Collect, analyze and leverage your data and you shall... stand out in the digital arena! But how can you streamline the process? How can you automate it, “infuse” it with time efficiency and keep all data analysis safe from any type of subjectivity? You go for a big data platform, no doubt about it! And now the question that arises is: "to Hadoop or not to Hadoop"? Weight the top pros and cons of using Hadoop and find out whether it's the perfect fit for your own use case, for your specific data environment:
Why Should You Even Consider Using Hadoop? What Needs Does It Address?
“Because at one point your valuable data, which can always get turned into "pure gold", risks turning into nothing but a heavy weighting “burden”.
That's right, all that data currently “flowing” within your organization, generated by your network of digital business processes which are being carried out... might just get too challenging to collect at some point.
Not to mention that processing it, analyzing it, storing it and, moreover, “capitalizing” will get time-consuming if you're still relying on manual operations mostly. And cost-effective if you consider outsourcing your data handling processes!
And so, it's in precisely this context, for meeting precisely these needs that any organization has, at some point, that Hadoop “tempts” you with its functionalities aimed at:
- collecting data
- processing data
- analyzing data
- storing data, all data: raw data, structured data, “steamy fresh” data and “ages old” data
- extracting valuable insights from that data, on your customers, on the existing possibilities for improving your current business processes, on various emerging opportunities etc.
Yet it's true: every new technology adoption and integration into your organization's whole infrastructure of technologies, systems and efficiency-boosting tools does have its consequences. Good and bad.
The Pros and Cons of Using Hadoop
Key Advantages of Hadoop:
1. It's Cost-Effective
And when I refer to “cost-effectiveness” I do not mean just “money”, the resource you would otherwise need to invest for storing your high volumes of data.
I'm thinking of the “costs” that derive from the necessity to actually... delete your historical, raw data, due to lack of storage space and expenses.
Just try to imagine this case scenario here:
Your business grows, along with your goals, you simply restructure it from the ground up. Now you're in a situation where you need to refer to some of your old crucial data!
But you will have already removed it due to lack of storage capacity...
2. It Automatically Duplicates Your Data... Multiple Times
In a fiercely competitive digital world, your data's your most valuable asset.
So, in this analysis of the pros and cons of using Hadoop this feature, in particular, is of critical importance: the platform automatically generates multiple copies of the data it stores!
Take it as an enduring safety net to rely on in an increasingly vulnerable digital landscape!
3. It Enables You to Fully Capitalize Your Stored Data
It's all there, at hand, stored on your Haddop platform! All your data: both structured and unstructured, new and historical. Ready to be turned, at any time, from “raw” material into meaningfully, value-added insights!
4. It Collects Data From a Wide Range of Sources
Just think about all the resources of time and money that you would otherwise invest in collecting your data from a whole plethora of data sources generating it: social networking sites, emails, clickstream data...
Hadoop can practically collect it from any type of data source, then store it all one place and in a unified format...
5. It's Built to Process Terabytes of Differently “Flavored” Data at Top Speed
For it's not just about efficiently storing this valuable asset of yours, but about efficiently gaining access to it, as well. About quickly processing it and turning it into actionable insights!
So, it's definitely about data processing speed! And this is precisely what Hadoop does: it accelerates data processing, thanks to its distributed file system.
The stored data and the data processing tools share the same servers, so speed for processing significantly large data sets comes... right out-of-the-box!
6. It Enables You to Do Data Analysis In-House
I couldn't have left out precisely this aspect from my analysis on the pros and cons of using Hadoop, now could have I?
It's all about achieving agility within your organization! Why should you be tied to a service provider that you would outsource all your data storage and analysis processes to, when you can carry them out... in-house? By leveraging Hadoop's capabilities...
This way you have your whole data cluster right at hand and you're free to customize the resulting insights to your liking.
Limitations of Hadoop
1. It's Not a Perfect Fit for Small Data Sets
In other words: if it's a small data environment that you're planning to “exploit” Hadoop's capabilities across, you'd better rethink your choice! For you might just not be able to use them to the fullest!
The platform's not suited for small data sets, but for very large ones instead. And since it's just large businesses that can generate big datasets and which are capable to fully leverage all of Hadoop's functionalities... it might just not be a perfect fit for your use case.
2. It Runs on Java: It Makes You Think of All The Java Vulnerabilities
When it comes to the pros and cons of using Hadoop, this disadvantage here might just weight heavy on your choice making process.
When you say “Java,“ you say:
- one of the most popular programming languages
- one of the most vulnerable to cyber attacks programming languages
… and Hadoop is built entirely on Java!
3. It Has No Data Securing Features Whatsoever
So do not expect a pre-built set of preventive features. For Hadoop has none... you're all alone when it comes to implementing the right security measure for protecting your data!
4. It's a Batch Processing System: Not Fit for Low-Latency Queries
No one can deny this key capability that Hadoop's been “supercharged” with: it can cut through huge clusters of data, process it and deliver answers remarkably fast.
Yet, it's not ideally equipped to:
- handle queries received on a site
- handle real-time systems
… or other low-latency queries.
It's your use case that will “dictate” your choice in the end. If, after running through all these pros and cons of using Hadoop, you realize that Hadoop's benefits can't outweigh its limitations, then you might want to consider other data storage solution instead.
But if functionalities such as automated data duplication, impressive capabilities to handle massive loads of data (and we're talking about really large sets here) and top speed in processing data are some key issues for you, then... you're better off with Hadoop!