Integrating big data analytics systems in the development of software systems
Tags: softwaredevelopment
Published: 2017-10-04 10:09:26

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title: Integrating big data analytics systems in the development of software systems
published: true    
description: Big data is a technology revolution that has impacted numerous industries all over the world. Software development has also been impacted by the tech revolution. 
tags: software development
---

<a href="https://www.tatvasoft.com/">Custom software development companies</a> provide numerous services to meet the ever changing demands and needs of enterprises and businesses wherever they may be located in the world. A reliable service provider focuses on timely delivery, high quality and cost-effective services. Typically, service providers have varied and rich experience in development as well as stringent quality standards that ensure the development of solutions that provide an edge to any business over the competition.

<b><a href="http://www.itproportal.com/features/big-data-analytics-is-a-great-help-in-boosting-the-software-development-project/">BIG DATA IN DEVELOPING SOFTWARE SYSTEMS</a></b>
Big data is actually huge data volumes that need special procedures for analysis and processed. The world nowadays is data-driven. The benefits that a big data analytics software gives are speed and efficiency. This helps in a big way to the development of software systems. Furthermore, it helps organizations harness data and use it to uncover more opportunities.

<b>RISKS ASSOCIATED WITH BIG DATA SYSTEM DEVELOPMENT</b>
There are numerous risks specific to developing data analytics system. Software developers developing any system, whether big data or another, should address the associated risks with cost, quality and schedule. All the risks are amplified in the data analytics context. Architecting data systems is challenging since the tech landscape is rapidly changing and new and the quality attribute challenges. Some developers manage risks with architecture analysis, others use prototyping. 

<b>The risks inherent in huge data systems stem from:</b>
1. Volume, variety, velocity, veracity and value. 
2. Paradigm shifts, like polyglot persistence, data lake, lambda architecture. The main effort had been focused on small data system development coding but now focused on technology selection and orchestration in data systems. 
3. The fast proliferation and hardship in choosing huge data technologies. 
4. Rapid changes in technology. While small data systems concentrate on data stored or at rest in a database, analytics systems emphasize data that’s in use and in motion, which for most architects, bring data management to a new territory, like NoSQL. 
5. Short history of developing big data system. There are numerous technologies as well as tech families and many programmers have few, if any, experience in them. 
6. Complex new and old systems integration. A lot of data systems have to integrate with legacy or small data systems. 

<b>FIVE IDEAS FOR ENGINEERS IN APP DEVELOPMENT COMPANIES</b>
There are several things that software developers and engineers should keep in mind with the new world of data. The following are some of the things that a developer must know about big data and some tips as well.

1. Build or buy. IT stores have had a difficult time since 2000. Budgets were rising and outsourcing was not widespread. To reduce the budget, development teams say it could replace the database of the vendor with something they build themselves. Building would be a big mistake in the world of big data because it is a relatively young challenge. The key is to evaluate if the new tech is strategic to a business or not. If it is not, then it likely is not worth building. Buying it would be a better option. 

2. Coping with mix streams of data. Now, data is surrounded by an ocean of new data. Although big data marketing brochures make it appear as if the problem is all about data analytics, it is still important to do all familiar things with unglamorous, old-school data and determine ways to store, process and monetize all new data. 

3. Agility could mean patience, never anarchy however. Although agile often may feel chaotic, when properly managed, it could be a wonderful model to cope with the challenges of data analytics.

4. Embrace inner craftsmanship. Today is an interesting time in data. From the first computing days, it was all about structure, lists, arrays, tables, columns, rows and more. Audio, video, social media, electronic media of all forms, blogs, smart phones with search services, mobile apps all translate to different kinds of information, which used to go unrecorded to information now could be addressed by a computer. The engineers today working with huge data should be craftsmen when dealing with new data for the first time. Never be afraid to experiment.

5. Get executive buy-in. Buy-in from senior leadership is important to successfully build any kind of big data program and goes well beyond information technology. Leadership, sponsorship and strategy could be crucial to success. The greatest challenge is not technology but the fortitude and will to change to see a project through.

Nowadays, to keep up with the evolving technology and requirements, companies must invest in building a customized software that integrates all data. One of the most important elements of the approach to developing software is gathering good data. Traditional development processes are no longer the only means to build a robust system. There are so many new technologies and requirements to be taken into consideration.