Since the past few years, Apache Hadoop has emerged as the biggest platform offering Big Data analytics. Despite of its huge potential, maintaining a successful Hadoop environment for retrieving valuable business insights is a difficult task for many organisations. Lack of infrastructure, insufficient expertise and improper implementation in maintaining large and parallel systems have made way for Hadoop as a Service (HaaS) providers. Businesses in acute need for managing the huge volumes of data through Hadoop ecosystem are pleased by the idea of outsourcing the task to HaaS providers.
HaaS providers offer unique features, services and support ranging from basic access to virtual machines and Hadoop software and from fully configured software to full service support options for optimum monitoring and maintenance. With so many different offerings, business must first evaluate their requirements and expectations from Hadoop to minimise the chances of failure in installing an apt Hadoop environment. Here are few considerations that must be discussed upon for choosing the right HaaS options:-
1. Data scientists and data administrators must find the HaaS equally beneficial. While data scientists prefer Hadoop services with no downtime to avoid the frustrating delays in starting clusters and reloading of data, system administrators require fully streamlined management consoles for performing various tasks by following minimum procedures.
2. HDFS is the basic format of storing data in Hadoop. And the data in different formats get converted to HDFS before getting stored. Persistently storing data in HDFS format helps in avoiding delays, besides being reliable and cost effective.
3. While selecting HaaS providers, the elasticity factor must also be considered as businesses today want services that can effectively handle the changing computing and storage demands.
4. Providing non-stop operations is yet another pertinent criterion as Big Data environments are complex, extremely large, dispersed and parallel systems. Such environments create challenges to operating conditions and HaaS efficiently deals with these grave obstacles by maintaining non-stop operations through Hadoop expertise and tooling.
5. The requirement for Hadoop expertise reduces when HaaS configures itself for types of nodes and optimal numbers. To assist data scientists and system administrators in managing seamless running of their workflows, an effective HaaS comes with self configuration to result in significant reduction in administration time and human error along with delivering faster results.
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.
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