by Angela Guess
According to a new article, Richard Winter sees two major trends in the realm of Big Data: “First, traditional data warehouse vendors are investing in scalability improvements to better accommodate growing volumes of transaction data. And second, new open source technologies such as Hadoop, MapReduce and NoSQL databases are emerging for use primarily as data warehouse alternatives in tackling other forms of big data — Web activity logs and sensor data, for example.”
Winter says, “When you have very large volumes of data to manage and analyze, a data warehouse can look like a very expensive solution.” The article continues, “That isn’t necessarily a valid perception when it comes to transaction data: He noted that in general, data warehouse technology has demonstrated a strong return on investment for uses in which data is highly structured, tightly managed and used widely within an organization on an ongoing basis.”
It adds, “But the economics of a Hadoop-style approach to big-data management can be better in certain kinds of use cases, Winter said. For example, scientific research processes can produce enormous volumes of data — about 15 petabytes of raw sensor data annually in the case of the Large Hadron Collider, located near Geneva and designed for use in high-energy physics experiments. Dealing with such information is the kind of challenge for which Hadoop is well suited, Winter said.”
