• January 31, 2023

The stakes of Big Data

The challenges of Big Data

Most companies don’t really understand the basics of Big Data. However, without a clear understanding, a project using Big Data may be doomed to failure. Companies can waste a lot of time and resources on tasks that’they can’t realize.

If employees don’t understand the value and challenges of Big Data or don’t want to change existing processes in the organization, they may be unwilling to do so’In the interest of its adoption, they can resist it and hinder the progress of the’company.

The challenges of Big Data today

Data volumes continue to grow, as do the possibilities of what can be done with so much raw data available. However, companies need to know exactly what they can do with this data and to what extent they can leverage it to better understand their consumers, products and services. Of the 85% of companies that use Big Data, only 37% have succeeded in data-driven analysis.

A 10% increase in the amount of data available’65 million increase in net income for the company’a company. Although Big Data offers a multitude of’It has its own set of problems. This is a new set of complex technologies, still in the nascent stage of development and evolution.

Some of the challenges of big data include lack of knowledge about the technologies used, data privacy and inadequate analytical capabilities of organizations. Many companies are also confronted with the lack of skills to deal with Big Data technologies. Few people are actually trained to work with Big Data, which then becomes an even bigger problem.

Process a large amount of data

There is a huge explosion in available data. Look back a few years and compare it to today’s’today. You will find that’There has been an exponential increase in the amount of data that companies can access. They have data for everything from consumer tastes, to reaction, to scent. This data exceeds the amount of data that can be stored and calculated, as well as retrieved.

The challenge is not so much availability, but managing them. With statistics stating that data would increase 6.6 times the distance between the Earth and the Moon by 2020, the challenge is daunting. Along with the increase in unstructured data, the number of data formats has also increased.

video, smart devices, etc’audio, social media, data from other sources’smart devices, etc. are just a few examples.

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

Netflix is a content delivery platform based on Node.js. With the’increasing content load and the complex formats available on the platform, they needed to’a stack that can handle data storage and retrieval. They were using the MEAN stack and with a relational database model, they could actually manage the data.

Real time can be complex

A lot of data is being updated every second and organizations need to be aware of this. However, not all organizations are able to keep up with the pace of 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, although many still lack the necessary sophistication.

Embracing Big Data in your organization

Megadata, which represents a huge change for a company, should be embraced first by management and then by all other entities in the company. To ensure understanding and’With the acceptance of Big Data at all levels, IT departments need to conduct numerous trainings and workshops. For the’The challenge is to make the use of Big Data even more efficient, the implementation and the use of data’The use of the new Big Data solution must be monitored and controlled.

However, management should not abuse it as it could have a negative effect.

The confusing variety of Big Data technologies

It can be easy to get lost in the variety of Big Data technologies currently available on the market. Do you need Spark or would the speeds of Hadoop MapReduce be sufficient? Is it better to store data in Cassandra or HBase? Finding the answers can be tricky.

And it’s even easier to make the wrong choice if you explore technology opportunities without a clear vision of what you need.

If you are new to Big Data, get a professional. You can hire an expert or bring in a vendor for big data consulting services. In both cases, with joint efforts, you can develop a strategy and, based on that, choose the technology stack needed. More information at https://octopeek.com/

Paying additional financial expenses

The projects of’adoption of big data leads to a lot of expenses. If you choose an on-premise solution, you will have to take into account the costs of new hardware, new employees (administrators and developers), electricity, etc. In addition, although the necessary frameworks are open-source, you will still have to pay for the development, configuration and maintenance of the new software.

If you decide to use a big cloud data solution, you’ still need to hire staff and pay for cloud services, development data solution as well as the’installation and operation’maintenance of the necessary frameworks. Plus, in both cases, you’ll need to plan for future expansions to keep Big Data growth from getting out of control. The management of your company’s portfolio depends mainly on the technological needs and objectives of your company.

For example, companies looking for flexibility benefit from the cloud. While companies with stringent security requirements go on-site to protect themselves, the.

What solution to save money

There are also hybrid solutions where parts of the data are stored and processed in a cloud and parts on site, which can also be cost effective. Using data lakes or algorithm optimizations (if done correctly) can also save money.

Data Lakes can provide inexpensive storage options for the data you don’t need’you don’t need to’analyze for the moment. Optimized algorithms can also reduce computing power consumption by 5 to 100 times. In summary, the solution is to properly analyze your needs and choose the appropriate course of action to meet the challenges of Big Data.