Four Basic Ways to Automate Data Extraction FineReport | BI Reporting and Dashboard Software
Four Basic Ways to Automate Data Extraction

Four Basic Ways to Automate Data Extraction

Where to Get the Data?

Many people have experienced data analysis, but in the first step of data analysis, how to obtain data is an important step. Data extraction is the basis of data analysis. Without data, analysis is meaningless. Sometimes, how many data sources we have, how much data we have, and how well the data quality will determine what happens to our output. We need to consider that the trend of a data is influenced by multiple dimensions. We need to collect as many data dimensions as possible through multi-source data collection, while ensuring the quality of the data so that high-quality data analysis results can be obtained. There are many tools for data analysis, such as FineReport, Tableau, Power BI, etc.
So, from the data collection point of view, what are the data sources? I have divided the data source into the following four categories. 1.Open data source (Government, university and enterprise) 2.Crawler scraping (Web and application) 3.Log collection (Frontend capture backend script) 4.Sensor (Image, speed, thermal) These four types of data sources include: open data sources, crawler scraping, log collection, and sensors. They all have their own characteristics. Open data sources are generally industry-specific databases. For example, the US Census Bureau opened up data on population information, regional distribution, and education in the United States. In addition to the government, enterprises and universities will also open corresponding datasets. It is important to know that many studies are based on open data sources. You need the same data set to compare the quality of the algorithm. The third type of data source is log collection, which is the operation of the statistical user. We can track the event at the front end, collect scripts and statistics on the back end, and analyze the access of the website. Finally, the sensor, which basically collects physical information. Such as images, videos, or the speed, heat, pressure, etc. of an object. Since the main emphasis of this paper is data collection, this method will not be described. Now that we know that there are four types of data sources, how do you collect them?

How to Use Open Data Source?

The following table shows some authoritative open data source.
If you are looking for a data source in a certain field, such as the financial sector, you can see if the government, universities, and enterprises have open data sources.

How to Use Crawlers to Scrape the Data

Crawlers scraping should be the most common way, such as the evaluation data you want for the restaurant. Of course, we must pay attention to copyright here. Problems, and many websites also have anti-crawling mechanisms. The most straightforward way is to write crawler code in Python, of course, you need to learn basic syntax of Python. In addition, PHP can also write a crawler, but it is not as good as Python, especially when it comes to multi-threaded operations. In a Python crawler, you basically go through three processes. 1. Crawl content using Requests. We can use the Requests library to crawl web page information. The Requests library can be said to be an excellent tool for Python crawlers, which is Python’s HTTP library. It is very convenient to crawl the data in the webpage through this library, which can save us a lot of time. 2. Parse the content using XPath. XPath is an acronym for XML Path. It is a language used to determine the location of a part of an XML document and is often used as a small query language in development. XPath can be indexed by elements and attributes. 3. Save your data with Pandas. Pandas is an advanced data structure that makes data analysis easier, and we can use Pandas to save crawled data. Finally, it is written to the database such as XLS or MySQL through Pandas. Requests, XPath, and Pandas are three useful tools for Python. Of course, there are many other tools to write Python crawlers, such as Selenium, PhantomJS, or Puppteteer. In addition, we can also crawl the webpage information without programming. Here are three commonly used crawlers.

import.io

The most compelling and everyone thinks that the best feature is called “Magic”, this feature allows users to automatically extract data by entering only one web page, without any other settings.

parsehub

ParseHub is a web-based crawling client tool that supports JavaScript rendering, Ajax crawling, cookies, Sessions, etc. The application can analyze and retrieve data from a website and convert it into meaningful data. It can also use machine learning techniques to identify complex documents and export them to JSON, CSV, Google Sheets, and more.

Web Scraper

Web Scraper is a Chrome extension that has been installed by more than 200,000 people. It supports point-and-click data grabbing, supports dynamic page rendering, and is optimized for JavaScript, Ajax, drop-down drag, and pagination, with a full selector system, and supports data export to CSV and other formats. In addition, they also have their own Cloud Scraper, which supports scheduled tasks, API management, and proxy switching.

How to Use Log Collection Tool

The biggest role of log collection is to improve the performance of the system by analyzing user access conditions, thereby increasing the system load. Timely discovery of system load bottlenecks can also facilitate technical personnel to optimize the system based on actual user access conditions. The log records the entire process of the user’s visit to the website: who is at what time, through what channels (such as search engine, URL input), what operations have been performed; whether the system has generated errors; even the user’s IP, HTTP request time, user agent, etc. Here log collection can be divided into two forms. 1. Collected through the web server. For example, httpd, Nginx, and Tomcat all have their own logging function. At the same time, many Internet companies have their own massive data collection tools, which are mostly used for system log collection, such as Chukwa of Hadoop, Flume of Cloudera, Scribe of Facebook, etc. These tools are distributed architectures that meet hundreds of MB of log data collection and transmission requirements per second. 2. Customize user behavior. Such as listening to user behavior with JavaScript code, AJAX asynchronous request backend logging, and more.

What is Event Tracking?

Event tracking is a key step in log collection. Event tracking is to collect the corresponding information and report it at the location where you set. For example, the access status of a page, including user information, device information; or the user’s operation behavior on the page, including the length of staying time. Each event tracking is like a camera. It collects user behavior data and performs multi-dimensional analysis of the data, which can truly restore the user usage scenarios and user needs. So how do we track different event? Event tracking is to embed statistical code where you need statistics, of course, the implant code can be written by yourself, or you can use third-party statistical tools. I have talked about the principle of “do not repeat producing a wheel”. For the event tracking toolsg, the market is quite mature. I can recommend some to you. Three-party tools such as Google Analysis, Talkingdata, and more. They all use the front-end tracking method, and then the user’s behavior data can be seen in the third-party tools. But if we want to see deeper user behavior, we need to customize the event tracking settings. To sum up, log collection helps us understand the user’s operational data and is suitable for scenarios such as operation and maintenance monitoring, security auditing, and business data analysis. A typical web server will have its own logging capabilities, or you can use Flume to collect, aggregate, and transfer large volumes of log data from different server clusters. Of course, we can also use third-party statistical tools or custom buried points to get the statistics we want.

Conclusion

Data extraction is the key to data analysis. Sometimes we use Python web crawlers to solve the problem. In fact, data collection methods are very wide. Some can directly use open data sources, such as the price and transaction data of Bitcoin history. You can download directly from Kaggle and don’t need to crawl it yourself. On the other hand, according to our needs, the data that needs to be collected is also different, such as the transportation industry, data collection will be related to camera or speedometer. For operations personnel, log collection and analysis are key point. So we need to choose the right acquisition tool for a specific business scenario. If you want to know more about data analysis, just follow FineReport Reporting Software.

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