Many students know that as more fire python programming language, artificial intelligence need it. In addition to artificial intelligence, map zero Xiao Bian said that large data also need it.
Since in 2004, python usage increases linearly. January 2011, it was TIOBE list of programming languages named the 2010 language. Due to the simplicity of the Python language, readability and scalability, to do scientific computing with Python in a growing number of foreign research institutions, universities have adopted some well-known professor at the Python programming course.
Data is assets. Big Data engineers are now very hot, high-paying jobs. Big data development and analysis not only to use Java, Python is more important language.
Well, today we have to analyze, Python is to the meaning and role of big data.
Big Data Internet is now a hot term, and big data and keywords more closely believe that Java and python, and a year ago, the data may be large Java tagline many training institutions. By 2018, a large python has become the trend of the data, regardless of the industry bigwigs also, or training institutions are beginning to say that the big python data, and this is why? If you are interested in big data development, want to learn the system big data you can join the big data exchange technology to learn buckle group: 189 + 307 digital 522+ digital, private letters administrator can receive a free development tools and entry-learning materials
Why Big Data to learn python? What is big data?
Big data refers to data sets can not be captured, managed and treated with conventional software tools within a certain time frame, is the need for new processing mode in order to have more decision-making power, insight found massive force and process optimization capabilities, high growth rates and diverse information assets.
Why is python Big Data?
Seen from the Wikipedia describes big data, big data would-be information assets requires two steps, first, how to data, and second data processing.
How data?
Data on how this problem, data mining is no doubt that many companies or individuals preferred, after all, most of the companies or individuals are not capable of generating so much data, only data mining on the Internet.
Web crawlers are Python's traditional areas of strength, the more popular reptile framework Scrapy, HTTP toolkit urlib2, HTML parsing tools beautifulsoup, XML parser lxml, etc., are able to work independently in the library.
Of course, the web crawler not just open a Web page, how simple parsing HTML. Efficient reptiles to be able to support a large number of concurrent operations flexible, often thousands or even tens of thousands of pages simultaneously while crawling, the traditional way of the thread pool is relatively large waste of resources, the number of threads after thousands of system resources substantially on the whole waste thread scheduling on.
Python since well supported coroutine (Coroutine) operation, based on this developed many concurrent libraries, such as Gevent, Eventlet, as well as distributed task framework Celery like. Is considered to be more efficient than the earlier AMQP of ZeroMQ also provides a Python version. With support for high concurrency, web crawlers can really achieve large scale data.
data processing:
With big data, then also you need to be addressed in order to find their own data fit. In the data processing direction, is one of Python data scientists prefer the language, it is because Python itself is an engineering language, data scientists with the algorithm implemented in Python, can be directly used in the product, which for big data startups cost is very helpful.