Consumerization is described as the trend for IT to first emerge in the Consumer space and subsequently makes its way into the enterprise world. But what exactly in the consumer world, that is making the users, demand the similar things from the enterprise IT. To understand the underlying need, we need to first examine the basic requirements of the user.
Kathy Sierra, co-creator of the Head First series of books and founder of javaranch.com, describes the hierarchy of needs from the user(s) perspective. The needs are stacked in the order of increasing engagement from the user. Starting with the basic needs of a defined functionality and its correctness, moving on to the learnability, efficiency & usability and finally culminating in intuitiveness and enchantment. Merely provision of correctly working functionality is not guarantee of the success of the application(s). The idea is to hook the user; the application needs to do something extra.
Using the hierarchy of needs as the background, we will see how the applications in the consumer and enterprise world stack up.
[ Tuesday, January 17, 2012 | | Filed under : Architect, Bigdata, Enterprise, Hadoop, Open Source, Trends]
This is my take on how the Bigdata market space will evolve in 2012 and years to come.
- Data Analytics Service – We will start seeing SaaS vendors that will start offering data analytics as a service (e.g. 1010data). These vendors will be able to pull in your unstructured data and provide you standardized reports and data services. This is very similar to the Web analytics (like Omniture) but access lot more data to provide patterns. 70-80% of the data analysis pattern will be similar across industries. These vendors will be able to provide these reports as off the shelf capabilities. For the other 20%, the web interface will allow the user to create patterns to analyze the data
[ Wednesday, January 04, 2012 | | Filed under : Architect, Java, JEE, Performance, Programming, Web Services]
Any application you pick up, there are some issues – big or small. There will be copy-paste code, mistakes, algorithms which could have better thought through. But what distinguishes an antipattern from these normal errors is that like patterns these antipatterns are recurring throughout the code base. In my recent experience in dealing with performance issues, I had observed certain recurrent themes that are undermining the overall application performance. Most of these antipatterns are well documented but it seems we do not learn from others mistakes. We need to make our own mistakes. I am recounting some of the common patterns that I observed in the recent months.