Hadoop is an invaluable tool that allows large-scale data processing across commodity server clusters. Doug Cutting and Mike Cafarella gave a great contribution to handling big data in 2004. Hadoop works as an open source platform that allows efficient data management. In other words, this system helps companies in handling big data. It allows the user to add to or modify their data system as their needs change, using cheap and readily-available software from any IT vendor.
Hadoop’s Prominent Position in Big Data Handling
Ability To Store Huge Data
Using Hadoop software one can store big data in a shorter time span. As the data volumes and varieties constantly increasing, especially from social media and the internet, it became the key consideration to deal with them.
Hadoop's distributed computing model processes big data fast. In Hadoop, the more computing nodes user use, the better processing power user has.
Data and application processing can be protected easily against hardware failure. If one node goes down, Hadoop automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.
Traditionally databases accept the storage only after preprocessing, but now you can store as much data as you want and decide how to use it later. Basically, these include unstructured data in the form of text, images, and videos.
The open-source framework is available for free of cost and this uses commodity hardware to store large quantities of data.
Hadoop helps the business concerns by adding nodes to handle more data with little administration.
Major Concerns of Hadoop Implementation
Not Beneficial For Small Data
Nowadays, data benefits are not only strict to large business but small businesses are also getting great benefits from it. Big data in every business benefits in boosting sale figures. But Hadoop has the biggest disadvantage that it has a high capacity design that is not suitable for small business organizations. The reason behind, Hadoop Distributed File System(HDFS) is incapable of reading small files randomly so this makes it incompatible with small data. This is the biggest drawback factor in Big Data Hadoop implementation.
Hadoop setback from storing and networking feature moreover its security model is not well-designed for complex applications. Because of these inadequacies, data sets are always at risk of being hacked. Data are an invaluable asset for every organization and no organization wants its vital data to be leaked. Hadoop is also not a secured against data breach as its framework is written in Java, which is unsafe from cyber attacks viewpoint. These days, many cases of cybercriminals having exploited Java in the past make Hadoop not to be completely trusted as far as data security is concerned.
As Hadoop an open-source platform, so it is always surrounded by stability issues. Many developers have developed many models to deal with this issue but still unable to meet these criteria properly. So, It’s vital for a company to ensure that they have put into use the latest stable version of Hadoop. Another technique is to go for a third-party vendor who can take the responsibility of running it and fixing stability issues.
Problems With Pig and Hive Functionality
Hive and Pig are the two key elements of the Hadoop ecosystem. But Pig does not entertain Hive UDFs and vice versa. Both can’t be used in one another. As Pig script also does not offer any help whenever any requirement arises for extra functionality in Hive. But If you want to access Hive tables in Pig, you need to use HCatalog.
Hadoop repository installation is not an easy task. It usually takes a lot of effort because of the improper action. Another big concern with Hadoop repository is that it does not keep a check on compatibility while installing any new application. As a result, compatibility issues emerge at later steps and cause inconvenience.
Many companies use traditional techniques in Hadoop software for entire data, for the real-time analysis became impossible.
We know that the processing tools do not make use of distribute processing. Even though you have tools like SQL and Teradata, they fail to process petabytes of data. As RDBMS uses single node processing that failed to process a huge amount of data.
The traditional client-server architecture is unable to meet the challenges of real-time complex data processing, which is needed in the Big Data scenario.
How to overcome from challenges
There are still other problems with Hadoop like unrefined documentation, problems with Ambari installation and Oozie not behaving well when not properly distributed. But, to overcome from above concerns, the enterprise needs to go with Big Data Courses Toronto. Usage of few new commercial Hadoop Software tools like a cask, Talend, mica, Pentaho, bedrock, Informatica Big Data Management to also benefits the real Hadoop’s power.