Hadoop is undoubtedly ruling the Big Data industry since it brings a lot of advantages to the companies. It is an open-source and Java-based program framework that is meant to handle large data sets. Hadoop is gaining a lot of hype in the Business Intelligence field, and strong reasons back it. It helps significantly to the companies by increasing the ROI by retaining and targeting the customers. It allows the organizations to figure out the customers’ pulse and what things customers like and don’t. It also lets the businesses implement the quick fix and enhance its brand value. It also allows the companies to offer a personalized experience to the selected customers to make them loyal customers.
Without a doubt, Hadoop is the best choice when it comes to handling the Big Data of every type of business. This is the reason we see the increased enrollment in the big data courses for beginners as well. However, it is not a perfect platform, like any other tool. So, in this post, let us understand the significant concerns of Hadoop implementation for Big Data. Before that, know why it is regarded as a standard platform for Big Data management.
Advantages of Hadoop in Big Data
- It allows the businesses to store a large amount of data with cloud storage, giving the higher processing speed.
- Hadoop allows the business to scale their business in a big way.
- It allows more flexibility for the data analyst to get insight with ease.
- Hadoop is an open-source framework, so anyone can use and contribute to the development of this framework.
- It protects your data and application failures.
- Hadoop comes with higher computing power.
What are the problems with Hadoop implementations?
Suitability and stability
Many businesses are using and getting the numerous benefits of Hadoop, including large enterprises and small ones. It is one of the main reasons we see the rush in youths to learn Hadoop, and they are highly supported by big data courses for beginners online.
Even the small organizations are highly benefitting from Big Data techniques using Hadoop and its ecosystems. However, it is not fit for the organization with fewer data. Hadoop’s HDFS (Hadoop Distributed File System) is incapable of processing and reading small files randomly, and it fails to give the correct insights. It is the major setback for Big Data implementation.
Since Hadoop is an open-source platform, stability is a common issue. Many developers are tried continuously to make improvements in this area. However, the problem remains the same. Hence, every company needs to use Hadoop’s latest stable version to ensure quality work. Another great way to handle this issue is by opting for third-party vendor services to take care of stability issues. It allows organizations to reduce the problem to a certain extent. But still, organizations are uncertain over the implementation of Hadoop for processing big data sets.
Security and other issues
The protection of data is one of the critical aspects of every business. Data theft and other hacking activities are common these days, and companies are looking to have a reliable tool for data processing and management. Hadoop’s security model is not well designed to handle complex applications because of the lack of encryption options for networking and storing. Due to these issues, it always comes with the risk of data being compromised. This is why many organizations are still hesitant to adopt Big Data techniques since no one wants to leak the business strategies to their competitors. Another main reason for data breach risk is the programming language used to develop Hadoop. The programs written in Hadoop are using Java, and it is vulnerable to cyber-attacks. However, if you enroll in the best big data courses for beginners online, you can know how to avoid these kinds of issues.
Hadoop’s Pig and Hive can’t be used in one another. The reason is Hive does not entertain Pig and vice the same case with Hive. Along with this, the installation of Hadoop respiratory is not a smooth process. Many times, it takes a considerable effort because of the improper act and mismanagement.
Hadoop has these many issues, but, in the current world, it is the best way to manage the data.