Big data solutions are complex and relatively new to most of the devs. Despite that, many enterprises dive deep into the big data software development as it is hard to overestimate its value for the business. Here you will find a comprehensive explanation of the app architecture and big data solutions cost breakdown for the sampled development project.
Highly valuable information is hidden in the huge volume of data that more and more businesses are aware of their importance. Systems were conceived and created to save information so that we can analyze it and make decisions based on them. Business Intelligence and Data warehouse have proved the importance of analysis in decision making based on structured data. The traditional databases were conceived to store structured data, queried using SQL languages. Data warehousing and Business Intelligence were the first pioneers for data exploitation and data mining. These consulting services have provided tools to improve decision making.
In the meantime, the cost of server hosting has decreased significantly, more data can be saved. Data becomes more and more complex as they have grown exponentially rather than linearly. Today, structured data represent only 15% of the amount produced every day, the remaining 85% is unstructured like music, movie, etc. A study has found that the bigger amount is produced in two days than since the dawn of humanity till 2003. Since SQL can’t query unstructured data, another way to query it was elaborated and NoSQL database has emerged.
List of The Contents
- Structured and unstructured Big Data
- Big Data in 2018
- Where Big Data solutions are used
- Real-life Big Data solutions examples
- Technologies that are used for Big Data development
- Structure of Big Data software solutions
- Example of the Big Data application development cost
- In conclusion
FROM PROCESSING STRUCTURED DATA TO PROCESSING UNSTRUCTURED DATA
Even if a very large data warehouse can be conceived, it is limited to structured and semi-structured data. The BI consists of a set of tools and techniques for collecting, cleaning and enriching structured or semi-structured data to store in different forms of SQL-based, multidimensional databases. It will, therefore, be managed in standardized formats to facilitate access to information and processing speeds. Business Intelligence aims to produce KPI to analyze past and present data in order to predict the future using extrapolation and act based on these forecasts.
To handle it especially this huge amount of unstructured data, big data was born. Big data has become very popular, and in 2018 it is more relevant than ever to tame it. Traditional data consulting services have evolved and have integrated it with their services. They have become big data consulting services. Let’s dig a little further to better understand why it has become the tendency, what is big data software development, and especially what could be the cost of big data implementation.
WHERE DO WE STAND IN TERMS OF BIG DATA IN 2018?
Technology has evolved so fast, now we are talking about petabytes to evaluate stored data when a few decades ago, megabyte seems to be a big revolution. Today thanks to IoT (Internet of Things) there are even more and more interconnected objects, the estimation is over 10 billion interconnected objects to date.
A study published by McKinsey in 2014 reported that 131% is the percentage of ROI that companies obtain, compared to their competitors when their managers and executives have access to an adequate analysis of the data. This difference is likely to be more important today and it is increasing every day as we are getting more familiar, well trained and are more experienced in implementing big data software solutions.
Data find their sources from diverse platforms like social media producing huge quantities of data: individual information, link sharing, blogging, etc; applications: geo-localization, application logging, etc.
Several applications of big data are developed and implemented and AI has become its ally.
WHERE AND HOW BIG DATA SOLUTIONS ARE USED?
The data recorded relate to several domains, all the businesses can find the information they want. Currently, the field of use of big data solutions is quite large. These fields include:
- Telecom is known as big data solutions for telecom
- Media and Entertainment
And much other business and industry domains.
It is also used by companies and businesses to:
- Help in decision making: use of real-time and accurate data for an informed decision, decisions are based on facts not on guts
- Help with the customer segmentation
- Offer a fully-customized service to customers
- Manage stocks and predict needs in products, predict ideal product to be stored
- Help in understanding customers’ needs, behaviors and habits
- Help to better manage customer relationship, offering more customer-centric products
- Help in fraud prediction
- Help improving business process
- Help identify and remove performance bottlenecks proactively, optimize resource utilization, and reduce costs.
- Reduce billing errors, verify eligibility, detect and prevent fraudulent claims and speed-up revenue recovery.
It can be used in several different ways.
WHAT ABOUT REAL-WORLD BIG DATA SOLUTION EXAMPLES?
Big data finds its strength in analytics and offers a very powerful tool for decision making. Now that we understand better what is big data solution, we may wonder behind these theories, how it is really operating in the real world and what are these real-world uses.
Media (videos, audios, etc.) today represent an important part of the data stored to date. It is not surprising that one of the uses in the real world is in the marketing of video content. Netflix is a good example of big data solutions’ use. It has to this day millions of customers.
Customer management: when offering content to its customers it records their behaviors in order to offer them personalized programs and contents:
- What they watch
- The time at which they watched the videos
- How the video was seen (in a continuous or discontinuous way, the complete video has been watched or has it been watched partially, was it looped and how many times was it seen, etc.)
- How they interact with the video
- What are their appreciations if any
- How did they react at the end of the video
Telecom is one of the fields that mostly exploit it today since big data solutions are essential for:
- Customer management (how to acquire and retain customers): a behavioral analysis using data science, identifying proactively potential “churners” using a predictive model, targeted marketing (tools to perform customer segmentation profiling and offering areas of improvement), personalized plans
- Operation management: call routing optimization (using IVR or direct routing to customer services, agent-specific routing depending on the end user to allow better user experience, etc.), capturing all calls and events (CDR) in real time, capacity optimization by forecasting demands, marketing based on geo-localization (data centers are implemented in regional basis allowing a real-time analysis and approach)
- Network and infrastructure management: proactive maintenance, failure prediction by detecting an anomaly
- Security management: detect and prevent fraud, securing payment, protecting data
Adopting the data-driven approach increases customer interaction and improves customer satisfaction.
WHICH TECHNOLOGIES ARE NOW USED FOR BIG DATA SOLUTIONS?
In this concept, we are collecting huge and complex data sets collected, processing them requires specific tools and applications. Part of the complexity lies in the fact that the data volume is very large and is mostly unstructured.
The first challenge is to find the best way to address data restructuring. The required big data software solutions should support this restructuring. The second challenge lies in its storage, choosing the fittest databases to optimize the storage for future analysis.
What is currently the trend in terms of big data solutions?
Apache Hadoop: Apache Hadoop is one reference when it comes to big data frameworks, it brings a solution where traditional data exploitation tools are very limited in tackling unstructured data. Hadoop is a free and open-source framework used to store data and process data. Thanks to its replication, Hadoop provides high availability. Hadoop implements MapReduce framework, which allows large processing by distributing the computation on several nodes. Hadoop is a very mature big data solution, it has been used by several companies. Hadoop allows big data agile development.
AWS is a service provided by Amazon, used to store large amounts accessible online for other hosting applications. AWS has many advantages such as security, reliability, flexibility, and scalability. AWS is now very popular among companies, making the skills in this technology very on-demand.
Cloudera offers centralized storage making data analysis easier, software needed is delivered in a single package reducing considerably installation time.
NoSQL database: Since we are dealing with any data types, the database should adapt to the absence of structure. The specificity of this kind of database is that no specific schema is required for storage in the database. The most popular NoSQL databases used are: Apache Cassandra, Oracle NoSQL, HBase, MongoDB, Amazon SimpleDB
Tableau is an analytics tool transforming data into insights.
Talend Big data Integration: it is a big data software solution including graphical tools and components that generate code allowing the big data development team to work with other solutions and technologies like Apache Hadoop, and NoSQL databases, etc.
STRUCTURE AND ARCHITECTURE OF BIG DATA SOLUTIONS
We have seen there are several big data solution examples, telecom is among them. It is hard to generalize the application architecture as well as how the data itself is structured there. A very simple example is given on the scheme below for your general understanding how different modules are connected and integrated with each other to do what it is supposed to do – provide users with reports and the analytics based on the huge massive of the information from various sources received by this kind of the software.
In this section, let’s take one of the simpler big data solution examples in car rental services. When deciding to scale a car rental service, there are many challenges that we may face.
- Reliability of partners (car owners, other businesses)
- The success of the scale tentative
- Control over users
- Reliability of the renter
- Personalized offers for customers based on their habits, their budget, etc.
Taking profit of big data solutions, these challenges can be tackled intelligently with a real-time solution:
- Management of historical data regardless of the provenance
- Time processing allowing real-time analysis and offering a real-time solution
- Solutions that can scale in a straightforward way and very fast
- A solution providing fast delivery and almost immediate input/output
- Solution able to handle huge amounts of data, good performance
- Solution-focused on business
- Cost savings and even a better ROI
- IT resources leverage
- Enterprise-ready solution
- Complete production environment
- Rate management
- Preventing loss
- Evaluate customers satisfaction
- Accident management
The solution includes the following components and layers:
- Sources of big data: the tasks in this component consist of understanding the data provenance, at this point data scientists will clarify their needs in terms of data in order to identify the required one for their analysis. The structures and formats of data needed may vary considerably from analysis to another and even in one axis of analysis, there may be a big variety of structures and formats. It is important to understand the speed at which data arrive and evaluate its volume to predict the amount of data analyzed for each analysis. In this layer, we should be able to understand the restrictions regarding the data analysts may need.
- Data acquisition layer: at this point, the process consists of acquiring data from previously identified sources. Transformation occurs here, ETL-like transformation to extract it from their sources, transform them to fit the format needed for data analysis and then load in Hadoop Distributed File System store or a traditional RDBMS. After this first processing, some further processing may also occur.
- Analysis layer: Once the data are identified and processed, analysis can begin. It may happen that access is made directly on the source depending on the analysis that needs to be done. At this point, support for decision making is developed, we are producing analytics using the best tools and algorithms to do the analytics
- Consumption layer: In this layer, the analysis provided is consumed, analysis can be displayed in applications or entered into other business processes.
HOW MUCH DOES IT COST TO BUILD A BIG DATA SOFTWARE SOLUTION?
Big data applications are slightly different from traditional applications. They are data-driven, taking advantage of the huge amounts and then working on getting the valuable information underneath. The development process is also quite different but there is at least one common component for a successful project for both traditional and big data projects. This common denominator is the methodology advocated for its implementation, Agile methodology is more than ever relevant. Big data agile development is a must in order to have a successful implementation.
A good strategy should be taken into consideration while building big data solutions. Not every strategy will work as we are addressing a very specific area. Strategy consulting can be very helpful for companies so that they can avoid wrong investments in solutions and infrastructure that do not really meet their needs.
In the process of building a big data application, the following approaches are used:
- Development of analytic strategies to identify the hidden advantage of a large amount for more competitiveness
- Development and deployment of analytics tools: to identify possible bottlenecks, performance or production issues
- Development and deployment of applications that support the decision making
It can involve many more actors than those listed above due to the inner complexity that lies in big data.
Big data consulting services are not as numerous as traditional software development consulting services. Developers that worked with big data skills are currently really scarce, not every developer has the necessary qualification for this job. Many of them aren’t yet familiar with the complexities that lie in deploying, managing and tuning of the applications. Fortunately, the infrastructure is getting simpler so that the focus can be on the building of applications that are of interest to companies. big data consulting offers skills and allow companies to focus on their core business in a way that they can benefit without having to engage their human resources in setting up a big data software solution.
Big data applications can be built on the top of existing platforms and systems so it is important to consider the following processes:
- Design and infrastructure: designing the architecture of the big data application to be implemented. The design is based on several assumptions.
- Hardware and network configuration, and implementation: Once the application to be built is designed, hardware and software configuration are done.
- System development, integration, training: big data experts who are going to build the application are trained so that they have a better understanding of the company’s needs and goals to be able to implement the best solution. Application development starts following the agile development methodology. Starting from an assumption, the line of codes are written, every data can lead to new assumptions that lead to new lines written. It is really important to work in a very small increment developing one single assumption at a time, the assumption is analyzed, developed into actionable business information. Being Agile is really important because learning from feedback is one important aspect, concept and advantage of big data. Feedback helps evaluate if the direction taken was the right one or if we should adjust our direction during the following iteration.
- Test of integrated system and launch in the production environment: the output developed from assumption is tested and if the test is positive it will be pushed into the production environment.
- Improve infrastructure: from iteration made and integration to live system, we can work on improving infrastructure so that we can have a more reliable infrastructure
The following estimation doesn’t take into account the infrastructure and hardware needed for implementation. We should also remember that as data evolve the cost of big data implementation may evolve as well.
We are going to consider in the following estimation the big data solutions cost which takes into consideration the following endeavor:
- Improvement of partners’ reliability: acting on data about car owners, businesses’ partners
- The success of the scale tentative: how to take profit of data to increase the scope of car rental service’s business: address a wider customer.
- Competitivity: analyze data to be more competitive, identify strengths and weaknesses.
- Renters’ reliability: define and measure reliability for safer operations. Identify the KPIs that define reliability of renters retrieve them from data and analyze them to identify the cause of potential failure etc.
- Personalized offers for customers based on their habits, their budget, etc.: understand how to tailor big data application development services that best meet the needs and budget of users.
Six-month iterations will be done in order to improve the above actions. Let’s refer to this first iteration as the big data car rental iteration. The following estimation takes into consideration the afore-mentioned goals done in a six months scope.
Since this is the first set-up, the delivery time for each phase and type of actions is as follows:
|Hardware and software configuration
|System development and integration
|Testing and launch
From these processes, we can identify the following actors involved in the process of building a big data application:
- Computer analysts
- Business analysts
- Developers engineers
- Software developers
- Database analysts
- Data administrators
- Data analysts
- System administrators
- ETL developers
The big data software development team can be composed in several ways with the actors mentioned above.
|Developer engineers/software engineers
|Big data PM
|Hardware and software configuration
|System development and integration
|Testing and launch
The development of a first iteration needed for actions previously will cost:
- 4 System administrators working full time 1 month charging $70/hour
- 4 developer engineers working full time for 6 months charging $80/hour
- 4 database analysts and/or DBA working full time for 1 month charging $90/hour
- 2 data analysts working full time for 6 months charging $65/hour
- 1 big data project manager working full time for 6 months charging $100/hour
- 1 QA tester working full time for 6 months charging $70/hour
The big data module for the car rental service will cost approximately $698 000.
THE BIG DATA SOLUTIONS COST BREAKDOWN TABLE WITH THE COMPARISON OF THE PROJECT BUDGET IN THREE DIFFERENT LOCATIONS
In the table given below, we will try to show you the overall cost and hourly rate of hiring each specialist from a third party like a big data software development company with the required expertise. Also, we compare how much the big data development would cost in the US, Western Eastern, and in the location for offshore outsourcing, Eastern Europe in our case. The average cost of the big data software development services is calculated based on open sources such as Glassdoor and the rate cards provided by several companies.
|Number of Devs.
|Months of Engagement
|US Hourly Rate
If we consider continuing in pursuing big data exploration with an additional iteration, we repeat from system development to infrastructure improvement, there is no cost or a very low cost for infrastructure design and hardware configuration once it is first set.
There is no limit in big data solutions building, it is an ongoing process where every output leads to another axis of improvement.
Today we are facing rapid rise and evolution of technologies, companies may sometimes feel lost in front of the vast and unknown possibilities. Many big data consulting solutions are available to them, to help them analyze and choose the right tools and big data tools that best fit their business. These solutions are tailored so that companies can adapt their services to the often-changing demands of often very versatile customers.
Today, IoT makes information more available, information that took companies years to acquire a few years ago which unleashes more and more opportunities. Working with the right approach and adopting the right solution is key to business success.
Due to its complexity, we advise starting small while implementing this approach for the first time and then do the numbers of iteration needed in order to have the outcome expected. The slow implementation enables actors involved in the process to master technologies and business, to be more comfortable with the tools they are using. It is important to surround yourself with experts for the implementation of the big data application development that would best fit the business.
What is your strategy for the upcoming years, do you plan to use the advantages of solutions offered to you and your business? Share your thought and opinion in the comment section below. Need a quote for the big data solutions cost or help with building it? Existek is a professional software development company that has extensive experience in big data application development and will be glad to share it with you. Feel free to contact us via the contact page or use the chat widget on the right.