One of Hadoop’s hallmark strengths is its ability to process massive data volumes of nearly any type. But that strength cannot be fully utilized unless the Hadoop cluster is adequately connected to all available data sources and targets, including relational databases, files, CRM systems, social media, mainframe and so on. However, moving data in and out of Hadoop is not trivial. Moreover, with the birth of new categories of data management technologies, broadly generalized as NoSQL and NewSQL, mission-critical systems like mainframes can all too often be neglected. The fact is that at least 70 percent of the world’s transactional production applications run on mainframe platforms. The ability to process and analyze mainframe data with Hadoop could open up a wealth of opportunities by delivering deeper analytics, at lower cost, for many organizations.
Shortening the time it takes to get data into the Hadoop Distributed File System (HDFS) can be critical for many companies, such as those that must load billions of records each day. Reducing load times can also be important for organizations that plan to increase the amount and types of data they will need to load into Hadoop, as their application or business grows. Finally, pre-processing data before loading into Hadoop is vital in order to filter out noise of irrelevant data, achieve significant storage space savings and optimize performance.
The emergence of Hadoop as the de facto Big Data operating system has brought on a flurry of beliefs and expectations that are sometimes simply untrue. Organizations embarking on their Hadoop journey face multiple pitfalls that, if not proactively addressed, will lead to wasted time, runaway expenditures and performance bottlenecks. By proactively anticipating these issues and utilizing smarter tools, the full potential of Hadoop may be realized. Syncsort has identified five pitfalls that should be avoided with Hadoop.