You know Hadoop. You know about the cloud. But do you know why and how to run Hadoop in the cloud in order to supercharge your data analytics operation?

Apache Hadoop was born as an on-premise platform, and most of the use cases for early commercial Hadoop vendors – like Cloudera, Hortonworks and MapR – focused on on-premise implementations of the open source data analytics platform.

Hadoop in the Cloud

But alongside on-premise Hadoop environments, Hadoop-as-a-Service – meaning Hadoop running in the cloud – has become increasingly popular.

Versions of Hadoop-as-a-Service are now built into all of the major public cloud platforms, like Amazon Web Services, Microsoft Azure and Google Cloud.

You can also set up Hadoop to run in a private cloud either by configuring it on virtual servers yourself (though you should know what you’re doing because it’s necessary to work around challenges like breaking Hadoop redundancy by running multiple virtual servers on the same physical host) or by adopting a turnkey private-cloud Hadoop option, such as the one from Rackspace.

Why Run Hadoop in the Cloud?

There are several advantages to running Hadoop in the cloud:

1. If you use a turnkey solution or Hadoop-as-a-Service, there is very little setup to perform.
2. Hadoop-as-a-Service requires no maintenance.
3. If you lack the on-premise computing power to host a Hadoop cluster big enough to meet your needs, running Hadoop in the cloud will give you what you want without requiring new hardware purchases.
4. When using Hadoop in the cloud, you generally pay only for the time you use. That beats paying to maintain local Hadoop servers 24/7 if you only use them some of the time.
5. If the data you analyze is stored in the cloud, running Hadoop in the same cloud eliminates the need to perform large data transfers over the network when ingesting data into Hadoop.

Of course, these benefits come with trade-offs. The biggest is that by outsourcing your Hadoop environment to the cloud, you have less control over it.

Related: Strata + Hadoop World 2017 Recap: Machine Learning, Data Lakes and the Cloud

There could also be compliance issues to consider if you analyze certain types of data in a cloud-based Hadoop environment in the event that the data is subject to special privacy or access-control regulations.

These are drawbacks that you usually face when you use a cloud-based service of any type, however. For many people, the benefits of migrating workloads to the cloud outweigh the challenges. This is likely the case for you if you seek an easy way to run Hadoop without having to set up and maintain it yourself on your local infrastructure.

Using Syncsort to Achieve Hadoop-in-the-Cloud Bliss

No matter how you run Hadoop, one challenge that can significantly slowdown your productivity is the task of ingesting data into it. If you store data in unusual structures or in legacy mainframe environments that were designed long before anyone was thinking about Hadoop, offloading that data into Hadoop can be tricky.

It can be especially tricky if you run Hadoop in the cloud, where you have less control over exactly how your Hadoop environment is configured. In the cloud, you have to use Hadoop as the cloud vendors give it to you. That means you can’t tweak it in order to make it more friendly toward your mainframes in the way you could if you ran Hadoop locally.

With Syncsort’s Hadoop solutions, however, ingesting data into Hadoop from mainframes isn’t hard, even in the cloud. DMX-h streamlines the data offloading and ingestion process for you automatically. It allows you to focus on what matters most – deriving value from your data – rather than fighting with your data to get it into your Hadoop environment.

Legacy data in Hadoop causing unwanted roadblocks? Don’t miss opportunities to maximize the breadth of your data lake. Download Syncsort’s latest eBook, “Bringing Big Data to Life,”to learn trending insights on integrating mainframe data into Hadoop.

Let’s block ads! (Why?)

Syncsort blog