Big Data Solutions are complex and relatively new to the 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. We at ATSC hope you canfind a comprehensive explanation of the big data applications architecture and resource breakdown for a sample of this kind of project, with our best in its class software & technologies approach.
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 the data and make decisions based on them. Business Intelligence and Data warehouse have proved the importance of data 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. Data consulting services have provided tools to improve decision making.
In the meantime, cost of server hosting has decreased significantly, more data can be saved. Data become more and more complex as they grew exponentially rather than linearly. Today, structured data represent only 15% of data produced every day, the remaining 85% is unstructured like music, movie etc. A study has found that more data are produced in two days than since the dawn of humanity till 2003. Since SQL can’t query unstructured data, another way to query these data were elaborated and NoSQL database has emerged.
FROM STRUCTURED TO UNSTRUCTURED DATA
Even if a very large data warehouse can be conceived, they are 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. The data 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.
Building a data warehouse from scratch is no easy task. A large project such as this requires more than a year of setup, configuration, and optimization before it is ready for business intelligence purpose.
But remember, your database warehouse is only one aspect of your entire data architecture:
Typical Big Data Architecture
It is no surprise then, that according to Gartner, over 60 percent of all big data projects fail to go past the experimentation stages and are subsequently abandoned.
Amazon conducted a study on the costs associated with building and maintaining data warehouses, finding expenses can run between $19,000 and $25,000 per terabyte annually.That means a data warehouse containing 40TB of information (a modest repository for many large enterprises) requires a yearly budget of about $880,000 (close to $1M), assuming each TB requires $22,000 in upkeep.
What’s behind these costs and should they be worrisome to a business? While storage is growing more affordable every year, engineering (the setup and maintenance) of data warehouses is what lies at the heart of the issue. IT leaders, Architects and others in charge of developing data warehouses often encounter the following challenges, which add up to real and opportunity costs that affect the bottom line
REDUCING COSTS THROUGH BIG DATA INTELLIGENCE PLATFORMS
Running BI tools directly on Hadoop and other Big Data systems entails using Big Data intelligence platforms to act as mediators between BI applications and enterprise data lakes. The technology uses standard interfaces such as ODBC and JDBC that connect BI applications to various Hadoop ecosystems.
Big Data intelligence platforms provide data analysts with virtual cubes that represent the data within Big Data ecosystems. Analysts may use SQL, XMLA or MDX to create dimensions, measures and relationships as well as collaborate with other modelers to share, adjust and update models. For instance, an analyst using Excel could apply MDX queries, while another working in Tableau can employ SQL.
At their core, Hadoop intelligence platforms eliminate the need to extract, transform and load data to perform BI workloads through adaptive caching. This technology provides the responsiveness BI professionals expect when applying queries without introducing the need to create off-cluster indexes, data marts or multidimensional OLAP cubes. Instead, adaptive caching analyzes end-user query patterns and learns which aggregates users would want to pull.
The aggregates sit on the Hadoop cluster. In addition, the adaptive caching system handles the entire aggregate table lifecycle, applying updates and decommissioning as required. Ultimately, it eliminates the need for IT to migrate data to off-cluster environments.
But what about IT control? Hadoop intelligence platforms connect directly to secure Hadoop services, providing support for delegated authorization, Active Directory, Kerberos, SASL, TLS and other security protocols. In addition, administrators can monitor cube access and queries to ensure they’re effectively enforcing role-based access controls.
THE TRANSITION
With the technology available today, investing in proprietary cost intensive big data warehouses is no longer the only option. And neither is incurring the time, maintenance, and labor costs associated with constantly moving data into warehouses built for business intelligence queries.
It was to handle this important amount of data and especially this huge amount of unstructured data that big data was born. It has become very popular, and in recent years, it is more relevant than ever to master it.
And at ATSC, traditional data consulting services have evolved and have integrated big data with their services. They have become big data consulting services. Let’s dig a little further to better understand what big data is, why has it become the tendency, what is big data solution, and especially what could be the cost of implementing big data solutions.
BIG DATA TRENDING
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 billions of interconnected objects to date.
Even since 2014 when McKinsey 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 from big data. This difference is likely to be more important today and it is increasing every day as we are getting more familiar and well trained on big data and are more experienced in implementing big data 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 an ally of big data.
APPLICATION FIELDS
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:
The big data is used by companies and businesses to:
BIG DATA USAGE
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 big data 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:
Telecom is one of the fields that mostly exploit big data today, big data solutions for telecom are essential:
Adopting data-driven approach increases customer interaction and improves customer satisfaction.
TECHNOLOGIES
In big data 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 data restructuring. The second challenge lies in data storage, choosing the fittest databases to optimize the storage for future analysis.
With ATSC’s vast real-world experience and enthusiast experts, we can offer our customer with a renovated platform that come in trending in terms of big data software solutions:
ARCHITECTURE OF BIG DATA
We have seen there are several big data solutions, big data solutions for telecom is among them. It is hard to generalize the architecture of the Big data applications as well as how the data itself is structured there. A very simple example of the solutions is given on the scheme below for your general understanding how different modules are connected and integrated with each other to do what Big data 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.
ATSC architecture example
In this section, let’s take a simpler example of applications of big data such as a car rental services. When deciding to scale a car rental service, there are many challenges that we may face.
Taking profit of big data solutions, these challenges can be tackled intelligently with a real-time solution:
The solution includes the following components and layers:
COST TO BUILD A BIG DATA SOFTWARE SOLUTION
Big data applications are slightly different from traditional applications. They are data-driven, taking advantages of the huge amount of data 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 project. 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. Big data 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:
It can involve much 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 big data 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 from big data without having to engage their human resources in setting up big data solution.
Big data applications can be built on the top of existing platforms and systems. building big data applications follows the following processes:
The following estimation doesn’t take into account the infrastructure and hardware needed to implement big data. We should also remember that as data evolve the costs may evolve as well.
We are going to consider in the following estimation the cost of building big data application which takes into consideration the following endeavor:
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:
From these processes, we can identify the following actors involved in the process of building a big data application:
The big data software development team can be composed in several ways with the actors mentioned above. The development of a first iteration needed for actions previously will cost:
Assuming the project duration is about 01 year for this phase.
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 solution building, it is an ongoing process where every output leads to another axis of improvement.
(ATSC BigData solutions team.)