Avoid these mistakes while using Python for Big Data

Python – A true friend of web developer: Python is one of those old languages which were developed to give tough competition with C and C++. No need to say, it is highly inspired from the object oriented programming approach of C++ with some enhanced features within it. Due to its similarity with C and C++, it can be used for developing machine programming, mobile application development, desktop application and web application too. Its motto is “Batteries included” indicates its potential to develop huge applications at a very less time due to presence of all necessary and common methods and modules. Hence, Python gets maximum priority from the software developers while developing any big data application. It has the potential to develop big applications in small time with maximum efficiency. At the same time due to its large library, often the web developer miss to use the inbuilt optimized functions which creates trouble in future like performance issue, optimisation issue and synchronization issues. Let us have a quick look over these common mistakes made by the Python developers while dealing with big data projects.

Keep track of data types & Schemata: Sometimes a Python developer when projected into an environment which is heavily influenced by database accesses, he fails to determine the proper data type of each and every data of the database. Well, we can say it is a careless behaviour or over confidence of the web developer who think that the data type of some variables are what he supposes to; but in reality, it may have a different type. So the Python developer should come out of his perception and double check each and every data types before implementing the query in the application.

Manual integration with heavier technologies and scripts: Sometimes the web developer has to deal with huge amount of data like hundreds of GBs. At that time, the Python developer makes a peculiar mistake i.e., they try to fetch and analyze those huge data from the Python environment. They should understand that Python is a scripting language and even if it is optimised but still it is not a good idea to analyze such a huge data. So the Python developer must use a third party faster framework to perform the heavy lifting on the data and perform the analysis through the framework. So for such heavy technology or scripts (database) the developer must take the help of frameworks.

Time and Timezone should be handled properly: It is a common issue of Python developers while dealing with time and timezone for any application. Though the developer can extract the time using date time parameter but it must be converted to the local time using proper time zone method. For the same, the Python developer must implement timestamp concept in the code.

Tune for Performance: When the web developer develops an application he should focus on different bottle necks of the application because it determines the optimization standard of the application which affects the end user experience. So he must optimise the code while developing the application and should try to optimise it by analyzing the response time of the application.

Proper testing is a must: We have to accept the importance of testing in the software development lifecycle and it should be taken very seriously while dealing with big data applications. The developer should test the functionality of each and every function but at the same time, he should validate all the fields and should test the optimisation of the application thoroughly.

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Created on Dec 31st 1969 18:00. Viewed 0 times.

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