Hadoop-as-a-service (HaaS) is a means for companies that don’t have the capacity to store and analyze massive amounts of data in-house to perform and benefit from big data analytics.

Hadoop is an open-source analytics framework that stores and analyzes big data in the cloud. But while any organization can use it free of charge, not all companies can create and maintain internal Hadoop environments. That would mean spending exorbitant amounts on storage devices and space, not to mention utilities, specifically electricity, and human resources, to operate and maintain the systems. The need for Hadoop and lack of resources and expertise gave way to the rise of HaaS.

HaaS users don’t need to invest in or install additional infrastructure on their premises to enjoy Hadoop’s benefits.

Other interesting terms…

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HaaS primarily helps organizations, big and small, harness the benefits of cloud computing and big data analysis without breaking the bank.

Benefits of Hadoop-as-a-Service

Using HaaS allows companies to enjoy these advantages:

  • Easy to use: Launching a Hadoop environment is easy, as organizations only need to choose a provider and start using the service. You can watch this demonstration video to see how HaaS works:
  • No need for designated operators: Anyone, even those who aren’t Hadoop experts, can use and maintain the software. Users don’t need a designated team as well since there is no hardware or infrastructure to manage. All of that is taken care of by their HaaS provider. Even issues encountered are handled by the provider’s team of experts.
  • Scalability: Like any other XaaS offering (e.g., SaaS, IaaS, etc.), HaaS users can add or remove servers if they wish to anytime.
  • Lower costs: Since HaaS users don’t need to purchase hardware and maintain them, they don’t need to spend on innovation as well. That allows them to enjoy Hadoop’s advanced features even without upgrading servers and such.

Criteria for Choosing the Right Hadoop-as-a-Service Provider

When selecting any cloud service provider, you should look as deep as you can before making a decision. In HaaS’s case, you should choose a service that:

  • Meets the requirements of both data scientists and datacenter administrators: Data scientists prefer functionally rich and powerful environments. They need a service that lets them start computing as soon as they log in and can’t be bothered by reloading data when needed.

System administrators, meanwhile, want streamlined management consoles so they can work quickly. It’s better for them as well to leave low-level monitoring to the HaaS provider.

  • Elastic and self-configuring: The HaaS provider should be able to handle elastic demand. The service level shouldn’t falter when its number of users peaks. And users shouldn’t be bothered with adjusting their configurations to address fluctuations. The HaaS application should also scale up or down on its own with the addition and deletion of storage and current users.
  • Supports nonstop operations: The HaaS provider should craft the best configuration parameters and monitor key operational metrics to ensure jobs run as expected.

Hadoop-as-a-Service Deployment Types

There are two ways to deploy HaaS—run-it-yourself (RIY) and pure-play. 

RIY solutions require users to have Hadoop skills since they need to intervene manually to handle huge workloads. 

Pure-play applications, meanwhile, give organizations a nontechnical interface so they can use Hadoop even if they don’t understand how it works in the backend. Configurations for changing data sizes are handled by the HaaS provider.

The leading HaaS providers include Amazon, Verizon, IBM BigInsights, Google Cloud Storage Connector for Hadoop, Qubole, and Altiscale.

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The global HaaS market value in 2019 was US$7.35 billion, a figure that is expected to reach US$74.84 billion by 2026. Given that expectation, we can say that HaaS is here to stay, so understanding it is crucial for companies.