Update your Hadoop (Big Data on-premise) to Cloud

Cloud platforms benefit businesses greatly by bringing cost-efficiency, ease of management, and simplicity to their systems. Apache Hadoop, a widely used open-source data processing and storage system, stands to gain a lot by shifting to cloud. This article will help you understand the pros of this migration and how you can achieve it.

What is Apache Hadoop?

Hadoop is an open-source data storage and processing framework that allows its users to connect multiple clusters to process large volumes of datasets that may vary from gigabytes to petabytes in size.

Apache Hadoop cluster framework is designed according to the master-slave architecture. Herein multiple nodes are involved. One of the connected machines acts as the master while others work as slaves. The Apache Hadoop cluster can analyse structured, semi-structured, and unstructured data. Moreover, the cluster works on the basis of the Shared Nothing system, i.e., nodes do not share anything among them except for the commands from the network.

There are four key Hadoop modules:

• Hadoop Distributed File System
• Yet Another Resource Negotiator
• Map Reduce
• Hadoop Common

Each Hadoop module performs a distinct yet critical function.

Reasons Why You Should Shift to Cloud

Some significant benefits of shifting to Cloud Apache Hadoop have been listed below to make the case.

Cost-effectiveness

It is safe to say that cloud-based Hadoop is less expensive to maintain and manage than the on-premise one. It is so because storing data on premises requires a lot of servers, and the hardware also demands a large facility to accommodate that many servers. Not to mention, the high electricity consumption also adds to the organization’s expenses. However, there are no headaches like these when a cloud-based Apache Hadoop is in the picture.

Cloud best handles rising data demands

A cloud platform brings scalability to the system by accommodating the gradual increases in the volume and demand of data. Hadoop supports both structured and unstructured data. There is no need to add more servers to the facility. Neither is there the need to expand the storage capacity of the existing servers, which takes up a lot of time, capital, and effort.

Incorporated support

Most cloud providers like Google Cloud platform have built-in support systems (for example, Dataproc) for Apache Hadoop that maximize productivity and improve performance. As a result, data engineers don’t have to make many changes to existing jobs to get the new tasks done.

No worries about hardware configuration

Since there is no physical hardware on the site, the IT personnel don’t have to do the additional work of carrying out hardware configuration. Clouds provide relief to the users by allowing them to determine the configuration remotely. This setup eventually makes resource allocations and cluster performance.

Higher productivity

Cloud-based Apache Hadoop increases productivity considerably by ensuring data accessibility. Unlike on-premise Hadoop, the cloud platform does not limit how and when the data can be accessed. Hence, data analysts and engineers can consume data whenever they like and wherever they want.

Increased collaboration

Multiple personnel and teams can cooperate with one another on projects with the help of cloud-based Apache Hadoop ecosystem. It is often an unattainable luxury in the case of the on-premise hardware setup. There is no need to update the system files manually, as it keeps on happening automatically. This virtual collaboration also saves the teams from wasting time and inconvenience.

Less complexity in configuration

Users can generate as many clusters as they want to run specific jobs through the cloud platform. It is not possible with on-premise Hadoop as there is only one cluster serving different purposes. In this way, the dependency on a single cluster is also removed, which is often bound to run into complexities.

How to Do it?

Let’s walk you through how you can update your on-premise Hadoop to the cloud while capitalizing on its winning points.

Bucket your data

Using a regular internet connection, you can transfer your data to Cloud Storage buckets. Several tools can be used to materialize the transfer to clouds like Azure, Google Cloud Platform, etc. The speed of migration depends upon the Network Bandwidth you are using and the amount of data (in Terabytes) you are shifting.

Transferring data offline

One can also use tools like Data Box of Azure to migrate data offline. It involves shipping that takes place between the organization and the Datacenter. The data stays encrypted throughout and is well-protected against security breaches.

Testing

It is always wise to experiment a bit before you settle in for good. You can start off by using a small subset of data to run jobs and test their performance. Make adjustments with the in-built tools available on the cloud to formulate new strategies.

Move to specialized clusters

On-premises Hadoop ecosystem involves working with persistent and uniform clusters. However, the cloud version affords ephemeral clusters that are specialized, versatile, and terminable.

Conclusion

To sum up, it can be said that a cloud-based Apache Hadoop is best suited to meet the complex requirements of modern workflows. It is significantly easy to make the transformation to the cloud. The shift surely pays off as the cloud platform introduces simplicity, easiness, and versatility in organizational workflows.

Author bio:

Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and data analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.