Hadoop is now a very popular open-source framework which helps in storing big data and run functionally rich applications on commodity hardware clusters. The major advantage of Hadoop is that it facilitates massive storage of data of various kinds along with enormous processing skills and functional ability to simultaneously handle limitless tasks.
Why is Hadoop popular?
- Hadoop has the ability to store and manage huge volume data quickly and accurately. As data is turning out to be the most valuable business asset and its sources and volume are constantly growing through social media and IoT, Hadoop is a commendable consideration.
- Hadoop features a distributed computing model, which can handle big data more effectively. The more nodes used for computing, more processing skills can be attained.
- Hadoop has a better fault tolerance than other existing technologies. Applications and data processing can be protected against the top known hardware failures. Even if a node or a few go down, data and jobs are redirected automatically to other active nodes to ensure that distributed computing is rolling. Multiple copies of data get stored automatically at various places.
- Hadoop is flexible when compared to the existing relational databases. There is no need to preprocess any data to store it. You can instantly store as much data as possible and also use it in many different ways as needed in the future. This pool of data can contain structured as well as unstructured data too like images, videos, or text.
- Hadoop is also highly scalable, and the users can grow their systems to handle the increasing demand for data management as an organization grows. The need is just to add more nodes to scale up. There is no much reword or administration tasks required for this process.
- Most importantly, Hadoop is low cost. As we have seen, Hadoop is an open-source framework which comes for free. The cost reduction comes from the concept of using commodity hardware to store big volume data.
Challenges in Hadoop
We have sent he advantages of using Hadoop as above; however, like any other technology, it does possess some challenges too for the users to handle.
- MapReduce programming in Hadoop is not ideal for all issues. It seems to be ideal for basic info requests and issues which can be split into independent units. However, it’s not so efficient in interactive analytical tasks.
- There is a big talent gap in Hadoop. There may be a scarcity of entry-level programmers to handle MapReduce. This is one reason why distribution providers tend to put SQL technology also on top of Hadoop, as it is easy to find SQL programmers.
- Another significant challenge related to Hadoop is with data security. However, many new technological tools are surfacing to help with this issue. Things like the Kerberos authentication protocol are proving out be a huge leap in making Hadoop more secure.
- Hadoop still doesn’t feature any easy to use tools for data management and governance, data cleansing, and metadata, etc. There also a lack of tools for data standardization and ensuring data quality.
Hadoop data security
Among all the above issues, we have to worry about the data security issues primarily. The explosion of big data has given rise to many technology tools to be used in analyzing a huge set of both structured as well as unstructured data to gain some actionable insights. However, not just with Hadoop, but all similar technologies come with the challenge of keeping the sensitive business information secure and confidential.
For a lot of organizations now using Big Data-as-a-service, Hadoop proved to be the default enterprise data platform. This however posed some new security issues as the data which is siloed once is again brought together into the data lake and remained accessible to various users. This possessed further challenges as:
- To ensure the authentication of users based on a hierarchy with access to Hadoop.
- Authorization of Hadoop users to access the data which they are authorized to.
- The need to check the history of data access for different users and compliance with regulations.
- Ensuring data protection, both while stored or at transit with enterprise-grade encryption.
Best practices for Hadoop data security
For those who are on Hadoop, it is time to audit the data safety and reacquaint with the below-mentioned security best practices for Hadoop.
1. Plan well before deployment
Data protection plans must be accomplished at the initial phase itself when it comes to Hadoop deployment. It is essential to identify the sensitive data elements before migration to Hadoop. The privacy policies of the company as well as prevailing industrial and federal regulations must be considered during the planning itself to identify and effectively mitigate compliance risks properly.
2. Ensure baseline security measures
Ensuring the basic security measures may make your task easier in coping up effectively with Hadoop data security challenges. Measures like user identification and limiting user access to various sets of sensitive data, permissions assigned and specifying restrictions, and using stronger passwords, etc. should be followed strictly.
3. Practice appropriate remediation technique
While analytic for business insights need access to real-time data as opposed to the desensitized data, there are essential remediation measures like masking or encryption. Masking offers to remediation; encryption also can be an ideal choice by offering greater flexibility. Whichever measure you take, it is essential to make sure that data protection solutions you adopt are capable of supporting both these remediation techniques.
4. Closely monitor issues and resolve on time
Even if you adopt the best possible security measures, which may ensure protection against any non-compliance issues, it is essential to have a close monitoring system in place. If any security breaches, actual or suspected, come up, it should be instantly addressed and resolved.
To develop a healthy data-security culture and ensure fully effective data security, you must revisit the procedures and policies related to Hadoop data security and updated the employees through training programs. It is also essential to monitor the employee performance and compliance towards the set guidelines and to reinforce it from time to time.