Cluster and cloud computing have dominated the high-performance computing arena, with many IT professionals heading to get trained with the latest technologies. Hadoop is one such hot cake that is very much in use. Owing to the massive need for Hadoop professionals, many Hadoop training institutes have come up. But despite its popularity, it is not devoid of loopholes. Hadoop failed to deliver when data integration, specialized skills and budget were clubbed together for business planning and implementation.
One such Hadoop consulting services have shared their experiences on how Hadoop has failed to deliver results despite business analytics initiatives. They have listed few mistakes and here are some hacks to avoid them.
Mistake 1: Migration without chalking out a plan
You can think of implementing Hadoop without a successful strategy in place. Migration without a full proof strategy will result in costly maintenance. Once it is implemented for the first time, you can find a steep learning curve along with error reports.
So, go for error-free and smooth implementation and it is only possible if you begin with detecting a case, followed by a study on each process right from data accumulation to data manipulation and processing till the analytics.
Mistake 2: Assuming that rational database skill-set can be transferred to Hadoop
If your lack manpower with Hadoop skills, then no need to hire new people; instead, train the existing people. You can fill the gaps in skill-set through point solutions at times. Though there are quite a few challenges when it comes to Hadoop, with the right mixture of people, functionality and dexterity, you can transform a challenging situation to a successful one.
Mistake 3: Thinking Hadoop Database to be similar to regular database
Considering the database on Hadoop to be similar to any usual database available at HP Vertica, Teradata database or Oracle is wrong. It is completely different from these. You cannot expect it to store up data or files which you can do in case of Google Drive or Dropbox.
Data exists in raw format. You may imagine it to be clean, easy to spot and clear. However, if you don’t get it right, you may end up in a data swamp. Data will be available in abundance, but deciphering it becomes a challenge.
Mistake 4: Security Concerns
Most enterprise teams shield their private and confidential data. So, if you have such data, remember that since you are processing crucial data on bank details, credit card, insurance numbers and personal information about customers, clients, and employees, security comes first. Before deploying such big data, it is important to apply best practice methods for data protection such as authentication, authorization, audit, data protection and automation to prepare and send alerts on data available in Hadoop.
So, once the business gets big data, it is important to set a strategy and plan as to who will take the benefit, what will be its impact on the infrastructure, etc.
Mistake 5: Cementing the gaps in skills with conventional ETL
Filling the gap in skills can be difficult for some organizations bearing in mind as to how to resolve ETL problems of big data. You cannot find sufficient IT professionals bearing Hadoop skills. As a result, the problem may intensify.
Manpower, expertise, and top class methods are essentials for fruitful Hadoop projects. The permanent Hadoop professional can work with the existing professional for while integrating big data. It is important to have an all-inclusive business platform designed to make the Hadoop implementations work properly.
Mistake 6: Small budget
Many organizations are using it because it is scalable at a cheaper cost. However, organizations often fail to combine data replication, proficient resources as well as the completely managing the big data.
Hadoop can process large datasets, but it requires the hands-on skills of Business Intelligence and Structured query language. So, before anything, it is important to understand how big data analytics solutions can fit into your prevailing processes and methods of data analysis.