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Python and Big Data, a Current Trend

by Jyoti Saini
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The progress of Big Data over the last couple of years has meant that people are now looking to get a grasp of the concept behind it and how it can dictate the upcoming future. Having already talked about the benefits of Big Data and what makes it such a hit and a great prospect for the future, today we will be talking about the approach regarding the use of Python together with Big Data for the best results.

For readers who are not yet aware of what big data is, big data is basically a term used to refer to voluminous amounts of big data sets that are way out of the realms of traditional software used for treating data. The current challenges in big data include those related to the capturing, storage and analysis of data.

In this article, we will look at the use of Python with Big Data and the results that this merger will bring for both of them. Stay with us as we delve deeper into the use of both these technologies, with a bonus section on the cost of Big Data.

Trends for Python and Big Data

The future trends predicted for the growth of Big Data heavily rely on the usage of Python. Python is the programming language of the future and has become a favorite for brands and organizations today. Python development was previously used for web app development but has now become a fan favorite for its ease of access and the simple data extraction measures for data scientists.

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Some of the trends being driven by both Python and Big Data include;

  • Data synthesis and analysis at one time to ensure the data competence for the analytics process.
  • Self-service analytics for users and consumers present in the market.
  • Shifting from Big Data to wide data through the use of different data sets that are diverse in nature and form an amalgam of sorts.
  • Improvements in the recognition software along with better speech processing to ensure enhanced interaction with customers.
  • Emergence and widespread adoption of algorithms that are used in analytical systems and help identify data techniques and patterns for storage.
  • Using machine learning as a catalog to improve the extraction of intelligent data from different sources.
  • Using real-time analysis for crucial results and for driving processes forward.

All of these trends provide great potential for businesses in the future and will drive the extraction of results from key data sources. These trends rely on the costs of Big Data and how they will increase in the future.

Cost of Big Data

This is what everyone is wondering about right now; how to calculate the cost of a Big Data setup? We have all read about the setup and the benefits it promises to the people, but what is exciting people now is the information pertaining to the calculation of costs related to a Big Data setup. People, who calculate the cost of Big Data ownership by themselves, commit the costly mistake of not factoring for anything else besides the license needed for operations.

One common misconception behind the assumptions regarding the cost of Big Data is that the license cost upfront is an indication of what you can expect with big data over time. People who suffer from this misconception and commit this costly mistake are often let down and disappointed by the original cost once they start operating in the niche.

The Total Cost of Ownership or TCO for a Big Data setup is not just limited to the initial license cost but also includes the cost of additional manpower required for the implementation of the project, all the supporting technical infrastructure needed for managing the project effectively and additional costs such as training of the workers and customer support.

So, with upfront costs being a flawed way of approaching the costing method, the best way is to take into factor all the separate heads and prepare a cost report that is both authentic and a thorough representation of the costs you will incur during the process.

Technology

Technology is the biggest factor and perhaps the biggest contributor towards the costs that are incurred in a Big Data setup. The technology needs to be up to date and should comprise the best forms of modern innovation to make handling Big Data easy and manageable. There are two basic requirements in technology when it comes to computing the cost for the Big Data setup. The first requirement is of source data and the second is of source systems.

Source Data

Source Data is the amount of data you have currently present in non-relational systems. The size of this data will dictate how much cost you have to incur for getting the system off and running. Big Data setups usually comprise of a lot of data upfront, so you would be starting with TBs of data.

The calculations here are simple, as the more data you have, the more cost you will require getting the system running properly. The non-relational systems used for the system can include NoSQL, Hadoop, Amazon S3 and any other third party application.

Big Data is growing with time and is expected to act as a foundation for extensive research in the future. We hope you understand the role Python plays in this process and the individual costs of Big Data now.

Alternate Title:

Pairing Big Data and Python for future trends

Alternate Description:

Big Data is growing with time and is expected to boost research in the future. Stay with us, as we look at some of the trends behind this growth, and the use of Python.

Feature Image Credit: freepik

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