Most data integration solutions offered for Hadoop do not run natively and generate hundreds of lines of code to accomplish even simple tasks. This can have a significant impact on the overall time it takes to load and process data. That’s why it’s critical to choose a data integration tool that is tightly integrated within Hadoop and can run natively within the MapReduce framework. Moreover, it’s important to consider not only the horizontal scalability inherent to Hadoop, but also the vertical scalability within each node. Remember, vertical scalability is about the processing efficiency of each node. A good example of vertical scalability is sorting, a key component of every MapReduce process (equally important is connectivity efficiency, covered in Pitfall #5). When vertical scalability is most efficient, it also delivers the fastest job processing time, thereby reducing overall time to value.
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.