Over the past few years, traditional business and market management have changed dramatically in reference to traditional ways. New approaches towards customer acquisition, activation, and retention have put information on behavioral patterns and insights that can be derived from data influx in the front rows. By proper analysis of these properties, entrepreneurs can achieve productivity. By lack of it, enterprises are destined for burial under the increasing amount of competition.

Accessibility of technology and its overwhelming usage in everyday life influenced the massive increase in data amounts that are available to entrepreneurs. However, the practical usage of the data is dependent on the ability to store, manage and analyze it adequately. Before the Big Data as a Service technology appeared as an influential opportunity for small businesses and organizations, these domains were reserved only for those who could afford them – i.e. big corporations. Big Data as a Service or BDaaS enables new competitive advantages as well as profitable management of customers and the market in order to ensure business growth and is highly accessible due to reduced costs of data processing endeavors.

Everything You Need to Know About Big Data as a Service (BDaaS)

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In this article we will present important information, constituents and processes of BDaaS as well as challenges which it faces through sections 1) Big Data as a Service – Defining the Term; 2) Types of BDaaS; 3) BDaaS Framework, 4) Requirements for BDaaS; 5) Advantages and Disadvantages of BDaaS and 6) Differences of BDaaS in Relation to Traditional Environment and Big Data.


Big Data as a Service is an emerging technology-focused on efficient and ubiquitous availability of constructive data processing. It is a cloud-based spectrum of hardware and software services for storage and analysis of increased amounts of diverse information which have emerged in the past few years due to technological advances and intrinsic presence of technology usage in everyday life (social networks, online media, etc.). The goal of BDaaS technology is to provide cost-efficient and valuable insights for organizations and small businesses in order to increase their competitiveness, innovation and, consequently, revenues.

Ingredients of BDaaS

  • High Functioning Service-Oriented Architecture: BDaaS technology provides a highly functional architecture which includes big data storage infrastructure, data processing modules and diverse analytical tools whose purpose is to reduce customer’s expenditures on employment of programming experts and data scientists as well as opportunities for targeted usage of these diverse layers according to specific needs. Moreover, the Service-Oriented Architecture (SOA) of BDaaS leverages each of the above-mentioned services individually as well as connects them into a whole – which allows a comprehensive approach to specific business requirements.
  • Cloud Virtualization Capabilities: The above-mentioned structures of BDaaS are based on cloud-computing and horizontal scalability. Essentially, this means that data is stored and processed on multiple processers that have specified tasks regarding the result required. The horizontal scalability enables these separate entities to work as a single logical unit and allows introducing new ones if the amount of data increases. On the other side, systems such as Hadoop are open-source storage technologies that operate on vertical scalability basis. This means they upgrade properties of single processors in order to manage increased amounts of data (and are thus dependent on technology advances).
  • Complex Event-Driven Processing: BDaaS technology enables data management in three modules – explanatory, descriptive and predictive. Through different sorting and analytical approaches, customers can obtain valuable information regarding issues, threats, opportunities and possibilities that can be used for overall business growth. Moreover, due to real-time processing techniques and on-demand features, the BDaaS system is not only timely and accurate but also less costly.
  • Business Intelligence Tools: Big Data as a Service uses application software for reporting, querying, online analytical processing, data mining, and numerous other elements in order to transform raw (and frequently unstructured) data into constructive information for business intelligence – that is, into information that can increase actual business efficiency.

Key elements of Big Data Which BDaaS Addresses

Velocity. Velocity of Big Data represents the speed of data fluctuation through systems. It is an important dimension of Big Data management as it leverages computing abilities in order to generate information with regard to real-time events. This is done through complex event processing applications. The ‘streaming data’ requires sufficient storage capabilities – which are ensured by BDaaS’s horizontal scalability, as well as optimized response intervals – through new technologies such as NoSQL which retrieve data in lesser amounts of time.

Volume. The size of Big Data datasets can amount to multiple petabytes and thus requires adequate distributed computing and horizontal scalability features. The volume of data is obtained and managed through implementation of thousands of nodes (individual processing units) with paralleled but particular tasks. The accuracy of predictive and descriptive analysis rises proportionally with increased number of processing units.

Variety. Big Data as a Service technologies expanded processing abilities from only structured data to unstructured data as well. The applications used by BDaaS effectively extract valuable data for usage from the majority of raw data which fluctuates through the systems. The proper managing of the variety dimension of Big Data results in increased ROI figures regarding the technology infrastructure.

Statistics on BDaaS

When looking at figures we must combine individual statistics on key building blocks of BDaaS – cloud computing and Big Data. Statistics derived from tendencies of these two constituents imply a continuous growth of BDaaS usage as well as its firm incorporation into the IT market.

  • The total amount of data influx achieved over the past fifty years equals to data influx amount that is achieved in two day nowadays
  • 15% of all IT investment is focused on cloud-based systems (with the estimated rise to 35% by 2021)
  • 50% of data in organizations will be stored on cloud-based systems by 2016
  • Big Data market is predicted to reach 17 billion dollars revenue over the course of 2015 (with the estimated rise to 88 billion dollars by 2021)
  • Big Data as a Service market is estimated to a 2.55 billion dollar worth according to the above stated predictions (with the estimated rise to around 30 billion dollars by 2021)
  • Industries with increased Big Data and cloud computing usage are business, finance, media, retail and telecommunications.
  • Almost 50% of data in organizations is predicted to be stored on cloud-based systems by 2016.
  • The total amount of data influx achieved over the past fifty years equals to data influx amount which is achieved in two day nowadays.


BDaaS technology implements Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) tools and techniques in order to provide complete storage and analysis data processing. Moreover, BDaaS implements Hadoop infrastructures but can upgrade their efficiency through the incorporation of different software according to needs of particular data processing. With reference to these layers, we can divide BDaaS into four types.

BDaaS types

BDaaS types


IaaS. In the IaaS layer users are offered generic infrastructures for data storage in cloud environment as well as on-demand employment of nodes for data processing. The IaaS layer provides most opportunities for direct influence on the BDaaS technology (scalability, computing, and accessibility of raw data) but requires proficient programming and data skills. Amazon’s EC2 storage platform is excellent software for IaaS properties.

PaaS. Platform as a service incorporates basic infrastructure with provisionary features regarding application deployment. It requires expertise in programming and data science is necessary to maintain the layer. However, it does reduce the involvement of customers in matters of hardware and storage as it is mainly based in virtual surroundings. Some examples of PaaS layer are Heroku, Google App Engine, and Force.com.

SaaS. SaaS layer enables users to access applications without spending time and finances on programming, installation and maintenance of the underlying software. The service provider deals with these features while the customer uses applications on demand. However, customers cannot access infrastructure layers and raw data from the SaaS layer.


Core BDaaS. Core BDaaS is considerably generic and uses infrastructures such as Hadoop, Google’s Map Reduce, Spark or individually written Java-scripts. Many users opt for Hadoop-based infrastructures because it is free open source software. Core BDaaS combines this basic infrastructure with storage applications such as Amazon’s S3 or Hive and NoSQL processing engines such as YARN. A comprehensive Core BDaaS technology is Amazon’s Elastic MapReduce (EMR).

Performance BDaaS. Performance BDaaS uses basic infrastructure but includes provisionary usage of other software and hardware (for example, Altiscale) services in order to optimize performance for specific purposes – increasing scalability and computing potential at predictable costs.

Feature BDaaS. Feature BDaaS evolved in order to provide possibilities of application definition according to needs of particular assignments. Essentially, this means that the basic infrastructure allows employment of different basic software regarding features – that is, computing and storage are independent of the service provider and can thus be fully scalable. For example, Hadoop ecosystem offerings are refined with Amazon’s or Google’s IaaS software.

Integrated BDaaS. Integrated BDaaS has not yet been offered, but it would theoretically comprise out of both Performance and Feature BDaaS so as to allow maximum performance while supporting business owners.


BDaaS framework incorporates different layers according to the function each of them performs in the process of data storage, computing, and analysis.

BDAASData Infrastructure. The primary layer of BDaaS comprises out of data hardware and thousands of distributed computing unites (nodes) which are all interconnected and perform as a high-speed network lines through which the data fluctuates. This layer of BDaaS provides firewalls and backup system so as to prevent potential loss of data. As building your own database infrastructure can amount to expenditures of over 1.5 million dollars for 1000 square meters of space, BDaaS system’s infrastructure presents itself as the most profitable solution for the primary architectural sphere of data processing. Moreover, the profitability increases with the awareness that most businesses need data processing for specific information at sporadic intervals and would reach a negative ROI balance in case of building a new database each time.

Cloud Infrastructure. Cloud infrastructure is the virtualized domain on which data, software and hardware interrelate. Cloud infrastructure can be private or public and can be reserved in advance for a longer period (for example, several years), on demand (for a specific period of time during which particular processing will take place) or on spot (this option can have impact on availability of service as you cannot predict how much processors will be employed elsewhere). This layer does not include presentation access.

Data Storage Layer. The data storage layer is highly accessible for customers as it enables direct upload of data for analysis. Moreover, the layer is horizontally scalable for requirements of data volume, velocity and variety and introduces new nodes according to the demand of these factors, as well as needs of particular industries and goals of the analysis.

Computation Layer. Computation layer comprises out of technologies for performing distributed computing services such as processing frameworks and Application Programming Interfaces (APIs) whose objective is to manage and manipulate data according to requirements and customer’s preferences (users can write programs themselves if there is sufficient expertise in programming and data analytics) with the objective of constructive information derivation from Big Data.

Data Management. Data management layer undertakes procedures of maintenance and optimization of processing over the cloud platform. This includes system backups, deployments and resource requirements with the objective of safe-keeping of data and information as well as high efficiency.

Data Analysis. The data analysis layer is the highest level of data processing in BDaaS and is in charge of analytical procedures regarding the underlying data. The customers access data through a web interface and create analytical reports and queries that are related to the data submitted to the storage layer. In order to maximize performance, this layer offers wizards and graphical tools which guide users through the process. Moreover, this layer of the BDaaS stack enables and offers customized approaches and applications with reference to specific industry-based requirements of users. Due to this feature of the data analysis layer, BDaaS proves to be highly productive system for diverse organizations and enterprises – because you can choose from technologies that will address important segments of your industry (for example, in finance industry, it will offer stock exchange graphs, risk monitoring and banking operation analytical and presentation tools.


Data governance. Effective data governance can make the difference between failure and success. With the overwhelming increase in both structured and unstructured data (90% of current raw data has been generated in the past two years) from points of sales, transaction records as well as from media, social networks and diverse information gathering techniques – which are implemented in order to spur customer engagement through better understanding of their behavioral patterns, enterprises must govern their data conscientiously – targeting data which is to be analyzed with regard to their industry and business necessities – so as to extract actual value and profitability from the process.

Data Security. While big organizations and companies have the means to purchase private cloud platforms for their enterprises that can be beneficial for security issues, small businesses cannot afford such endeavors. In order to ensure the safety of your data (and exclude risks of outside data manipulation) request a division of units of data and tasks undertaking across separate processors which cannot be connected without special permissions. Additionally, employ data backup systems that should prevent potential data loss.

Data Strategy. The data that you intend to process should be structured with reference to layers of BDaaS through which it will be computing. If you design a structure of pathways through which the data will fluctuate, you will ensure a constructive process and eliminate potential inconsistencies even before the process is put in motion.

Don’t focus solely on the volume, variety and complexity of data. Data analysis should serve a predefined set of objectives. Even predictive analysis procedures are a strategy of a sort (anticipation of possible trends and future tendencies). Hence, you should structure a strategy within which the results of data analysis will be incorporated. Determine short-term goals of the strategy in correlation with long-term goals of your enterprise. Additionally, monitor the process from data extraction to the final analysis in order to avoid the overly abstract set of information which cannot be implemented in the predefined strategies that you have created.

Don’t try to rush all data out to everyone all at once. As you incorporate analyzed data and information that derived from the process into your strategy, present it according to current requirements of your business. There is no need to flash out all of the information to everyone. Use the information timely and with a comprehensive awareness of its place within the current or future advances of your enterprise.



  • Cloud Infrastructure: Enables instantiation of IT infrastructure and determines capabilities of overlying infrastructure (virtual machines and/or hardware);
  • Data Storage: Access to raw data in distributed storage;
  • Computing: Flexibility that arises from possible customized programming for data manipulation;
  • Data Management: Direct access to data and possibilities for complex data analysis and modification;
  • Data Analytics: Users can access analytics services without having to deal with data or programming spheres of BDaaS infrastructures;
  • Scalability: Proper addressing of challenges regarding Big Data processing and not dependent on technology advances;
  • Security: Responsibility for security issues is transmitted to the provider of the services;
  • Service: Transferring time and finance consuming operations and technology development to a third party.


  • Cloud Infrastructure: Infrastructure knowledge requirement – challenge regarding expertise;
  • Data Storage: Programming knowledge requirement – challenge regarding expertise;
  • Computing: Programming knowledge requirement – challenge regarding expertise;
  • Data Management: Programming knowledge requirement –challenge regarding expertise;
  • Data Analytics: No direct access to data and analytics services are restricted to the data which is in the data analytics layer;
  • Security: Potential negative manipulation of data by external parties – can influence business growth;
  • Expertise Issues: As can be seen in above-mentioned parameters, lack of the skilled workforce presents a challenge that will have to be addressed in the future management of BDaaS technology.


Big Data as a Service emerged as an answer to challenges of big data processing in order to increase enterprise competitiveness, productivity and longevity through the insightful implementation of valuable information. In this section, we will discuss the ways in which BDaaS proves to be more efficient than traditional approaches to Big Data processing.

Increased influx of voluminous data over the past few years occurred while the environment was not suitable for its adequate management and utilization. The traditional environment was capable of processing only structured data with less developed analytical tools and techniques. Moreover, it lacked computational power and storage capacities for large amounts of diverse data.

Traditional Big Data systems could address structured data processing requirements on distributed architectures and reached certain scalability in storage and computing as well as employed advanced analytical procedures. However, the accessibility of these systems was still limited and derived from custom coding.

Big Data as a Service enables processing structured and unstructured data (80% of data which is obtained by companies is unstructured) with advanced analytical tools. Moreover, it offers cloud-based distributed computing services with possibilities of scaling up as well as ubiquitous availability and on-demand opportunities. BDaaS offers both specified domain-based algorithms and custom coding possibilities from which analytical capability derives. Further on, it stores data on virtualized cloud platforms.

With the increased amounts of big data which is fluctuating in relation to market and its constituents with enterprises, entrepreneurs can employ accessible BDaaS technologies and services in order to endure and prevail among the competition. Business growth is nowadays dependent on obtaining valuable insights on the patterns of behavior, as well as changes in the market and reacting appropriately to these properties. By using BDaaS technology, these requirements can be met without driving your business into bankruptcy. It can be hard to discard all of the traditional approaches and methods which have been used in business for much longer than the new ones which are emerging at every corner but it does not change the fact that you must transfer into the progressive and active business management in order to survive on the market.

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