Nowadays, we are all familiar with big data, and as a hot industry, more and more people are devoted to big data industry. Many newcomers in learning will ask, "Do you need to know Python to learn big data? And what is the connection between them? Today we will take a look together.

Why do you need to know Python to learn big data?

Big data refers to the collection of data that cannot be captured, managed and processed by conventional software tools within a certain time frame. It is a massive, high growth rate and diverse information asset that requires new processing models to have stronger decision-making power, insight discovery power and process optimization ability.

And Python is recognized as a suitable language for big data. If you want to do big data development and big data analysis, you should not only use Java, but Python is also a very important core.

What is the connection between Big Data and Python?

After understanding Big Data, you will know that Big Data needs two steps if it wants to become an information asset: how to get the data, and how to process the data.

How the data comes:

Data mining has become the first choice of many companies, which can help them a lot in their business direction. Most of the companies are not capable of generating so much data, so they need to rely on data mining.

The web crawler is a traditional strong area of Python, the most popular crawler framework Scrapy, HTTP toolkit urlib2, HTML parsing tool beautifulsoup, XML parser lxml, and so on, are able to stand alone in the class library.

Web crawlers are not as simple as many people think, not just open web pages, parsing html so simple, college crawler technology can crawl thousands or even tens of thousands of web pages at the same time, while the traditional technology can not reach this level, the traditional threaded way of resource waste is relatively large.

Python can well support concurrent operations, based on this development of many concurrent libraries, such as Gevent, Eventlet, and distributed task frameworks such as Celery. ZeroMQ, which is considered to be more efficient than AMQP, was also provided earlier in Python. With support for high concurrency, web crawlers can really reach big data scale.

Data processing:

After mining the data, the next step is the need to go to processing, so as to help companies find the right data, data processing this piece of mostly used Python, Python as an engineering language, data scientists with Python to achieve the algorithm, can be used directly in the product, which is very helpful for many companies to save costs.

The above is about learning big data need to understand the content of Python, want to learn big data is not a short time to succeed, you need to have patience.