The Insights of Big Data Challenges
Tags: bigdata, hadoop, task, bigdatachallenges
Published: 2021-03-22 06:08:02

Do You know??

We perform 40,000 search queries every second on Google, which makes it 3.5 searches per day and 1.2 trillion searches per year.

Facebook users send on average 31.25 million messages and view 2.77 million videos every minute. Facebook generates 4 petabytes of data per day — that’s a million gigabytes.

Google gets over 3.5 billion searches daily. 294 billion emails are sent.

500 million tweets are sent On Twitter.

65 billion messages are sent on Whats App. And Many More is mentioned in the Below link.

https://www.visualcapitalist.com/every-minute-internet-2020/

All these are facts and there are numerous examples to understand that how big the Data is stored by Huge Companies. This is because of the users spending most of their time on Social media.

BigData Challenges
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Let Us understand What The BigData Is?

The term “big data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around a long time. But the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three V’s:

Volume: Organizations collect data from a variety of sources, including business transactions, smart (IoT) devices, industrial equipment, videos, social media and more. In the past, storing it would have been a problem — but cheaper storage on platforms like data lakes and Hadoop have eased the burden.

Velocity: With the growth in the Internet of Things, data streams in to businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time.

Variety: Data comes in all types of formats — from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.
Challenges Faced By Companies:

Handling a Large Amount of Data

There is a huge explosion in the data available. Look back a few years, and compare it with today, and you will see that there has been an exponential increase in the data that enterprises can access. They have data for everything, right from what a consumer likes, to how they react, to a particular scent, to the amazing restaurant that opened up in Italy last weekend.

This data exceeds the amount of data that can be stored and computed, as well as retrieved. The challenge is not so much the availability, but the management of this data. With statistics claiming that data would increase 6.6 times the distance between earth and moon by 2020, this is definitely a challenge.

Along with rise in unstructured data, there has also been a rise in the number of data formats. Video, audio, social media, smart device data etc. are just a few to name.

Some of the newest ways developed to manage this data are a hybrid of relational databases combined with NoSQL databases. An example of this is MongoDB, which is an inherent part of the MEAN stack. There are also distributed computing systems like Hadoop to help manage Big Data volumes.

Solution:

Well-managed metadata and well-managed big data are inseparable. Clean, well-defined metadata has a significant effect in delivering actionable business intelligence results.

2.Real-time can be Complex

When I say data, I’m not limiting this to the “stagnant” data available at common disposal. A lot of data keeps updating every second, and organizations need to be aware of that too. For instance, if a retail company wants to analyze customer behavior, real-time data from their current purchases can help. There are Data Analysis tools available for the same — Veracity and Velocity. They come with ETL engines, visualization, computation engines, frameworks and other necessary inputs.

It is important for businesses to keep themselves updated with this data, along with the “stagnant” and always available data. This will help build better insights and enhance decision-making capabilities.

However, not all organizations are able to keep up with real-time data, as they are not updated with the evolving nature of the tools and technologies needed. Currently, there are a few reliable tools, though many still lack the necessary Advancement.

Why Is Big Data Important?
![alt text](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3f4asnmjqe9biqbo0jzx.png)

The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics,you can accomplish business-related tasks such as:

Determining root causes of failures, issues and defects in near-real time.
Generating coupons at the point of sale based on the customer’s buying habits.
Recalculating entire risk portfolios in minutes.
Detecting fraudulent behavior before it affects your organization.

Importance Of Bigdata In Business:
    New revenue opportunities
    Customer Service
    Efficiency over the traditional approach.

Conclusion:

BigData technologies are evolving with the exponential rise in data availability. It is time for enterprises to embrace this trend for the better understanding of the customers, better conversions, better decision making, and so much more.

It is important for enterprises to work around these challenges and gain advantages over their competition with more reliable insights.