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Is Hadoop still relevant: Is it our future, or does it belong to the past?

Picture of Mikołaj Kromka, Principal Software Engineer

Mikołaj Kromka

Principal Software Engineer

20 minutes read

The amount of data that businesses deal with shows: Data solutions are a necessity. In their 2016 Data & Analytics Survey, IDG reported that the average company has 162.9TB of data, while the average enterprise has as much as 347.56TB of data.

The report also claimed that the average company believed their amount of data would increase by 52% within the next 12-18 months, while the average enterprise believed theirs would increase by 33%, to 461.25TB.  

Now, those numbers are bound to be significantly higher as companies are acquiring more and more data in short amounts of time. That’s why so many companies rely on data solutions. They provide data automation, organization and reliable storage of sensitive information. 

Hadoop was the first data framework to allow custom data transformation for large datasets and paved the way for many other data solutions. These solutions began gaining popularity, offering certain features that Hadoop didn’t. So as businesses choose solutions such as the cloud more frequently, does that mean Hadoop is slowly leaving the picture? 

In this article, we look at the pros and cons of using Hadoop, whether cloud data platforms may or may not be better and what the future holds for Hadoop.

Who uses Hadoop?

Hadoop is used across all industries, from banking and logistics to retail and airlines. Each industry has its preferred way of using Hadoop. For example, while some retail companies like to have a large variety of data sets and tables, banks focus on simplicity.

How Hadoop is used varies depending on the data types a company has. Logistic companies use it to optimize transport to be efficient and lower costs. Insurance companies rely on data from various sources to determine the offer they will give their clients.

On the other hand, a company that sells car parts can use this solution to study trends in the data to determine how much of a product they need in stock. Despite the differences in the use of data, the goal of using Hadoop is the same across all industries: to make smarter, data-driven decisions. 

Since the Hadoop ecosystem is a big data solution, it is typically used in larger companies. These companies have large amounts of data and the resources to create a cluster. Hadoop requires a physical server room to store the framework’s hardware that needs to be running 24/7, as well as a team of administrators.

Start-ups and smaller companies do not have the means, or sometimes even the need, to get Hadoop, so they rarely use the tool. However, with large companies and corporations, Hadoop is a popular tool that is trusted and relied upon heavily. 

The tool has direct and indirect users when a company has a Hadoop cluster. Direct users consist mainly of technical teams. These are data engineers, data science engineers, data scientists, data analysts and administrators. The number of direct users will vary depending on the company or industry.

For example, because banks have such sensitive data, they usually limit direct contact with Hadoop only to the necessary technical team members. The rest of the company’s employees are indirect users–business analysts, project managers and anyone who receives a prepared report. 

Benefits of using Hadoop

Hadoop was the first data framework that came on the scene, and it still continues to evolve as a technology. It offers new updates and features to meet its users’ needs. Initially released in 2006, Hadoop was a tool created in response to the challenge of data storage. That’s why, at first, its primary purpose was to provide companies with a way to store and sort out data in a distributed fashion using a cluster of computers, and that’s all it did.

Then, as companies were dealing with much more data and their needs changed, Hadoop also changed. It then began offering more features. Now, Hadoop enables predictions and forecasts that help companies run efficiently and successfully. This evolution is still present today, as more modern versions of Hadoop are becoming available.   

This dedication to evolving and meeting the needs of its users resulted in Hadoop becoming a well-developed ecosystem. That’s why the Hadoop ecosystem has grown into a mature technology throughout the years. It knows its users, the market, and the challenges they have faced and continue to face.

This knowledge enables the Hadoop ecosystem to meet its users’ needs and help them quickly make data-driven decisions. The result is that Hadoop users trust the tool and view it as reliable.  

One unique feature of the Hadoop ecosystem is data locality, which means the data is stored on the same machines that perform calculations. When there is no data locality, data needs to be downloaded from one machine (for example, a database) to another one which performs the calculations.

Therefore, data locality saves a significant amount of time and bandwidth. When Hadoop users transfer data, they don’t move the actual data but rather the computation of the data. Code is much smaller than actual data and, therefore, much easier to transfer.

The limitations of the Hadoop ecosystem

Although Hadoop is a mature technology with certain benefits, it also has multiple limitations that make some businesses turn to other data solutions. 


One limitation of Hadoop is the data’s security. It’s possible to have top-notch security in Hadoop. However, for that to happen, you need a large team of skilled experts, which is expensive. Since not all businesses can afford the luxury of having such a team, many Hadoop users are stuck with not-so-strong security. 

Unlike the optional security team, a team of Hadoop administrators is necessary. They are essential to ensuring Hadoop is running smoothly and fixing and maintaining it, so the stored data is intact. Like hiring any IT professional, hiring Hadoop administrators can be expensive.

Hadoop is a piece of software with security fixes and new features in upgrades. A team of Hadoop administrators will understand what features are needed to put in appropriate work and timelines for upgrades. That’s why a dedicated team is necessary to implement any fixes and upgrades properly. 

Another high-cost element of Hadoop that all users must take into account is the manufacturing cost. Hadoop is hardware that requires at least one server room, which does not only equal high electricity costs. It also means Hadoop users must spend a lot of money updating and fixing the machines. 

All in all, Hadoop requires a lot of money to run properly. 

Working in real-time

One major limitation of Hadoop is its lack of real-time responses. That applies to both operational support and data processing. If a Hadoop user needs assistance with operating the Hadoop software on their server room machines, that assistance will not be provided to them in real time.

They have to wait for a response, which can impact their work. Similarly, if a Hadoop user needs to analyze some data to make a data-driven decision quickly, they can’t. In Hadoop, there is no data processing in real time. That can pose a challenge in high-paced environments where decisions need to be made without much notice.


Hadoop can also be challenging to scale. Because Hadoop is a monolithic technology, organizations will often be stuck with the version of Hadoop they started out with. Even when they grow and deal with larger amounts of data. If they want an upgraded version of Hadoop, they have to replace their entire setup, which is expensive.

They either have to replace their entire setup or decide to run a new version of Hadoop on an older machine, which requires more computing power as well as the business to maintain these machines on their own. Since Hadoop users have to deal with fixing all the components of a cluster instead of just one, it will be more time-consuming and costly. 

Other minor limitations

Other limitations of Hadoop include a lack of data lineage and trouble with storing metadata. Without data lineage, Hadoop users don’t know where a particular piece of data originates from and the places it moves to over time. Metadata is data about pieces of data.

With no metadata, Hadoop users don’t have information about the context and the purpose of a particular piece of data. Hadoop has some ways to store metadata, but unfortunately, these tools are a bit old and don’t always work well. Additionally, there is a lack of reverts on Hadoop.

Cloud computing vs Hadoop

As the first data framework, Hadoop paved the way for cloud data platforms, which then, in a way, became its competition. Since Hadoop was such a breakthrough technology and cloud data platforms are its modern alternative, the two are often compared. 

Can two technologies, with the same goal of storing and analyzing large amounts of data, be so different from each other?

Let’s take a look.


Flexibility over solution size

Unlike Hadoop, cloud data platforms are not a monolithic technology, so they offer more power and flexibility over how the data is stored. With a cloud solution, you can easily increase or decrease the size of your solution whenever your business needs change. That also means that the cost of using cloud solutions is flexible and can dynamically change based on your needs.

Cloud solutions offer the pay-as-you-go model, eliminating the risk of paying for things users don’t use. It is the opposite with Hadoop.

Once you have a Hadoop cluster, you have it and are pretty much stuck with the size you started with, regardless of how much data you’re dealing with. To increase or decrease the amount of data storage available, a company must increase the size or replace the entire Hadoop cluster, both of which are expensive and complicated. And decreasing and repurposing purchased hardware is borderline impossible in corporate environments.

So while the cloud offers this flexibility, Hadoop does not. 

The cost of storing data

Right now, storing and accessing huge amounts of data on the cloud can be much more expensive than storing them on Hadoop. In fact, the usual case with cloud data storage is having to pay per read and for every written operation. However, the cost of using the cloud will continue to decrease, and the cost of using Hadoop will increase.

The more people use cloud solutions, the cheaper they become. Meanwhile, with Hadoop, the cost of electricity used to keep the server room up and running and the cost of wages of hired Hadoop administrators will both increase over time. 

Comparing the features

The cloud is a more modern solution and therefore offers more modern features to the user. In fact, Hadoop has been around for so long that it is sometimes regarded as a legacy technology. One of the most modern features offered by the cloud is reactivity in real-time, something Hadoop lacks.

Hadoop carries out data processing in batches, which means it deals with large amounts of data all at once, and this is usually data that has been stored for quite some time. On the other hand, cloud solutions carry out data processing in real-time and on demand. They react to data collected in real time and continuously generate reports based on it.

Whenever a user needs a prediction or intelligent reporting based on data, ML algorithms as a service will deliver it in real time. 

Compared to Hadoop, the cloud has more features and offers users more possibilities. One example of such a feature is remote access to the data. Data stored in the cloud can be accessed from anywhere in the world with the same speed, unlike data stored in Hadoop, which is located in a physical server room. This means that although data stored in Hadoop can be accessed from anywhere, it’s not replicated to different regions, and results in a slower connection with distance increase.

Cloud solutions also seamlessly integrate other existing resources, such as storage, buckets, and eventing. This is especially simple when a company uses a public cloud and most infrastructure is already present.

Modern vs mature 

The modernity of the cloud can also be a disadvantage. Since cloud data platforms are pretty new, they have many bugs that a mature technology like Hadoop does not. It has certain old technologies that the IT world has become familiar with, which means it can be more predictable.

All of the possibilities that cloud platforms offer can be great in terms of an innovative approach to data, but they can also make things a bit complex for the user. However, if someone wants to build a more modern solution in Hadoop, things can also get complicated. This is because the user would be stuck with setting up most of the tools independently since Hadoop is not equipped with modern tools.  

Still, maintaining a cloud data platform can often be much simpler than Hadoop. A business using a cloud solution only has to pay a subscription fee and does not have to worry about upgrades or the possibility of needing to scale or switch to a different version of the solution.

While Hadoop users need to hire a team of administrators, cloud platform providers offer their users professional support. That being said, both data solutions require an IT service partner for the tool to work properly and successfully help the business achieve its intended goals.

The Future is Hybrid 

Despite its many limitations, Hadoop will not be replaced entirely by cloud data platforms. Because it’s been around for so long, Hadoop has become a solution businesses have learned to trust. The way it works is familiar, and its limitations are known, while cloud data solutions are still pretty new.

That’s why some businesses are hesitant to let go of Hadoop completely. With it, they know what to expect. The familiarity of a tool can be especially significant when dealing with something as sensitive as data. That’s why Hadoop will continue to stick around. 

Hadoop is the preferred solution of large organizations, while cloud data platforms might be better suited to a wider range of businesses. That’s why certain companies will continue to choose one or the other based on their needs and preferences.

However, there is a way to get Hadoop’s mature technology and the cloud’s modern features to get the best of both worlds. This can be done by combining the two into one hybrid data solution. 

The way a hybrid model works is that Hadoop is on the bottom of the cloud platforms and users migrate the data between the two. Most of the data is on Hadoop, while data sets of manageable size are moved to the cloud so that users can use the cloud’s features on those data sets.


The hybrid model is more reactive than Hadoop alone and provides real-time predictions. If a company has a lot of data on Hadoop, it will be difficult to migrate it all to the cloud. That’s why this model can meet the needs of companies that already have Hadoop but want to try a more modern solution.

This is also a money-saving option for companies that want to use a cloud data platform but have enormous amounts of data, as storing all that data on the cloud alone is expensive. 

As business needs and market trends change, Hadoop and cloud data platforms will evolve together. It will be less and less visible, but it will remain a solution companies rely on. 

As much as data platforms are significant to meeting business goals, so is knowing how to utilize these tools to their full potential. When companies get a data platform, they risk turning it into data silos or so-called “data trashcans” instead of leveraging data for business value.

Working with an IT service partner eliminates the risks and the guesswork of working with a data platform. An IT service partner will introduce a data culture into the company so that the data-driven decision-making approach is present at all levels. Additionally, an IT service partner will provide all necessary technical expertise based on a company’s particular data needs and expectations. They can build data-intensive applications based on business goals and the team’s skill set.

Curated by

Sebastian Synowiec

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