Everything You Always Wanted to Know About Hadoop Automation (But Were Afraid to Ask)

by April 9, 2019

I’m sure that every day you hear the same old story, Big Data could be a game-changer for your organization. A unique opportunity to harness massive volumes of structured and unstructured data, make faster decisions and deliver highly personalized customer experiences on a massive scale. Right?

And now you’re probably faced with that yellow elephant in the room, at the center of developer’s conversations, infiltrating your meetings, charming your boss and hiding between your budget lines – this is Hadoop and obviously, you cannot avoid it. 

So what is Hadoop exactly?

This is probably the question you don’t even want to ask your geek friend, as you’re sure the answer would either generate even more questions or bring on a light migraine. So, how to define Hadoop in simple non-geeky words? Well, let’s say it is a solution to common database problems you’ve been facing more and more frequently such as data that cannot fit into your tablespaces, SQL statements that take ages to complete or database schema that’s changing all the time.

In fact, Hadoop is an open source framework that has been designed to address the three Vs, those three main challenges of Big Data, known as Volume, Velocity, and Variety. Hadoop starts to make sense when traditional relational databases struggle to scale.

How does Hadoop work?

The principle of operation of Hadoop is pretty simple. The infrastructure applies the well-known principle of grid computing, which means dividing data storage and process execution on multiple nodes or clusters of servers.

Imagine you have a file way larger than your server capacity. You cannot store that file, right? But Hadoop lets you store files bigger than what can be stored on one single server by splitting data into chunks that are distributed on multiple nodes. So you can store huge files, and also many, many files – this piece of technology is known under the name of HDFS (Hadoop Distributed File System).

Ok, so what about processing data now? Everyone knows moving files over the network can be slow, in particular for really large datasets. But rather than moving data to be processed by the software, Hadoop uses a much smarter approach by moving processing software to the data. This is a programming paradigm named Map-Reduce (or Yarn). By analogy, you can think of map and reduce tasks as the way a census is made, the central administration dispatches its employees to every city. Each census taker has the task to count the population in that city and then return results to the central organization. The results from each city are then reduced to a single sum to determine the overall population. This mapping and distribution of processing, in parallel, and then combining the results is much more efficient than counting centrally in a serial fashion.

How to properly integrate Hadoop?

Specifically, Hadoop was made for developers. But the widespread adoption and success of Hadoop depend on business users, not developers.

The real challenge is how to integrate Big Data processes and Hadoop tasks into existing IT workflows without causing major business disruptions or using your data-knowledgeable resources inefficiently.

So most of the answer lies in the way you automate your data. Automation reduces complexity and enables organizations to achieve the scalability needed to fulfill the demands of Big Data processing. Moreover as scripting and application-specific tools are not scalable or practical at a time when the focus is on fast innovation, drag and drop user interfaces can make life easier for non-power users wanting to leverage existing templates and functions as well as create their own.

Wait, wait, wait, what’s new here? Large files, distributed storage, distributed processing, automated workflows, is that all that I need to know about Hadoop?

There’s barely not much to say, as, for many other data technologies, the integration process may spell the difference between success and failure for your Hadoop project. 

That’s why it is important to evaluate how well your automation solution can interact with Hadoop. No need to reinvent the wheel, as proper automation tools can provide the required centralized orchestration capabilities for integrating Hadoop within your existing business processes.

Nothing new, disappointed? You should not, didn’t you learn that there is an easy way to integrate Hadoop tasks without the additional stress of learning a multitude of new tools?