When you buy through affiliate links in our content, we may earn a commission at no extra cost to you. Learn The Apache open-source Hadoop data handling resource and related tools are becoming influential in the big data world. But in the race to adopt newer, more modern IT solutions, companies are asking whether Hadoop is a universal tool that should be broadly applied to big data and analytics processes.
In reality, there are several considerations for whether a system is going to benefit a lot from Hadoop implementation. One is whether big data is relevant to the industry. In other words, whether the business will run on the acquisition and analysis of extremely large data sets, data sets larger than what can be analyzed using a traditional relational database.
In addition, companies can choose between Hadoop and other proprietary tools that may require less in-house technical skill. Some other tech companies are building similar big data tools that may have more intuitive interfaces or shortcuts to allow less experienced users to do more with big data.
At the same time, there is a consensus that most big data projects can benefit from Hadoop with sufficient administration. Tools like Apache Hive warehouse design and Apache Pig programming syntax for big data are extending what Hadoop can do. Other advances, like Hadapt and MapR are making the syntax and use of Hadoop more transparent to a wider variety of users, or in other words, starting to do away with the "techiness" problem.
In general, the business has to look at how much big data it is using and where that data is coming from. Executives and leaders have to consider who is going to be working on the IT projects involved, and their skills and backgrounds. They have to understand the difference between the implementation of various big data tools. This will help leadership teams understand whether Hadoop is right for their projects.
