Seven Big Data Challenges and How to Solve Them
                     Seven Big Data Challenges and How to Solve Them
Organizations can get stuck at the early stages of Big Data projects because 
they might  not be aware of Big Data challenges and how to solve them. We 
will take a look at  some of the most common scary Big Data challenges and 
how to solve the challenges  if we ever encounter them.
1st Big Data Challenge: Lack of In-Depth Understanding of Big Data
One of the major Big Data challenges is the lack of proper understanding of 
Big Data.  Some employees in an organization might struggle to understand 
the proper meaning of  Big Data, which can lead to such an organization 
lacking the needed Big Data initiatives
that will help with decision-making. If an organization does not have a 
sufficient  understanding of Big Data basics and the benefits it offers, 
there’s a high tendency of  their Big Data adoption projects failing.
The Solution:
The top management at every organization should adopt a Big Data culture. 
This  involves arranging Big Data workshops and training for staff at all 
levels. Employees  can also enroll in Big Data Online Course or register for Big 
Data Hadoop  Certification. This will encourage the acceptance and proper 
understanding of Big Data  at all levels, which in turn promotes change.
2nd Big Data Challenge: Difficulty in Managing Big Data Growth
Just as the name implies, Big Data can experience huge growth over a period 
of time.  When this happens, it can be quite difficult to handle the massive 
data growth. Also,  adjusting to this Big Data growth turns out to be a serious 
challenge that needs to be  addressed. From Big Data online training research, 
it is estimated that the amount of  data stored in IT data centers will continue 
to accumulate for years to come. So, figuring  out how to manage Big Data 
growth storage will become a pressing challenge that  needs to be addressed 
and solved.
The Solution:
The best way to solve a Big Data growth challenge is to learn how to manage 
large data  sets. Some techniques can be deployed such as tiering, 
deduplication, and  compression. When data is compressed, the number of 
bits in the data is reduced  which will then reduce the data size. 
Deduplication of data involves removing unwanted  data duplicates, while the 
tiering technique involves storing data in different cloud  storage tiers. A Big 
Data tool like Hadoop can be used to solve this challenge. The Big  Data 
Hadoop Certification will be useful for this issue.
3rd Big Data Challenge: Lack of Skilled Data Professionals
Big Data is a modern-day technology that requires the control of skilled data  
professionals. They include Data Analysts, Data Scientists, and Data 
Engineers. These  are certified data professionals with Big Data online 
training and certification. They
have the experience of handling sophisticated Big Data tools and they 
are in high  demand across the industry.
The Solution:
A good way to clear this roadblock is to encourage employers to focus on 
hiring skilled  data professionals. Companies can also support the 
professional development of  employees that work with data by enrolling 
them in Big Data online training. They can  also pay for a Big Data Hadoop 
Certification to help their employees remain loyal to  the course of Big Data 
culture.
4th Big Data Challenge: Confusion with Big Data Technology Selection
There is a massive variety of Big Data technologies available in the market. 
This means  it can be difficult to determine the right technology to use for a 
particular Big Data  project. Confusion might set in when differentiating the 
technology to use in storing data  or the one that will offer the fastest speed. 
Is it Spark, HBase, Cassandra, or Hadoop?  This can be a serious challenge.
The Solution:
The best way to solve this Big Data challenge is to seek professional help. 
You can hire  the services of a Big Data consultant or expert who will guide 
you on how to use the  various Big Data technologies available in the 
market. Another good strategy involves  getting in-depth knowledge of Big 
Data technologies from a Big Data online course to  understand them better.
5th Big Data Challenge: Big Data Security Breaches
One of the most daunting challenges of Big Data lies in how to secure a huge 
data set.  Many organizations focus more on gathering data, analyzing data, 
and storing data that  they leave data security for later. If data sets are not 
properly secured, hackers can  breach through and steal vital information. 
Companies and organizations can lose a lot  of money if this happens.
The Solution:
A good way to solve Big Data security challenges is to put data security first. 
Data  security is very important across every stage of Big Data projects, 
especially at the  design stages. Another effective way to solve data security 
challenges is for companies  to hire security professionals. Other steps that 
can help include Data Encryption and  Real-Time Security Monitoring.
6th Big Data Challenge: Difficulty in Data Integration from Variety of Sources  
Data can be integrated from a variety of sources but it is a task that can 
be quite  challenging. For instance, data can be integrated from sources 
like emails, customer
logs, data reports, social media platforms, and even financial reports. The 
integration of
data is vital for data analysis, so it is important to learn data integration 
for compiling  reports and data analysis.
The Solution:
The best way to solve data integration issues is to purchase the right tools. 
Several  tools can be used for data integration from a variety of sources; 
they include Microsoft  SQL, IBM InfoSphere, CloverDX, Centerprise Data 
Integrator, Xplenty, QlikView,
and Oracle Data Service Integrator. You can check out the Big Data online 
training  course to learn how to use these tools.
7th Big Data Challenge: Big Data Handling Expenses
Handling Big Data involves spending a lot of money. These expenses 
include power  supply, new hardware, and recruitment of data 
professionals. If the software is newly  developed, a lot of money will go 
into configuration and maintenance. Whether a  company is using an on-
premises data solution or a cloud-based water solution, it is  important to 
learn how to handle the costs.
The Solution:
A good way to reduce the cost of Big Data projects is to analyze company 
needs and  deploy the best Big Data solutions for the project. Applying Big 
Data on-premises  solutions, cloud-based solutions and hybrid solutions 
will go a long way to reduce the
Cost of handling Big Data in organizations. These are cost-effective 
solutions that will  help you handle data properly.
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