Monthly Archives: September 2016

Amazon RDS Options: Ample Choices For Every Database Scenario

By | September 21, 2016

With scores of different services, AWS can be daunting to new users. Indeed, it exemplifies the paradox of choice, namely that having more options and degrees of freedom creates complexity that makes it harder to make a decision. Nowhere is this more apparent than in the choice of databases. AWS has six database products, including two different relational database services, not to mention its Hadoop EMR, Kinesis Streams and S3 object storage services. As if that isn’t enough, Amazon RDS, its primary relational database product, supports six database engines. To help AWS users decide which to use, we’ll detail the options, identify key features and differences and recommend some usage scenarios.

In this article I detail the RDS options,Β including several based on MySQL along with mainstream enterprise databases from Microsoft and Oracle. The article highlights important features and drawbacks of each along with a price comparison. When analyzing the choice of RDS engine, the decision entirely hinges on application requirements. Those porting legacy systems running on Oracle or SQL Server will undoubtedly want to use the same platform on Amazon. In contrast, developers building new, cloud-first applications should opt for one of the open source databases unless they have specific needs that can’t be met. Among these, Aurora is the strongest choice since it has more features, auto-scaling which eliminates costly over-provisioning and multi-AZ, four-9’s reliability. Read on for all the details.

Bimodal IT Can Help Turn Raw Data Into Information Assets

By | September 20, 2016

The combination of an accelerating rate if data generation and drastic changes in IT infrastructure, software and development environments leaves most organizations unable to keep up with, much less exploit new technologies while also maintaining critical IT business processes. Indeed, an organization’s IT legacy can leave it at a disadvantage to nimble, disruptive new competitors building digital businesses around cloud services and using agile, DevOps development processes. One approach IT organizations can take to resolve the paradox of trying to be both a business utility and technology innovator is through what’s popularly known as Bimodal IT.

As I detail in this post, there is a growing concensus among business leaders and IT analysts that mining the vast and growing quantities of data accumulating in most organizations will become a significant competitive advantage for those that can do it effectively. An important part of this transformation of data to information will be having the proper organizational structure and here, bimodal IT could pay dividends. Although often associated with agile, mode 2 DevOps teams using public cloud services to build new products,  bimodal is increasing appropriate for data-related activities as organizations seek novel ways to exploit the flood of business information that often doubles in volume every couple years.

Follow the link to learn how the learning curve for big data and analytics software like Hadoop, Spark, R, Kafka and Storm and the complexity of building and deploying the distributed systems to run them mean that Mode 2 teams are the ideal vehicle for introducing data analytics to an organization, building aggregated data repositories and experimenting with new analysis techniques.