Big data is everywhere , although sometimes we may not immediately realize it . First thing to be believed is that most of us don't deal with large amount of data in our life except in unusual circumstance. Lacking this immediate experience, we often fail to understand both opportunities as well challenges presented by big data. There are currently a number of issues and challenges in addressing these characteristics going forward.

1. BIG DATA
Big data is everywhere , although sometimes we may not immediately
realize it . First thing to be believed is that most of us don't deal with
large amount of data in our life except in unusual circumstance. Lacking
this immediate experience, we often fail to understand both opportunities
as well challenges presented by big data. There are currently a number
of issues and challenges in addressing these characteristics going
forward.

2. BIG DATA: ISSUES
There are various issues areas that need to be addressed in dealing
with big data. Each of these represents a large set of technical
research problems in its own right.
● Privacy and Security:
● It is the most important issue which is sensitive and includes
technical, conceptual and legal significance.
● The combination of personal information of a person with external
large data sets leads to the inference of facts about that person and it
can be possible that these kinds person donot want to share their
information.
● Information regarding the users is collected and used in order to add
value to the business of the organization. This is done by creating
insights in their lives which they are unaware of.

4. Management Issues:
●
The another major problem is Management, it first occured in UK science where data
was distributed geographically and managed by multiple entities.
●
Resolving issues of access, metadata, utilization, updating, governance, and reference
(in publications)have proven to be major hindering blocks. There is no perfect big data
management solution yet.
●
This represents an important gap in the research literature on big data that needs to be
filled .
Processing Issues:
●
Assume that an exabyte of data needs to be processed in its entirety. Lets say data is
divided into blocks of 8 words, so 1 exabyte = 1K petabytes.
●
Assuming a processor expends 100 instructions on one block at 5 gigahertz, the time
required for end-to-end processing would be 20 nanoseconds. To process 1K petabytes
would require a total end-to-end processing time of roughly 635 years.
●
Thus, effective processing of exabytes of data will require extensive parallel processing
and new analytics algorithms in order to provide timely and actionable information.

5. .
Storage and Transport Issues :
● The storage which is available is not enough for storing the large amount
of data which is being produced. The most recent explosion was due to
large social media -there has been no new storage medium.
● The rising demand of the Big data on networks, storage and servers
outsourcing the data to cloud may seem an option. Uploading this large
amount of data in cloud doesn’t solve the problem.
● The transportation of data from storage point to processing point can be
avoided by processing in the storage place only and results can be
transferred or transport only that data to computation which is important.

6. CHALLANGES IN BIG DATA
Technical Challanges:
1) Fault tolerance :
●
As with the growth of technologies like cloud computing and big data it is
always seen that whenever any failure occurs the damage done should
be within acceptable threshold rather than beginning the whole task from
the scratch.
●
Fault-tolerant computing is extremely hard,involving intricate algorithms.
It is simply not possible to devise absolutely foolproof.
●
Thus the main task is to reduce the probability of failure to an
"acceptable" level.

7. 2) Scalability:
●The clock speeds have largely stalled and processors are being built
with more number of cores.
●Years ago, Data processing systems had to worry about parallelism
across nodes in a cluster but now the concern has shifted to parallelism
within a single node.
●The scalability issue of Big data has lead towards cloud computing,
which now aggregates multiple disparate workloads with varying
performance goals into very large clusters.
●This requires a high level of sharing of resources which is expensive and
also brings with it various challenges like how to run and execute various
jobs so that we can meet the goal of each workload cost effectively.

8. 3) Quality of Data:
●
Collection of huge amount of data and its storage comes at a cost. Data which
is used for decision making or for predictive analysis in business will lead in
better Results.
●
Big data basically focuses on quality of data stored rather than having very
large irrelevant data so that better results and conclusions can be formed.
●
This further extends to various questions like how it can be ensured that which
data is relevant, how much data would be enough for decision making and
whether the stored data is accurate or not to draw conclusions from it etc.
4) Heterogeneous Data:
●
Unstructured data illustrates almost every kind of data produced like social
media interactions, recorded meetings fax transfers to emails etc.
●
Structured data is always organized into highly mechanized and manageable
way. unstructured data is completely raw and unorganized.
●
Working with unstructured data is difficult and of course costly too. Conersion of
unstructured data in structured data is also not preffered.

9. To accept and adapt this new technology- Big Data, faces many challenges
and issues which need to be fix and put in place before its too late. These
challenges and issues will help the business organizations which are
moving towards Analytics to consider them right in the beginning and to find
the ways to counter them.