d have gone to the wall.

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Transaction strategies in WebSphere Process Server Microflows


Transactional and non-transactional services

A service call is said to participate in the navigation (process) transaction, if the service call commits (or rolls back) when the navigation transaction commits (or rolls back). We are then talking about a transactional service. A service with a transaction behavior that is independent of the navigation transaction’s outcome is a non-transactional service.

Whether a service is transactional or not depends on:

  •  Transaction management during the service call
  •  Interaction style of the service call.


Transaction management during the service call

The transaction management of the service call is determined by SCA quality of service qualifiers that are set for the process interface and the reference of service to be invoked. These qualifiers are:

  • Suspend transaction: Suspend transaction is a qualifier on the reference of the process component. It defines whether the calling component is disposed to share the transaction context with the called service.
  • Asynchronous invocation: Asynchronous invocation is a qualifier on the reference of the process component. It determines if an asynchronous invocation should occur as part of the client transaction or not.
  • Join transaction: Join transaction is an interface qualifier of the service to be invoked. It states whether the called service is willing to share its transaction with the calling component.

Interaction style of the service call

The interaction style can be either synchronous or asynchronous. A service call from a microflow is typically synchronous. The only exception is a service with a one-way interface that is declared to be called asynchronously.


Transactional services

A service is transactional in the following two constellations:

  • The service is called synchronously, and the process component and the service component express to share the transaction, for example, the reference qualifier suspend transaction of the process is set to false and the interface qualifier join transaction of the service is set to true. The transaction boundary for this case is illustrated in following Figure.

                                      Synchronous service joining the transaction of the microflow

  • The service is called asynchronously and the actual service call happens upon commit of the microflow’s transaction; the service call is however not performed if the transaction of the microflow rolls back. This is the case, when the reference qualifier asynchronous invocation of the microflow is set to commit.

Experiencing the WebSphere Cloud: WebSphere CloudBurst Appliance


IBM® WebSphere® Emerging Technologies Evangelist Dustin Amrhein uses the WebSphere CloudBurst Appliance to illuminate the effort that goes into constructing a showcase enterprise application environment that can host various client sessions and can be reconfigured on demand to support many different instances of those client sessions.

Weather report: Build a reconstructable application showcase

Transaction strategies: Understanding transaction pitfalls


Transactions improve the quality, integrity, and consistency of your data and make your applications more robust. Implementation of successful transaction processing in Java applications is not a trivial exercise, and it’s about design as much as about coding.

In this series, Mark Richards is your guide to designing an effective transaction strategy for use cases ranging from simple applications to high-performance transaction processing. Using code examples from the Spring Framework and the Enterprise JavaBeans (EJB) 3.0 specification, series author Mark Richards explains these all-too-common mistakes.

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Big Data, Small Startups: One Angle On Turning Data Into Money


Thanks to commodity computing power, it’s possible to build a startup business based around big data and analytics. But what does it take to do this, and how can you make money? 

It’s no surprise that in the time since Varian’s opining (2008), we’ve seen oodles of small startups setting their sights to capitalize on Big Data. And now, we’re learning from their failures. But Big Data doesn’t need to be the place bright startups go to die. After a number of startup breakups with El Data Grande, Pete Warden came up with a tangible analysis of what the path is from stacks to riches.How To Turn Data Into Money is one way to approach a complex topic in a landscape of changing tools, and it’s well worth a look. He describes the process of identifying how to make data turn a profit. Warden reinforces the notion that we’re still in the early days of really knowing where the ‘big wins’ are with Big Data.

The overall issue is this: From the outlay, many startups are going to be sitting on a large bucket of data but won’t be in a position to imediately know where the monetization sweet spot lies. As Warden suggests, they will have to go through a series of processes that enables them to zero in on how to provide the maximum amount of value by iterating in partnership with their customers/users.

The first step might be to summarize the data and provide simple graphs. This allows everyone, your customers and your own team, to really understand what the data might show.

As feedback is obtained from this initial process, key metrics and other indicators can be focused on in reports. This will begin to allow you to answer specific questions that will (hopefully) be of value to your customers.

It’s no surprise that your customers, once identified, are going to be where you go for answers to their needs. Iterating your business in response to working with your customers – which is always valuable no matter what vertical you are in – will ultimately bring you to a point where you can provide business intelligence and actionable recommendations for your customers based on what they are already doing with the data.

Being able to point out specific trends, suggestions and points of friction (contextual to your data’s domain) should be of great value and something your current and future customers will be willing to pay for.

Finally, Warden touches on how your shiny data should be presented – dashboards are commonplace, but clearly we’re in very early days in this space. What’s important to remember is that what’s being built here is a combination of product and consultation. If you need an initial framework to begin tackling the problem Warden’s post works as a great framework, but given that Big Data could be about anything, you will need to consider your domain space, the nature of the data and your own expertise to be able to know whether this will work for you.

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About OPTinity eSolutions


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About


Emerging trends in technology, disruptive innovations and new developments in IT & social computing industry will affect the way we live. Trough this blog i’ll select emerging and innovative tech  and share my thoughts and experience in enterprises mostly but not only in following areas:

  • JEE & .NET
  • Web 2.0: Enterprise Mashups, Social Networks, IBM Mashup Center, Lotus Quickr, Google OpenSocial
  • Wb 3.0: Semantic Web & Linked Data
  • RIA – Rich Internet Applications: Ajax, Adobe Flex & AIR, RCP, GWT, Comet
  • SOA – Service Oriented Architecture : WebSphere Process Server, WebSphere ESB, ActiveMQ, Mule, ServiceMix
  • WCM: Web Content Management: IBM/Lotus WCM, Oracle UCM, Drupal, Alfresco
  • DM: Digital Document Management: Adobe LiveCycle ES
  • Application Servers: WebSphere, Weblogic, JBOss, Jetty, Apache
  • Enterprise Portal Servers: WebSphere Portal, Lotus Quickr, Oracle Portal, Liferay, exo Platform, Drupal
  • Enterprise Business Integration: WebSphere Process Server, WebSphere Integration Developer
  • ALM & CI: Application Life Cycle Automation: Rational RTC/Jazz, Maven, Cruise, Hudson, Continuum, TeamCity
  • Databases: DB2, DB2/i5, Oracle, MySQL, Derby, SQLServer
  • Open Source Web Frameworks: Spring, Hibernate, JSF, Struts2, Wicket, Seam
  • Legacy: IBM i5/iSeries Platform
  • Mobile: iPhone, Android, Meego, J2MEE, Wurlf

 

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