Big data is leading the market today and Hadoop is the most concrete technology behind this trend.
Most companies have started experimenting with Hadoop and building applications to transform their businesses in real. However, when hadoop applications fail to cater desired expectations, it becomes costly failure. To get successful application, you need to look at the promises of big data analytics that will tell you the way to avoid costly, disillusioning failure.
# Short supply of data scientists
Data scientists are the people who possess great talent to bear complex statistical analysis techniques, programming skills, business insight, incredible innovative issue solving capabilities, and cognitive psychology. However, the supply of these people is low and thus, companies have less resources to handle hadoop based applications services.
Acquiring or developing capability of data science is a significant factor in a big data project.
# Shortage of big data tools
Shortcoming of big data tools is the major reason behind the data scientist talent gap. They need more effective analysis framework and toolkit, not what at present is offered by Hadoop and its ecosystem. These tools are in the wish list of data scientists as they can make a wide audience reach with these tools.
# Low data quality
Hadoop as the basis for several big data projects gets success not just because of its capacity to store and process large quantities of data in economic way, but it can also accept any form of data. However, this approach involves various risk factors- automatic generated data might be changing structure instantly and after long time when you come for data mining, you may find it difficult to determine its structure.
You need to pay attention to the format and quality of data streaming inside hadoop software applications. Do ensure the identification of structure is done and quality of the data is checked by you.
Aegis big data hadoop developers are posting this article to let the development community know how to get top N words frequency count via distinct articals in a sorted way using hadoop MapReduce paradigm. You can try your hands on the code shared in this post and feedback your experience later.