What is Big data as a service (BDaaS)

What is Big Data as a Service 

BDaaS encompasses the software, data warehousing, infrastructure and platform service models in order to deliver advanced analysis of large data sets, generally through a cloud-based network.

Big data as a service is the delivery of data platforms and tools by a cloud provider to help organizations process, manage and analyze large data sets so they can generate insights in order to improve business operations and gain a competitive advantage.

BDaaS = DaaS+ HaaS + data analytics as a service.

Benefits of BDaaS

Initially, most big data systems were installed in on-premises data centers, primarily by large enterprises that combined various open source technologies to fit their particular big data applications and use cases. But deployments have shifted more to the cloud because of its potential advantages. In particular, big data as a service offers the following benefits to users:

• Reduced complexity. Because of their customized nature, big data environments are complicated to design, deploy and manage. Using cloud infrastructure and managed services can simplify the process by eliminating much of the hands-on work that organizations need to do.
• Easier scalability. In many environments, data processing workloads aren't consistent. For example, big data analytics applications often run intermittently or just once. BDaaS makes it easy to scale up systems when processing needs increase and to scale them down again after jobs are completed.
• Increased flexibility. In addition to scaling systems up or down as needed, BDaaS users can more easily add or remove platforms, technologies and tools to meet evolving business requirements than typically is possible in on-premises big data architectures.
• Potential cost savings. Using the cloud may reduce IT costs by enabling businesses to avoid the need to buy new hardware and software and to hire workers with big data management skills. But pay-as-you-go cloud services must be monitored to prevent unnecessary processing expenses from driving up their cost.
• Stronger security. Concerns about data security kept many organizations from adopting the cloud at first, particularly in regulated industries. In many cases, though, cloud vendors and service providers are able to invest in better security protections than individual companies can.

Large enterprises lead big data as a service investment

As mentioned, the SMB market doesn’t account for the largest share of the Big-Data-as-a-Service market. Small- and medium-sized businesses only accounted for around a quarter of the USD 5,356.8 million value of the BDaaS market in 2018. However, during the forecast period, the small and medium-sized business segment is expected to grow fastest.

What is Data as a Service (DaaS)?

Data as a Service (DaaS) 

Data as a service, or DaaS, is a term used to describe cloud-based software tools used for working with data,such as managing data in a data warehouse or analyzing data with business intelligence

Data as a Service (DaaS) is one of the most ambiguous offerings in the "as a service" family. Yet, in today's world, data and analytics are the keys to building a competitive advantage. We're clearing up the confusion around DaaS and helping your company understand when and how to tap into this service..

Data as a service (DaaS) is a data management strategy that uses the cloud to deliver data storage, integration, processing, and/or analytics services via a network connection.

What are the benefits of data as a service?

DaaS increases the speed to access the necessary data by exposing the data in a flexible but simple way. Users can quickly take action without the need for a comprehensive understanding of where the data is stored or how it is indexed

Compared to on-premises data storage and management, DaaS provides several key advantages with regard to speed, reliability, and performance. They include:

• Minimal setup time: Organizations can begin storing and processing data almost immediately using a DaaS solution.
• Improved functionality: Cloud infrastructure is less likely to fail, making DaaS workloads less prone to downtime or disruptions.
• Greater flexibility: DaaS is more scalable and flexible than the on-premises alternative, since more resources can be allocated to cloud workloads instantaneously.
• Cost savings: Data management and processing costs are easier to optimize with a DaaS solution. Companies can allocate just the right amount of resources to their data workloads in the cloud and increase or decrease those allocations as needs change.
• Automated maintenance: The tools and services on DaaS platforms are automatically managed and kept up-to-date by the DaaS provider, eliminating the need for end-users to manage the tools themselves.
• Smaller staff requirements: When using a DaaS platform, organizations do not need to maintain in-house staff who specialize in data tool set up and management. These tasks are handled by the DaaS provider.

Data as a Service is one of 3 categories of big data business models based on their value propositions and customers:

• Answers as a Service;
• Information as a Service;
• Data as a Service.

Hadoop as a Service (HaaS)

 

 What is Hadoop as a Service (HaaS) ?

Well While world is busy in Saas,Paas or CaaS,Now new term HaaS is also gaining curiosity

Hadoop as a service (HaaS), also known as Hadoop in the cloud, is a big data analytics framework that stores and analyzes data in the cloud using Hadoop. Users do not have to invest in or install additional infrastructure on premises when using the technology, as HaaS is provided and managed by a third-party vendor.

Definition of HaaS

HaaS (commonly referred as Hadoop in the cloud), is a framework of Big Data Analytics. This framework analyzes and stores data in the cloud utilizing Hadoop. For using HaaS, there is no need to install or invest in extra infrastructures On-Premises. The technology of the HaaS is offered as well as handled by the third party. In other words, HaaS is a term, which defines virtual data analyses as well as storage in the cloud. It arises as an alternative to On-Premise Hadoop.

Features                                                         

• HaaS providers offer a variety of features and support, including:
• Hadoop framework deployment support.
• Hadoop cluster management.
• Alternative programming languages.
• Data transfer between clusters.
• Customizable and user-friendly dashboards and data manipulation.
• Security features.

Why HaaS As A Cloud Computing Solution?

Apache Hadoop as a Service when providing as a cloud computing solution is aimed at making medium and large scale data processing easier, faster, accessible and cost effective. To help a business focus on the growth perspective, the HaaS eliminates all the operational challenges that emerge while running Hadoop.

With outstanding features like unlimited scalability and on demand access to storage capacity and computing, cloud computing perfectly blends with this Big Data processing technology. More than the on-premise solutions, the Hadoop as a Service providers offer various distinct advantages as given below:-

1. Fully Integrated Big Data Software

Hadoop as a Service comes fully powered with the Hadoop ecosystem comprising Hive, Pig, MapReduce, Presto, Oozie, Spark and Sqoop. The HaaS also offers connectors for integration of data and creating data pipelines that coordinate with the working of existing data pipelines.

2. On-Demand Elastic Cluster

In accordance with the changes in the data processing requirements, the Hadoop clusters in the cloud scale up and down, thus providing more operational efficiency in comparison to static clusters deployed on-premises. Moreover, performance is improved as nodes get automatically added or removed from the clusters depending upon the size of the data.

3. Cluster Management Made Easier

Opting for cloud based HaaS offers a fully configured Hadoop cluster, thus relieving of the need to invest extra time and resources in setting up clusters, scaling infrastructure and managing nodes. 

4. Cost Economical 

One of the major reasons why Hadoop in the cloud is becoming immensely popular is its cost effectiveness. Businesses are not required to make investments in installing on site infrastructure and IT support and on-demand instances render 90 percent savings and payment has to be made only for space when used with auto-scaling clusters.