NoSQL or Know SQL ?

 

I have spent the last few months exploring the Big Data landscape, discussing some of the technologies that are underpin the promise of Big Data like Hadoop and Map Reduce.

 

NoSQL is a set of capabilities that have emerged over the last few years, motivated by the fact that relational databases technologies are limited by the need to model an application up front, define a schema and operate around the schema. Proponents of NoSQL believe that this presents a significant limitation for some emerging classes of applications, particularly where the schema definition can not be determined in advance. It is important to note that NoSQL is a movement, as opposed to a specific technology. What motivates proponents of this technology is the need for SQL schema flexibility. The actual implementation varies, with proponents adopting various approaches, e.g. key store, document store, in-memory or graph oriented databases etc. 

 

Most of the clients I have interacted with understand the value of relational databases and the merits of being able to model applications and design around a specific scheme. This is how relational technologies are able to deliver scale, robustness, and transaction guarantees also referred to as the ACID properties. The dilemma clients have is how to retain the goodness of relational databases, while adopting elements of the NoSQL promise that has the potential to improve their overall effectiveness. This is where the notion of “Know SQL” comes in. In my view, it is not about the irrelevance of relational SQL based database technologies, but rather about knowing when more schema flexibility is desirable, particularly in the Big Data world.

 

Recognising this trend, IBM has been investing its relational database technologies, adding XML support into DB2 to deliver more schema flexibility, and introducing NoSQL Graph Support, also known as DB2-RDF. As a result, clients are able to achieve schema flexibility, without having to give up the proven capabilities and robustness of relational database engines. More information on DB2 NoSQL support can be found here.

What IT systems does a Smart City need?

A McKinsey Global Institute article “Urban world: Cities and the rise of the consuming class“, reflects on the speed and unprecedented scale of city expansion. This is placing anincreasing demand fornew environmentally friendly infrastructure for water and energy, buildings, transportation and communication. Migration is motivated mostly by the search for a better life, and in additional to the infrastructure challenges previously outlined, this also places a strain on the cities to deliver citizen based services such as education, health, public safety,economic development and social programs.


 Smarter city operations rely on the ability to capture data that can be used to anticipate and proactively resolve problems. Problem resolution can be achieved by coordinating processes and resources for more efficient operations. Take public safety as an example. IT systems can be deployed to predict, monitor and mitigate crisis situations. This can be achieved by automatically analyzing video streams for threats based on known criminal patterns. Similar capabilities can be applied to transportation for more effective traffic management, and for analyzing water use and consumption patterns, thereby enabling utilities to identify leakages and optimize repair jobs for improved service delivery.

 

 

The figure to the left is from  “Competing on Analytics, Davenport and Harris, 2007″. 

 

To be competitive, Smart Cities need to deploy Analytics capabilities that enable the delivery of a high quality of services that meets and exceeds citizen expectations today, and  can accommodate future needs for real-time dynamic access to innovative new services. 

 

Additionally, these new services need to be resilient, secure, compliant with local requirements, and sufficiently agile to address new risks posed by an ever more connected and collaborative world. And all of this needs to be achieved cost effectively.IBM’s PureData System for Analytics is designed specifically for this use case.

 

Imagine a situation where city operations can be collaboratively managed via Executive, City Operations and Agency dashboards that include domain key performance indicators for standard operating procedures. Such a system could support centralized planning, execution and monitoring for more efficient operations.  

 

IBM’s Intelligent Operations Centerdelivered on a Cloud enabled PureSystems platform, enables cities to not only contain operational cost and complexity, but achieve breakthrough productivity gains through rapid time to value viavirtualization, optimization, energy stewardship.