A quick peek into the world of data science tools: Open Source v/s Paid Tools
In a World that runs on data, one needs to be sure of the inferences we draw from it. Data scientists are geniuses extraordinaire who speak to data like no other. They are the data whisperers who make things happen on a global scale! What is it that they have that makes them all-encompassing and powerful? Tools! Useful data science tools that help them make sense of rows and rows of data. Which in turn decides the color of the packaging of the bar of soap you use!
These can either be openly available online or might need to be bought with a certain sum of money, thus termed as open source and paid tools.
Open source tools are ones performing very specific tasks. Their source codes are published online and are available free of cost.
Pertaining to the needs of the user, these can be modified from their initial design. Such tools are generally created in collaboration. Programmers work on the code and share changes, if any, with the community. On the other hand, paid tools are licensed commercially. They are available only after paying a certain amount of money.
The Controversy
The controversy in regard to the usage of software, whether open source or paid, has lasted since forever. Where companies often prefer using tools which are openly sourced, the choice largely depends on how well the business use cases are defined.
Paid tools are specific to certain domains. They are generally built considering specific use-cases. So, when end-to-end solutions are required to be built from scratch, open source tools are preferred. This is because of the flexibility they provide. Some such open source tools majorly used by data scientists are Zeppelin, Python, PySpark, Jupyter, Keras, TensorFlow, FastAI, Kubernetes, Dockers, etc. A few popular paid tools were Tableau, Amazon S3, AWS Glue, SageMaker, Redshift, and Batch.
Benefits of using Open Source tools
Many open source tools like Spark provide a fundamental framework based on which the user can alter the tools or can build algorithms. Another important factor which plays a major role in the selection of the appropriate tool is the fact that many professionals contribute to the platform. This helps in bringing out newer versions of the same on a frequent basis, making it popular in the community of data scientists.
Advantages offered by Paid tools
Even though open source tools are preferred frequently, paid tools have their own advantages. These provide better services and have algorithms which are inbuilt. Built on open source tools, paid tools are easily manageable and the organizations offer some support. There is some automation which is built-in, making it user-friendly.
One important factor that must be considered, though, is the cost or expense arising in the usage of any one of the two. Even though open source tools don’t have direct costs, they might include some hidden costs like that of internal labor.
Both open source and paid tools have their pros and cons. Thus, selection of the right data science tools depends on the requirements of the user. It is always advisable to first list down the specific needs and then select a tool accordingly.