In the past few years, we have witnessed an explosion in big data space, which opened up huge organization opportunities. With big data analytics, organizations can manage, capture, and analyze massive volumes of disparate data to get insights and deriving decisions to create a competitive advantage. However, the big challenge with big data is managing it with traditional databases. Also, existing scalable architectures are another obstacle.

This needs an innovative technological solution. In the same context, we know that services are often delivered as technological tools. Many big data vendors offer Software as a Service (SaaS), Data as a Service (DaaS), and Platform as a Service (PaaS) as data solutions. And in this space, Big data as a service takes things to a whole new height, combining these tools and applying them to large data sets. Big data as a service helps large and small organizations to meet today’s big data demands cost-effectively.

Big data as a service is a cloud-based solution that interestingly is being applied at an increasing rate and is about to increase from 15% to 35% by 2021. This is equal to a forecasted Big data as a $30 billion service market, whereas the global Big data market will be worth $88 billion by that point.

What is Big data as a Service?

Big Data as a Service or BDaaS is an umbrella term used for various services related to big data management functions that run in the cloud. This is in analogy with the XaaS models based on SaaS, IaaS, and PaaS model in cloud computing and applicable to big data.

The cloud-based framework, Big data as a service, provides end-to-end big data solutions to enterprises depending on their demand. This is a combined structure of data-as-a-service (DaaS), Hadoop-as-a-service (HDaaS), and data analytics-as-a-service.

Big data as a Service = data-as-a-service (DaaS)+ Hadoop-as-a-service (HDaaS) + data analytics-as-a-service.

To ensure the high data quality and channelized data flow in enterprises has urged the need for more advanced big data solutions that can gather, assess, store, visualize, and make predictions from the analyzed result of large data volumes. As there is a rise in cloud-based predictive analytics usage, we can witness strong growth of the global market for big data as a service in the years to come.

In the technology of Big data as a Service model, the statistical analysis from third party big data analytics service providers helps to understand an organization's insights. This works as an objective to achieve a competitive advantage. As global data as a service market is growing faster, it is due to the increasing demand for statistical analysis. Interestingly, a massive amount of unstructured data is getting generated almost regularly from various organizations.

Thus, the organizations outsource these massive data to big data services to manage the large data rather than in-house. Big data as a service is offered in various ways, for example,

• big data analytics software as a service
• as data fabric that includes data management and data aggregation.
• as data platform service which covers the analytical programming,
•  as cloud infrastructure.

The key functions of Big data as a Service include:

Multiple Functionality Service-oriented Architecture. Big data as a Service is an architecture that includes Big Data storage architecture, different data processing modules, and analytical tools. The analysis process of Big data as a Service helps reduce the user's cost from employing additional resources like data scientists and programming experts. It also provides scopes for targeted usage of the different technological layers, depending on specific needs.

Capabilities of Cloud Virtualisation. Big data as a Service is based on cloud computing. Moreover, it is horizontally scalable, which means that you can store and process data with specific tasks on multiple levels. This enables separate entities to work as a single unit and, due to its scalability, allows new ones to increase the amount of data.

Facilitating Business Intelligence. Big data as a Service uses application software to query, report, data mining, and online analytical processing to change unstructured and raw data into meaningful information for Business Intelligence.

Even-driven Processing.  Big data as a service enables data management in three ways – explanatory, descriptive, and predictive. This data management enables customers to obtain valuable information about the business like the possible threat areas, issues, possibilities, and opportunities. These are essential to know for expanding and the growth of the business. Additionally, as BDaaS processes data on a real-time basis, you can obtain on-demand features accurately, timely, and in an affordable way.

Related post - 5 Big data analytics trends for 2019 expected to influence Artificial Intelligence

What types of BDaaS are there?

There are three competing types of cloud-based BDaaS :

1. Core BDaaS – This type of service offers a minimal use of Hadoop with HDFS and YARN along with other services such as Hive. The services have been preferred as part of a huge architecture and specifically for irregular workloads. An example of the core big data-as-a-service is Amazon Web Service’s Elastic Map Reduce (EMR). EMR integrates instantly with the NoSQL store, DynamoDB, S3 storage, and other services.

2. Performance BDaaS – As the name implies, this service optimizes the already used Hadoop infrastructure's performance. But who is the right fit for performance BDaaS? Organizations that are rapidly growing but are limited by scale and complexity, related SaaS layer, and can’t build data architecture independently, are a good fit for Performance Big Data-as-a-Service. In this scenario, the companies can outsource their infrastructure and platform related needs to a provider and focus on the domain-specific processes. This can add value while eliminating many of the responsibilities associated with complex big data deployments.

3. Feature-driven BDaaS – This Big data as a Service is ideal for the companies that need extra features beyond what is commonly offered in the Hadoop ecosystem. An example of feature-driven BDaaS is Qubole. Qubole performs through the web, database adapters, and programming interfaces that effectively put Hadoop technologies behind the scene. Not only that, depending on the workload, but it also starts, scales, and stops Hadoop clusters transparently.

4. Integrated BDaaS. This is a combination of Performance and Feature BDaaS. Not to mention, this will allow maximum performance.

What is so special about Big data as a service?

As we know, Hadoop enables organizations to analyze data using commodity hardware and open-source software; however, the costs of launching a big data initiative are substantial. Not to mention, in the case of Hadoop, it is an ongoing investment of time and resources to store and manage a massive set of data. In contrast, big data-as-a-service allows organizations to outsource various big data activities to the cloud, and organizations need to pay only for the computing power they require.

This is an out-of-the-box solution in Big data space, eliminating many of the costs associated with a Hadoop deployment. Additionally, organizations can focus on gaining actionable big data insights to drive business growth. Furthermore, big data analytics service providers vary greatly in strategy and collaboration with security experts when it comes to keeping data secure.

There is another important side of Big data as a service. It eliminates the limitation of traditional hub-and-spoke architecture, which cannot satisfy increasingly complex business analytics demands. Thus BDaaS is gaining popularity rapidly in business, e-commerce, e-service, and management in recent years.

Advantages Of BDaaS

Now let us point out the advantageous areas of BDaaS which can help to secure the future of business. Below are just a few of the advantages of BDaaS.

1. Reduce Costs

Establishing and maintain a Big Data environment like the Hadoop eco-system in business with in-house IT teams will surely a huge compromise of capital that business can use in other core areas of business operations. Not only cost but also it demands additional human resources. On the contrary, Big data as a Service is a third party provided service that eliminates this extra expenditure.

2. Proven Service Architecture

When a well-established BDaaS provider serves you, your Big Data applications are assured with proven technology architecture and very functional layers. It ensures the maximum efficiency for business applications. Thus the organizations will be able to leverage the advantages and true power of Big Data.

3. No need for Infrastructure Management

In –house, Big data infrastructure means a huge investment for administration, security, technical support, policy compliances, and other departments. This will be the burden of Big Data. Additionally, it needs complex software or hardware integrations in setting up and implementing BDaaS. However, this is not an issue for a well-established BDaaS service provider.

4. Technical Competence

When an organization is a business partner with a good BDaaS service provider, it will get expert advice on Big data services like using the right applications for mining the right data sets, getting the best insights, etc. In this case, there is no need to get hands-on large and unstructured data clusters; instead, one can more easily access relevant information to make strategic decisions.

What is the market outlook of Big data as a Service?

The global big data as a service (BDaaS) market is estimated to reach USD 51.9 billion by 2025, registering a CAGR of 38.7% over the forecast period, according to a new study by Grand View Research, Inc. The global market is anticipated to witness substantial growth owing to the increasing requirement of structured data for analysis and long-term data retention over the forecast period hybrid cloud segment is expected to register a CAGR exceeding 40% over the forecast period owing to the benefits it provides in terms of cost efficiency, scalability, flexibility, and security

Big Data as a Service Deployment Outlook (Revenue, USD Million, 2015 - 2025)

• Public Cloud
• Private Cloud
• Hybrid Cloud

Big Data as a Service Solution Outlook (Revenue, USD Million, 2015 - 2025)

• Hadoop-as-a-Service
• Data-as-a-Service
• Data Analytics-as-a-Service

Big Data as a Service Enterprise Size Outlook (Revenue, USD Million, 2015 - 2025)

• Small and Medium-sized Business
• Large Enterprise

Big Data as a Service End-Use Outlook (Revenue, USD Million, 2015 - 2025)

• BFSI
• Manufacturing
• Retail
• Media & Entertainment
• Healthcare
• IT & Telecommunication
• Government
• Others

What is the risk associated with BDaaS?

Big data initiatives to the cloud can be a timely and risky process when you start afresh or move your existing infrastructure. When an organization is opting for Big data as a Service, vendors may ask them to move data to the vendor’s storage system. Similarly, some vendors can leverage public cloud infrastructure and their ecosystems of compatible products. This is a question around data ownership, compatible tools, and vendor lock-in risks. So, it is crucial to fully understand the vendor’s security policies and procedures to keep the corporate data safe before entering into a legally binding contract. Additionally, you'll need to carefully consider the risks and benefits of leveraging private systems or a solution that leverages the public cloud. Finally, it's best to start small, ensure that the data is clean, the metrics are right, and the results are accurate before tackling a large and more complex big data project.