We are excited to announce that Knowledgent Informationist Aaron Cutshall will be presenting at the upcoming SQL Saturday conference happening October 6th in Orlando, Florida! Continue reading Knowledgent Informationist Speaking at SQL Saturday in Orlando
Technology time is measured in dog years. Grandfather dog always began his thoughts with, “In my day, there were only SQL-based databases and we liked it!” To be clear this was also in the days of expensive memory and disk space. No network or computer time itself was as, or more expensive than the human time required to program the computer. Today, virtually all of the component prices have dropped to the point that the key constraint is now how fast capability can be delivered to the business. Faster delivery begins with a strong foundation – at the data storage level. Continue reading Up with Data Storage Diversity
Last week Microsoft announced that it acquired Revolution Analytics to “help customers find big data value with advanced statistical analysis.”
In my view, this is an excellent move. R is one of the most popular software packages around. It’s very much a part of the Big Data phenomenon, used heavily for machine learning and predictive analytics. Continue reading What Microsoft’s Acquisition of Revolution Analytics Means for Big Data
As discussed in my previous blog, data in Hadoop is mainly accessed via programming languages such as MapReduce or Python, a scripting language like Pig, or an SQL-like language (Hive). However, the skillset for data analysis most prevalent in IT shops is SQL, which means that Hadoop will have to support a SQL interface in some capacity to appeal to these people and to the widespread BI tools in existence. However, Hadoop was created to process data in a “batch” mode – you submit jobs to analyze massive datasets stored in HDFS. A number of initiatives and solutions are underway that are focused on marrying SQL with Hadoop. This convergence is truly in progress.
A number of people I have spoken to have a hard time differentiating between various solutions for traditional data warehousing and the upcoming big data solutions. At Knowledgent, we have a very simple way of looking at them.
More organizations are turning to Hadoop for their Big Data analytics needs, specifically storing and processing large datasets. Hadoop and its collection of components – HDFS, MapReduce, Pig, Hive, Zookeeper, etc. – provide a platform to store a large quantity of data and batch process this data for analysis. Continue reading Top 10 questions for choosing the best SQL-on-Hadoop solution