Arguably, the most successful legacy mainframe technology is the COBOL programming language. For many organizations, COBOL systems remain as the lifeblood of day-to-day operations; however, increasing support costs, lack of application flexibility along with the shrinking pool of available COBOL resources has led most organizations to seek more modern technology.
Extract, Transform, Load (ETL) - extracting data from transaction or operations computers to load into analytical systems - has been the way business analysts have worked with data to provide insights, trend analysis and strategic advice to executives. This multi-step path to insight is under pressure.
How can enterprise IT leaders scan the enormous amounts of information without experiencing the decline in performance in relational database servers? Mostly, this decline in performance is due to legacy databases’ tendency to preserve a relational structure, or attain normalization. Here’s where “The Elephant” enters the picture. Enterprises focusing on Hadoop should not be gravitating to it as a solution for data storage issues, but rather, to overcome slow processing times.
Hadoop and big data have been all over the press lately. With all the hype, Hadoop does look like an attractive low cost solution that your company could leverage.