Why Should My Company Use Hadoop Over Its Current Data Warehouse?

Or simply put: “What can Hadoop possibly do that my data warehouse can't already?”

A predictable and legitimate question following the “Why should my company use Hadoop after all?”.

Our today's post is not aimed at convincing you that you should, indeed, replace your current data warehousing solution and move your data over to a Hadoop platform. That would just point out the“why it's best to go with Hadoop”.

Instead, we're ready to answer your specific question: “Why should my company use Hadoop as a data storing and data processing solution?”

We'll be:

 

1. presenting you with specific use cases when Hadoop is, indeed, the best option
2. outlining key advantages of using Hadoop over the traditional data warehousing 
    

When Should You Consider Replacing Your Data Warehouse With Hadoop?

One of the most popular sayings here, at our Toronto web design company, is: “if it ain't broken why fix it?”.

Therefore, let us point out to you just 2 specific situations where you should consider a massive data migration to Hadoop as your best option:

 

1. you're dealing with a huge amount of data 
2. you need built-in capabilities for processing raw and semi-structured data... in a scalable way, of course
    

Does any of these contexts ring a bell to you? If so, you're better off with Hadoop.

 

"Why Should My Company Use Hadoop?" 7 Advantages Over Traditional Data Warehousing 

For it all comes down to the benefits that your company will gain from such a transition.

In this respect, we've put together a list of 7 key reasons why Hadoop is a great asset for your company.

Analyze them, weight them, compare them to the benefits that you're currently “reaping” from using your current enterprise solution and... do the math yourself:

 

1. It's Cost Effective: it's free actually

No, no, we're not trying to brush under the carpet costs such as:

 

• staff training investments that you should consider 
• commodity hardware costs to take into account for storing impossibly large sets of data 
   

And yet, they are insignificant compared to the costs that legacy commercial vendors products' come along with:

 

• annual support offered by the data warehouse's vendor (compared to Hadoop's open source support)
• perpetual licenses
• significant costs that each scaling process would call for (no wonder that companies used to get rid of loads of raw data since scaling their data warehouses to accommodate it all was cost prohibitive)
   

2. It's (so much) Easier to Use: skip formatting and “exploit” your data from day one

Here's an answer, which makes a strong argument itself, to your “Why should my company use Hadoop over my current data storage solution?”

Its ease of use feature will come as a major surprise to you once you've gone through a:

 

• changing formats
• complex preprocessing
• establishing data models
   

… type of experience with your current enterprise solution. An entire “ordeal” to go through just to be able to finally leverage your own data!

With Hadoop it's just a “feed the data” process! That's all! No preliminary steps to take.

And where do you add that you get to use all your familiar tools, languages and even to test the newest methods for getting the most value out of our data!

 

3. It's Flexible: it can capture data from a plethora of data sources

And this is gold when you have an entire ecosystem of data sources ready to deliver you data if you just have the right tool to... tap into!

Hadoop's perfectly suited for the job: it will access and extract data and provide you with valuable insights from sources ranging from:

 

• social media
• email conversations
   

… and lots of other “repositories” of both structured and unstructured data. It will go and get this heterogeneous load of data to you.

Data that will then fuel your marketing campaigns, your fraud detection initiatives, your log processing actions etc.

Do giants such Marks & Spencer and Yahoo and their own use cases of Hadoop make convincing enough answers to your “Why should my company use Hadoop?” question?

They're using Hadoop to:

 

• play the “personalization” card right
• put together cross-functional teams (IT, marketing, e-commerce, finance...) thanks to Hadoop's capability to seamlessly process all types of data
• gain a better understanding of their customers (this is where Predictive analytics comes into play) 
   

And this is what extracting value from your own data, that's just sitting there, waiting to be leveraged, really means.

 

4. It's Open Source Technology: bugs and feature development handled by multiple companies

Just try to compare bug fixing and new features development being handled by a single company (your commercial license vendor) to the same processes being carried out by hundreds of companies!

In other words: when choosing Hadoop as your data storage platform there's an entire community of contributing companies offering you support and continuously improving the platform.

 

5. It's Built With High Scalability in Mind: keep on adding more and more data clusters

How easily (or “costly”) is it to scale your current data warehouse to accommodate your increasing amounts of data?

Hadoop scales... organically, using low-cost hardware as a unique resource!

Here's how it works: 

 

1. as you add new and new heavy nodes (clustering thousands of terabytes of data)
2. Hadoop manages to seamlessly accommodate it all 
3. … and to distribute it across hundreds of inexpensive servers that run in parallel
    

Scalability is, undoubtedly, one of Hadoop's “five-star” features, the one that traditional relational database systems (RDBMS) can't possibly compete with! 

 

6. It's Fast: data processing at high speed

When you're questioning yourself “Why should my company use Hadoop instead of sticking to its current data warehousing solution?”, you might be thinking, in fact:

“How much faster than my current data warehouse can Hadoop process data?”

A lot faster!

And this is exclusively thanks to its unique data storage method: the data mapping & the data processing happen on the same server where data is stored.

This way mapping and processing massive volumes of unstructured data is no challenge for Hadoop at all: it will map it no matter where it might be located in a cluster.

And so processing it (we're talking about petabytes of data here) turns into a matter of hours!

 

7. It's Equipped to Handle Fails Remarkably: say Hello to automatic data replication!

You can run, but there's no way of hiding/completely avoiding cluster fails!

But luckily Hadoop provides you with a great “safety net” type of capability: it automatically replicates data for you, sending it to other nodes.

So, when faults happen (and they will), you can stay reassured: Hadoop will always have copies of your data ready to be passed on to other, non-compromised locations of your data infrastructure.

 

Final Thoughts

Or “recommendations” if you prefer:

 

• if it's small data that your company's “piled up” so far, if there are small files that you need to store and leverage, don't go for Hadoop
• if you don't really need to access and to process your unstructured or semi-structured data, there's no real need to use Hadoop
   

Now getting back to the initial question, “Why should my company use Hadoop over its current data warehouse?”, our answer is:

“Because Hadoop is built and being constantly enhanced with impossibly large amounts of data in mind!"