The universe of software technology is a quick developing one. New technologies are continually rising in the scene. If you need to positively shape the field, at that point you have to keep the pace with the most recent improvements in the field. The competency of software engineer is estimated by his/her insight into the most trend setting technology. In this blog, today, we are discussing two of the technologies that everybody is obsessed with - Python and Hadoop.
If you’re new into this field or working for a long time you must have heard comparisons, which language is better, which framework should we use and so on. But here we are catering to a never ending debate, Should I learn Hadoop or Python? Well well well, before we answer this for you why not take a look at both of these entities separately and then decide?
What is Python?
Indeed, as you may know, Python is a programming language used for general purpose coding. Python is a language that centers widely around code readability and meaningfulness. One of the most perceptible highlights of Python is the utilization of whitespace space to delimit code squares instead of utilizing wavy supports or watchwords. Another extraordinary favorable position of Python over comparative languages, for example, Java and C++ is that is permits communicating ideas in less lines. In view of these focal points, Python is one of the most generally utilized structure languages on the planet today.
What is Hadoop?
Hadoop, or as it is commonly called, Apache Hadoop, is a framework which is used for big data management and for storing big data, it is an open-source framework. You have two basic parts at the core of Hadoop – one is the Storage Part, known as Hadoop File Distribution System and the other is the processing part.
If we look at the base Hadoop Framework, it comprises of the accompanying modules:
- Hadoop MapReduce
- Hadoop Distributed File System
- Hadoop YARN
- Hadoop Common
Looking At Both Collectively
Hadoop and Python are not something very similar - not in any case or choices. Along these lines, since one doesn't supplant the other there is no point of 'OR'.
Hadoop is a framework that permits you to store, process big data using basic programming models. Even though nowadays it has advanced into an ecological system of devices and technologies that help you to work with Big Data. There are a ton of new tools that take a shot at Hadoop either one way or the other.
Along these lines, to get into the Big Data world, Hadoop is the best beginning platform. So, you need to think about Hadoop.
Presently Python, It has nothing to do with Hadoop ecological system. It is a general-purpose programming language much like Java or C++ yet is substantially more straightforward and simple.
The explanation python gets heard such a great amount in the Big Data and Data science circles is because it is truly adaptable and focuses on Code intelligibility and efficiency. Additionally, since it has its underlying foundations in C language, software engineers lacking a lot of Data science information can likewise use it to rapidly chip away at different data analysis projects.
Looking At Both Individually
Having discussed these two collectively, now let’s have a look at their pros and cons as separate entities:
Python Pros:
- Adaptable, Fast to develop and Flexible:
Python centers on code readability. The language is adaptable, perfect, and simple to use and learn.
- Has countless useful libraries:
You can discover a library for essentially anything you could imagine: be it machine learning or web development.
- Huge Community Support:
Python developers’ network is truly outstanding - it's extremely huge and dynamic. The support forums are full of python experts that are ever ready to help you out.
Python Cons:
- Speed Limitations
Python is slower than some other well-known languages because it is an interpreted language.
- Not Native to Mobile Environment
Android and iOS don't support Python as an official programming language. Python isn't native to the mobile environment.
Hadoop Pros:
- Speed
There’s no speed limit in Hadoop, unlike Python. Hadoop uses a distributed file system that enables the processing at a quicker rate.
- High Throughput
Because of HDFS, Hadoop offers distributed processing with ease resulting in high throughput.
- Financially savvy
Hadoop is a cost-effective option for big data storage. Generally, to control expenses, raw data is deleted once the job is done but with Hadoop, this raw data is still there for a long time so even if the organization is looking to migrate to another framework or platform, this feature of Hadoop proves to be of great help.
Hadoop Cons:
- Small Data Concerns
Hadoop is best known for big data management but when it comes to small data, Hadoop is not the best choice.
- Dangerous Functioning
Hadoop framework is built on Java and this can cause unexpected harms. It is because Java-built platforms are prone to cybercrimes. After all, Java is a widely-used programming language.
Should I Learn Hadoop Or Python?
Finally coming to the question, should I or should I not? Well, this question compares two different concepts and technologies of the analytics world. In any case, the fact that this correlation is reasonable is questionable. It resembles asking what tastes better, Tacos or Nachos. Confused about what I’m saying? Well, both Tacos and Nachos are eatables, both taste extraordinary, can be expended together and independently as well. It anyway comes down to inclination.
Same is with Python and Hadoop. Both add to analysis, both work extraordinary when used together. In any case, both fill various needs much of the time.
Python is extraordinary for machine learning tasks and statistical analysis. For the most part, it adds to the decision making part. Hadoop allows you to deal with and pre-process chunks of big data that can be used for business choices.
It comes down to how the line of the field would you like to be in when you get into analysis. Most likely experts ask you to get good knowledge in both. Python and Hadoop will help you in the future one way or the other.
We have discussed both these technologies separately as well as collectively to give you better insights. Choosing for any of it depends on your nature of work and your goals. We wish you all the luck for your future.