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| Hadoop Batch |
Hadoop being implemented in almost all
sectors is a clear message that it is capable of adapting into any industry’s
environment. Although Hadoop has innumerable benefits, in order to apply it to
a specific problem calls for due diligence. Below given are some scenarios in
which big data helps financing sectors.Professionals from finance background can also learn this technology.Madrid Software Trainings provides complete practical Hadoop Training in Noida and in other parts of India.
Fraud Exposure: Hadoop efficiently takes care of common industry challenges such as
financial crimes, fraud and data violations efficiently. By analyzing point of
sale, transactions, authorizations and other factors, banks can find out and
prevent fraud.
Big data is also helpful in finding out
unusual patterns and informing the banks of the same, while cutting back the
time and resources needed to do so.
Data Storage and Security: Safety, easy storage and access of financial information are the
most important necessities of financial institutions. While HDFS (Hadoop
distributed file system) offers scalable and reliable data storage made to span
large clusters of commodity servers, each node is processed by the MapReduce in
parallel manner, transferring only the package code for that particular node.
This simply means that information is stored in various clusters but with
additional protection for a better and safer data storage facility.
Risk Management: Big data solutions can help in risk management as well to financial
sectors. Hadoop avails an accurate view of rick as well as impact, making firms
to take informed decisions by evaluating transactional data to decide risk
based on market behavior, potential clients and scoring customers.
Analysis: Banks
and financial institutions have to analyse unstructured data stored in various
channels like emails, social media profiles, discussion forums, complaint logs
and so on. Not just these but from other sources too like cash and equity,
transactional data, trade and lending and so on for better understanding of
their customers. Hadoop enables financial sectors to access and evaluate this
data and also offers accurate insights for right decision making prowess.
Not just in baking, but Hadoop has made its
presence be felt at other departments too like experience analysis and customer
segmentation, targeted services, credit risk assessment and so on.
Does Hadoop have any limitations?
Besides being adopted by several banking
firms, there are several factors that point it that Hadoop may not always be
the right solution.
Understanding of Big Data: Hadoop comes into view when big data is to be applied. But prior to
getting along with it, one must ask some questions and think whether it’s the
right solution. Any firm that has a massive inflow of data from different
channels and that which is experiencing storage issues along with the effective
use of existing data can make use of these two solutions (big data and Hadoop) for
their organization.
It’s Not a unique service: Hadoop facilitates analysis; however, there are many products that
enable you for the same. Hence, using Hadoop for the purpose of analysis,
implementing framework just to address the issues isn’t a smart idea. It is
beneficial only if one gets more than one situations where its USPs can be
utilized properly.
It is not a solution, but a tool: Remember Hadoop isn’t a complete solution, although it can help
effectively in fraud detection and risk management. But it doesn’t solve the
issue by itself, it needs programmers to write the codes with an understating
of the problem so that Hadoop’s potentiality could be utilized to the optimum
to solve the business problem.
Other vulnerabilities include: If we talk about any other technology, then we find that Hadoop
isn’t a fullproof as well. Encryption isn’t found at storage and network level
in Hadoop, hence the data remains at risk. Also, it creates duplicates to store
data, it is vulnerable to data violations as well.
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