by Angela Guess
A new article gives a thorough outline of the current states of Hadoop, Big Data, and Enterprise BI. The article begins, “Traditional enterprise data warehousing and Hadoop/Big Data are like apples and oranges – the well-known and trusted approach being challenged by a zesty newcomer… Is there room for both? How will these two very different approaches co-exist? This post is an attempt to summarize the current state of play with Hadoop, ‘Big Data’ and Enterprise BI, and what it means to existing users of enterprise business intelligence.”
The article states, “Currently, Hadoop has carved out a clear niche next to conventional systems. Hadoop is good at handling batch processing of large sets of unstructured data, reliably, and at low cost. It does, however, require scarce engineering expertise, real-time analysis is challenging, and it much less mature than traditional approaches. As a result, Hadoop is not typically being used for analyzing conventional structured data such as transaction data, customer information and call records, where traditional RDBMS tools are still better adapted.”
It adds, “To considerably over-simplify: if we consider what’s called the 3 ‘V’s of the data challenge: ‘Volume, Velocity, and Variety’ (and there’s a fourth, Validity), then traditional data warehousing is great at Volume and Velocity (especially with the new analytic architectures), while Hadoop is good at Volume and Variety.”
photo credit: rickprokosch
