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Last Updated: Mar 27, 2024

Custom and Semi-Custom Applications for Big Data Analysis

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Speaker
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM

Introduction

The amount of data shared and transferred between humans with each passing second is incomprehensible. It is challenging to organise, analyse, predict, and make decisions based on that data. Companies today aim to comprehend the newest market trends, client preferences, and other requirements, which necessitates the interpretation of vast amounts of data because it is a primary asset.

 

Big Data is made up of a lot of data that isn't processed by typical data storage or processing units. Several international corporations use it to process data and conduct business. Before replication, the data flow would reach 150 exabytes per day.

Big Data Analysis Approaches

Reports and visualisations are typically used to communicate big data analysis to the end-user. Traditional reports may not be able to deliver new insights or produce the unexpected results that decision-makers are looking for. To deal with big data, data visualisation approaches would be helpful, but they will need to be complimented or assisted by more complex technologies.

 

Traditional reporting and visualisation are standard, but they are insufficient; thus, new applications and methodologies for big data analysis will be essential. Early adoption of big data necessitates the development of new applications that fulfil analysis needs and timelines. It's crucial since a common representation from standard data analysis might be insufficient.

 

These new applications will be classified as either custom (coded from the ground up) or semi-custom (based on frameworks or components). We'll now try to figure out why these tactics work and how we can utilise them to make big data more useful in our daily work life sooner rather than later.

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Custom Applications

In general, a custom application is developed for a particular or a group of connected purposes. To support unique operations or create a competitive advantage, certain parts of a business or organisation will always require a specific set of technology. The goal of custom application development for big data analysis is to reduce the time to decision or action. 

 

Traditional software manufacturers will be hesitant to bring new technologies to the market as big data advances as a science and a market. Big data architecture has little value if there are few opportunities to decide or act on due to a lack of analysis capabilities relevant to the business area. Vendors can use their technology components to assist in developing solutions for their clients. However, there is no such thing as a fully packed application that would function right out of the box for a complex big data solution.

 

One can also look for additional custom applications available in the market as per their need. Two of these additional options are as follows:

R Environment

The "R" environment is based on Bell Laboratories' "S" statistics and analysis language developed in the 1990s. It is maintained by the GNU project and licenced under the GNU General Public License. Many users of S and R have made significant contributions to the base system over the years, increasing and expanding its functionality.

 

R is a set of software tools and technology for building bespoke applications that help with data processing, calculation, analysis, and visualisation.

 

R is a platform for creating interactive big data analysis methodologies. It has grown quickly, and a significant number of packages have been added to it. It's ideal for one-off, custom applications to analyse big data sources.

Google Prediction API

The Google Prediction API is an example of a new class of big data analysis application tools on the horizon. It's available on the Google Developers website, and it's well-documented, with a variety of ways to access it using various programming languages. It's free (with certain limits) for the first six months to help users get started. Following that, licencing is relatively limited and project-based.

 

The Prediction API is straightforward. It searches for patterns and compares them to existing proscriptive, prescriptive, or other patterns. It "learns" while conducting its pattern matching.
 

Prediction is a RESTful API that supports .NET, Java, PHP, JavaScript, Python, Ruby, and many other languages. Google also provides scripts for accessing the API and an R client library.

 

Predictive analysis is one of the big data's most powerful potential capabilities, and the Google Prediction API is a great way to build custom apps.

Semi-Custom Applications

Many people mistakenly believe that custom apps are produced utilising "packaged" or third-party components such as libraries. It is not always essential to code a new application from scratch. There is no replacement when it is required. When developers or analysts use packaged apps or components, they must write code to "knit together" these components into a workable custom application. The following are some of the reasons why this is a good strategy:

  • Deployment Speed: The development time can be significantly decreased because you don't have to write every aspect of the application.
  • Stability: Using well-built, dependable third-party components can help to strengthen the bespoke application.
  • Better quality: Because packaged components are used in a wide range of contexts and domains, they are frequently held to higher quality specifications.
  • More agility: If a better component becomes available, it can be switched into the application, increasing the bespoke application's lifespan, adaptability, and usefulness.

 

Another form of semi-custom application is one that has the source code and has now been altered for a specific purpose. Because there are so many instances of application building blocks to incorporate into your semi-custom programme, this can be a time-saving strategy. Here are a few examples:

  • Technical Analysis Library (TA-Lib): The Technical Analysis library is widely used by software developers who need to conduct technical analysis on financial market data. It's open-source and licenced under the BSD licence; thus, it can be used in semi-custom applications.
  • JUNG: The Java Universal Network Graph framework is a library that provides a standard framework for analysing and visualising data that can be represented as a graph or network. It can be used for data mining, social network analysis, and importance measures (PageRank, hits). It's accessible under the BSD licence as open-source software.
  • GeoTools: It is an open-source geospatial toolkit for manipulating GIS data in various formats, analysing spatial and non-spatial properties, and building graphs and networks out of the data. It's free and open-source, with the GPL2 licence allowing it to be integrated into semi-custom programmes.

 

Because of the speed and variety of big data, a shift toward real-time observations will occur, enabling better decision-making and quick action. Most of these observations will most likely be the product of custom apps developed to enhance the ability to react to changes in the environment as the market evolves. Analysis frameworks and components will make designing, altering, sharing, and maintaining these applications more accessible and efficient.

Frequently Asked Questions

What is big data in simple terms?

Big data refers to unprocessed data that is huge and complex. This data is complex and time-consuming to process while using typical processing approaches.
 

What is the meaning of big data analysis?

The complicated process of analysing large amounts of data to uncover information such as correlations, hidden patterns, market trends, and client preferences is known as big data analytics.
 

What are the various big data analytics tools?

The following are some of the most important big data analytics tools:

  • Hadoop: aids in the storage and analysis of data.
  • MongoDB: used for constantly changing datasets.
  • Talend: a tool for data management and integration.
  • Cassandra: a distributed database for handling data chunks.
  • Spark: used to process and analyse enormous volumes of data in real-time.
  • STORM: a real-time computational system that is open-source.
  • Kafka: a distributed streaming platform which is used for fault-tolerant storage.

Conclusion

In this article, we learned about the custom and semi-custom applications for Big Data Analysis. We saw the elements attached to each of the big data analysis approaches.

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Happy Reading!

Topics covered
1.
Introduction
2.
Big Data Analysis Approaches
3.
Custom Applications
3.1.
R Environment
3.2.
Google Prediction API
4.
Semi-Custom Applications
5.
Frequently Asked Questions
5.1.
What is big data in simple terms?
5.2.
What is the meaning of big data analysis?
5.3.
What are the various big data analytics tools?
6.
Conclusion