Prakash Sukumar is a Principal Consultant at iOLAP, Inc., and specializes in Big Data Architecture. He has had many years of experience architecting Big Data Platforms and Data Warehouses. He has worked in various roles including architect, leading teams, administrator, analyst and developer. Prakash has special interest in emerging technologies and is always looking for new and promising methods and technologies that help businesses perform better.

We were recently asked by a customer to assist with getting their Cloudera environment spun up on Azure. While this has been accomplished several times, we had some unique challenges to solve due to security requirements. This post will cover the major pre-requisites and challenges we faced along the way.

We had Cloudera and Microsoft professional services work with us as we performed the installation for the client.

There have been several advancements within the Hadoop world that have positioned Hadoop closer to the data warehousing community than ever before. With a series of Hadoop 2.0 releases starting in October 2013, Hadoop is now much closer to being a platform for a data warehouse.

MicroStrategy prefers de-normalized snowflake schemas, but this does not mean it is always necessary to “snowflake” your dimensions. MicroStrategy architecture can work with a star schema as well. This article coves various design considerations for MicroStrategy that includes techniques for certain situations to make MicroStrategy perform as well with a star schema as it does with snowflake schema.

Every snowflake/star schema requirement specific to MicroStrategy is outlined and explained below. In addition, most of these techniques are the preferred way for any Data Mart, regardless of the reporting tool you use.

IBM released Hadoop-based InfoSphere BigInsights in May 2013. There are already Hadoop-based commercial distributions from other vendors such as Cloudera, HortonWorks and MapR. So it was interesting to learn how IBM stacks up against other vendors in the Big Data landscape. I learned more about this because I had the opportunity to get hands-on with the InfoSphere BigInsights Big Data ecosystem the week of October 7, at an IBM boot camp.

Every successful technology goes through several cycles of invention, discovery, socialization, adoption and continuous improvement. Hadoop is no exception. It has been embraced by early adopters and is now in the “discovery path” for other customers and vendors. The adoption is well supported by third party vendors who have customized and extended their product offerings with their own Hadoop distributions and implementation to help customers adopt the new technology.