Hadoop is supposed to be a programming framework which comes free and is Java-based. The framework is effective in not only storage but even processing of big data in a truly computing setting which is distributed. It is very much grounded on Apache Project and is sponsored by the well-known Apache Software Foundation. Hadoop necessarily uses free license for supporting data-intensive distributed apps. Machines are not expected to share memory or disk because the architecture itself is distributed.
Several firms have incorporated Hadoop into their IT infrastructure. Adoption of Hadoop is no big deal for experienced and proficient big data managers with robust engineering teams. Adoption could be regarded as pretty easy and simple just like choosing a technology stack, designing a specific target system, and initiating implementation. However, Hadoop beginners could face few issues and challenges. Keep these in mind while managing Hadoop database.
Choosing the Right Vendors
There are a huge number of vendors and distributions that are available. You have Apache, MapR and Hortonworks to name just a few. You must never forget the fact that very few firms are capable of using the original form of Hadoop deployment precisely in their production set-up, as it is, implying without any sort of modifications. Often highly-experienced Hadoop users could face a problem in choosing the right distribution. Different vendors are known to have different components and configuration managers.
Checking Out the SQL on Hadoop
Hadoop databases are known to store huge amounts of data. Apart from utilizing predetermined pipelines to enable data processing, companies are trying to extract more value from the data by allowing interactive data access directly to their data science and business analysis teams. This is even expected from all marketing buzzes online, asking Enterprise Data Warehouses clearly to boost and intensify their competitiveness.
There are numerous frameworks that offer interactive SQL precisely for Hadoop. Choosing the best framework could prove to be slightly challenging. Sometimes, replacing the traditional OLAP databases could be difficult despite so many strategic advantages. There are however, some highly debatable and controversial shortcomings relating to support simplicity, SQL compliance and performance.
Ensuring Big Data Engineers’ Availability
Every IT firm should employ a robust team of engineers. Big data management necessitates a competent team of engineers. Remote DBA experts are known to provide efficient outsourced data management and they should not depend on outdated engineers who are not in sync with the current times irrespective of the fact that they are good at Python, C++, Java and the like. You should employ people with specific experience and expertise in big data’s technological stacks. You must seek assistance from a team of proficient developers who would be keeping the system valuable and simple in future.
Securing Hadoop Environments
Hadoop is increasingly being utilized for storage of super-sensitive data, leading to a few technical problems regarding compliance. If only HDFS and MapReduce are utilized, the situation seems to be certainly simpler because both of them would be including in-motion and at-rest data encryption, file system permissions precisely for authorization and Kerberos for authentication.
But if they use any other framework, especially those that maybe using their very own system user for the purpose of request execution, there could be some issues. To start with, these frameworks may not be having Kerberos capability thus, coming up with a loophole. This could prove to be a problem particularly if there are some requests for submission outside the cluster.
These are just a few possible challenges faced at the time of Hadoop deployment but these should in no way discourage or scare off a newcomer from this revolutionary NoSQL technology. There are loads of advantages to be enjoyed by the effective deployment of Hadoop into a company.