Why is SaaS so popular among software development companies in India?

Software-as-a-Service (SaaS) has paved its place as the business service delivery model of everyone&

Software-as-a-Service (SaaS) has paved its place as the business service delivery model of everyone’s choice for custom software development companies - whether be it association or analytics. Yearly global survey of CIOs by (Gartner, 2014), found that 72% of responses indicated that respondents are already using SaaS, yet these results validated there is still high potential for greater adoption of SaaS. In another study (KPMG, 2014) specified that 50% of the executives surveyed identified cloud-delivered SaaS as their most likely investment area.

Core business functions, office and instant messaging software, project management software, design software and games, financial accounting software and financial payroll software are delivered by SaaS model.

SaaS is Popular for the Enterprise:

The popularity of SaaS is constantly rising because it makes deployment simple and assists in reduction of customer acquisition costs. With SaaS, service providers can support multiple customers with a single version of a product. This is called the multitenancy approach and it allows companies to scale as quick and as much the amount needed without replacing costly infrastructure or adding IT staff or assistance from software companies India.

SaaS is Popular with IT:

A recent survey of 1000 IT professionals by (Forrester, 2014) found that they are turning to SaaS products which are hosted, as a way to decrease the load of management of non-mission-critical applications such as HR and CRM. Plus, the subscription-based SaaS pricing model can help in maintaining the IT budget costs consistently or lower than packaged or homegrown software.

The reason why SaaS is popular are its benefits. These are some of the advantages of using SaaS model:

1. Time to benefit is reduced

In SaaS the software (application) is installed and configured beforehand. This is what, it makes it different from the traditional model. The user has the advantage of keeping a provision for the server for an instance on cloud and in few hours the application is ready for usage. This eases the time spent in installation and configuration for a software development company, and can lessen the problems that can get in the way of the software deployment.

2. Costs incurred are less

SaaS has a differential charging facility since it usually resides in a shared or multitenant environment which incurs lower hardware and software licensing costs, compared to traditional model.

Another advantage is that the customer reach in the market can be increased since it allows SMB to use a software that otherwise they would not use due to the high licensing cost.

Maintenance costs are reduced as well, since the SaaS vendor owns the environment for using the software and it is split among all customers that use that solution.

3. Integration with scalability

Usually, SaaS solutions are located in scalable cloud environments and have integration with other SaaS offerings. While in traditional model, a new purchase of software or a server is must. They only need to enable a new SaaS offering and, in terms of server capacity planning, the SaaS provider will own that.

4. Beta releases & upgrades

SaaS vendors provide the facility of upgrading the solution and then it becomes globally available i.e. for all their customers. The amount of costs incurred and the amount of effort put in, for upgrades and new releases are lower than the traditional model that usually compulsives the user to buy an upgrade package and install it, or the user pays for the specialized services to get the environment upgraded.

5. Easy to use and perform POC

SaaS offerings are easily usable since they come with best practices for software development companies and samples inside it beforehand. Users can do POC and assess the software functionality or a new release feature in advance. Also, they can have more than one instance with different versions and do a fluent migration from one version to another. Even for large environments, users can use SaaS offerings to assess and check the software before buy it.


In view of software companies in India, SaaS will remain the leading cloud model of the future. Smart businesses that are focus more on time and energy on their core objectives rather than delving in the IT’s unwanted services should evaluate whether the above benefits are aligned with their own corporate goals.


Forrester. (2014). The Public Cloud Market Is Now In Hypergrowth. Cambridge: Forrester Research.
Gartner. (2016). Worldwide Public Cloud Services Forecast. Stanford: Gartner.
KPMG. (2014). Elevating business in the cloud. Amstelveen: KPMG.


What are different types of Big Data as a Service (BDaaS)

The fame of Big Data lies within its wide-ranging definition of employing high volume, velocity, and

The fame of Big Data lies within its wide-ranging definition of employing high volume, velocity, and different data sets that are difficult to manage and excerpt value from. Clearly, most software development companies can identify themselves as facing Big Data challenges and chances today or in future. This, therefore is not a new issue yet it has a new quality as it has been aggravated in recent years. Cheaper storage and omnipresent data collection and availability of third party data has overtook the capabilities of traditional data warehouses and processing solutions. Businesses investigating Big Data frequently recognize that they lack the capacity to process and store it sufficiently. This shows either an inability to employ existing big data sets to the fullest or inability to expand their current data strategy with additional data.

Three strata of cloud computing as a service

Big Data as a Service is in the business of countless as-a-Service offerings. The most noteworthy ones that allow us to classify any subsequent services are threefold. Infrastructure as a Service (IaaS), e.g. virtual machines, networks, storage devices, or servers, is the most basic building block and includes everything (real or virtual) you would expect inside a data center. Above this level, exists the Platform as a Service (PaaS) which includes frequently employed software like web and database servers, or Hadoop and its ecosystem. At the next level is Software as a Service (SaaS) which are still nonspecific but more user interactive services like Email, CMS or CRM. Finally, past SaaS are usually domain or business specific applications.

Hadoop or a substitute distributed compute and storage technology at the platform level naturally builds the core of a BDaaS. Subsequently, any BDaaS solution includes the PaaS layer and may be SaaS and/or IaaS. This leaves us with four possible groupings for BDaaS:

  • PaaS only – focuses on Hadoop
  • IaaS and PaaS – focuses on Hadoop and optimizes infrastructure for performance
  • PaaS and SaaS – focuses on Hadoop and productivity & exchangeable infrastructure features
  • IaaS and PaaS and SaaS – focuses on total vertical integration for features and performance

Big Data as a Service Models


Big Data as a Service (BDaaS) offerings in the cloud can be categorized into one of four types:

Core BDaas

The core BDaaS implements the minimal platform, e.g. YARN and HDFS having Hadoop and a few popular services like Hive.  Amazon Web Service’s Elastic MapReduce (EMR) is the most noticeable core BDaaS and represents this model. EMR is one of myriad services in Amazon’s offering and EMR assimilates well with many of the other services just as NoSQL store DynamoDB or S3 storage. Users can combine them to build something like data pipelines to a comprehensive full company infrastructures around the EMR service. However, composability of its services being Amazon’s strength, also signifies that the core BDaaS offering is meant to stay nonspecific to interact with the other services.

Performance BDaaS

One way of vertical integration for BDaaS is in the downward direction to include an optimized infrastructure. This allows to get away with some overheads of virtualization and specifically build hardware servers and networks keeping in mind Hadoop’s performance needs.

Businesses are served, understanding and working with Hadoop that are rising, but are held back by scale and complexity. The software development company can outsource their infrastructure and platform needs and management around Hadoop to an infrastructure service provider. Business can then emphasize on putting Hadoop to work and the stack from SaaS upwards. A package pricing strategy based on storage and compute usage aims to remove common problems of choosing between performance and cost optimization, and give anticipated, fixed costs.

Feature BDaaS

The other way of vertical integration for BDaaS is in upward direction to include features past the common Hadoop ecosystem offerings. The feature driven BDaaS emphases on efficiency and abstraction to get users started with Big Data quickly. The feature BDaaS company’s services includes web and programming interfaces and database adapters pushing technologies like Hadoop into the background and their service reaches into the SaaS layer. In fact, Hadoop clusters are initiated, scaled and even stopped transparently according to the load requirement.

The feature method uses IaaS to provide computing and storage with a noteworthy difference. Being independent from a cloud provider allows a feature BDaaS to leverage computing and storage as a fully scalable and more importantly interchangeable commodity. Amusingly, the compute and storage from IaaS are pass through pay as you go and thus ideal for very variable, volatile, or exploratory workloads.

Integrated BDaaS

Finally, another possibility is a fully vertically integrated BDaaS that syndicates the performance and feature benefits of the previous two BDaaS. This is an interesting approach since it could result in the perfect BDaaS, which is productive and supports business users and experts, and provides supreme performance. Both feature and performance BDaaS are at initial stages and the integrated BDaaS could in practice turn out to be a good solution to this difficult problem.


As Big Data is growing as a topic, business and service models are evolving and we can see the similarities and differences between the three competing types of Big Data as a Service. The core BDaaS has been around for quite a time and is in use by many software development companies especially as part of a bigger architecture or for uneven workloads. It has settled as a model supporting the provider’s broader service architecture.

The feature BDaaS needs a proof to be competitive on a performance level, though the commoditization and service level generalization means that at the end of the day, winning of this model isn’t dependent on squeezing the most performance from comparable hardware, but on a dollar to dollar basis. The performance BDaaS, will face business demands from companies that diminishingly are willing to take on the complex challenges of building their own data architecture and linked SaaS layer, and progressively want to focus on their value adding domain specific processes.