Data Scraping Services - Things to take care while doing Web Scraping!!!

In the present day and age, web scraping word becomes most popular in data science. Basically web scraping is extracting the information from the websites using pre-written programs and web scraping scripts. Many organizations have successfully used web site scraping to build relevant and useful database that they use on a daily basis to enhance their business interests. This is the age of the Big Data and web scraping is one of the trending techniques in the data science.

Throughout my journey of learning web scraping and implementing many successful scraping projects, I have come across some great experiences we can learn from.  In this post, I’m going to discuss some of the approaches to take and approaches to avoid while executing web scraping.

User Proxies: Anonymously scraping data from websites

One should not scrape website with a single IP Address. Because when you repeatedly request the web page for web scraping, there is a chance that the remote web server might block your IP address preventing further request to the web page. To overcome this situation, one should scrape websites with the help of proxy servers (anonymous scraping). This will minimize the risk of getting trapped and blacklisted by a website. Use of Proxies to hide your identity (network details) to remote web servers while scraping data. You may also use a VPN instead of proxies to anonymously scrape websites.

Take maximum data and store it.

Do not follow “process the web page as it comes from the remote server”. Instead take all the information and store it to disk. This approach will be useful when your scraping algorithm breaks in the middle. In this case you don’t have to start scraping again. Never download the same content more than once as you are just wasting bandwidth. Try and download all content to disk in one go and then do the processing.

Follow strict rules in parsing:

Check various rules while parsing the information from the web site. For example if you expect a value to be a date then check that it’s really a date. This may greatly improve the quality of information. When you get unexpected data, then the algorithm need to be changed accordingly.

Respect Robots.txt

Robots.txt specifies the set of rules that should be followed by web crawlers and robots. I strongly advise you to consider and adjust your crawler to fully respect robots.txt. Robots.txt contains instructions on the exact pages that you are allowed to crawl, user-agent, and the requisite intervals between page requests. Following to these instructions minimizes the chance of getting blacklisted and banned from website owner.

Use XPath Smartly

XPath is a nice option to select elements of the HTML document more flexibly than CSS Selectors.  Be careful about HTML structure change through page to page so one xpath you made may be failed to extract data on another page due to changes in HTML structure.

Obey Website TOC:

Some websites make it absolutely apparent in their terms and conditions that they are particularly against to web scraping activities on their content. This can make you vulnerable against possible ethical and legal implications.

Test sample scrape and verify the data with actual scrape

Once you are done with web scraping project set up, you need to test it for sometimes. Check the extracted data. If something is not good, find out the cause and make changes accordingly and finally come to a perfect web scraping project.

Source: http://webdata-scraping.com/things-take-care-web-scraping/

Web Scraping Services : What are the ethics of web scraping?

Someone recently asked: "Is web scraping an ethical concept?" I believe that web scraping is absolutely an ethical concept. Web scraping (or screen scraping) is a mechanism to have a computer read a website. There is absolutely no technical difference between an automated computer viewing a website and a human-driven computer viewing a website. Furthermore, if done correctly, scraping can provide many benefits to all involved.

There are a bunch of great uses for web scraping. First, services like Instapaper, which allow saving content for reading on the go, use screen scraping to save a copy of the website to your phone. Second, services like Mint.com, an app which tells you where and how you are spending your money, uses screen scraping to access your bank's website (all with your permission). This is useful because banks do not provide many ways for programmers to access your financial data, even if you want them to. By getting access to your data, programmers can provide really interesting visualizations and insight into your spending habits, which can help you save money.

That said, web scraping can veer into unethical territory. This can take the form of reading websites much quicker than a human could, which can cause difficulty for the servers to handle it. This can cause degraded performance in the website. Malicious hackers use this tactic in what’s known as a "Denial of Service" attack.

Another aspect of unethical web scraping comes in what you do with that data. Some people will scrape the contents of a website and post it as their own, in effect stealing this content. This is a big no-no for the same reasons that taking someone else's book and putting your name on it is a bad idea. Intellectual property, copyright and trademark laws still apply on the internet and your legal recourse is much the same. People engaging in web scraping should make every effort to comply with the stated terms of service for a website. Even when in compliance with those terms, you should take special care in ensuring your activity doesn't affect other users of a website.

One of the downsides to screen scraping is it can be a brittle process. Minor changes to the backing website can often leave a scraper completely broken. Herein lies the mechanism for prevention: making changes to the structure of the code of your website can wreak havoc on a screen scraper's ability to extract information. Periodically making changes that are invisible to the user but affect the content of the code being returned is the most effective mechanism to thwart screen scrapers. That said, this is only a set-back. Authors of screen scrapers can always update them and, as there is no technical difference between a computer-backed browser and a human-backed browser, there's no way to 100% prevent access.

Going forward, I expect screen scraping to increase. One of the main reasons for screen scraping is that the underlying website doesn't have a way for programmers to get access to the data they want. As the number of programmers (and the need for programmers) increases over time, so too will the need for data sources. It is unreasonable to expect every company to dedicate the resources to build a programmer-friendly access point. Screen scraping puts the onus of data extraction on the programmer, not the company with the data, which can work out well for all involved.

Source: https://quickleft.com/blog/is-web-scraping-ethical/

Data Scraping - One application or multiple?

I have 30+ sources of data I scrape daily in various formats (xml, html, csv). Over the last three years Ive built 20 or so c# console applications that go out, download the data and re-format it into a database. But Im curious what other people are doing for this type of task. Are people building one tool that has a lot of variables and inputs or are people designing 20+ programs to scrape and parse this data. Everything is hard-coded into each console and run through Windows Task Manager.

Added a couple additional thoughts/details:

    Of the 30 sources, they all have unique properties, all are uploaded into individual MySQL tables and all have varying frequencies. For example, one data source is hit once a minute, another on 5 minute intervals. Majority are once an hour and once a day.

At current I download the formats (xml, csv, html), parse them into a formatted csv and put them into staging folders. Within that folder, I run an application that reads a config file specific to the folder. When a new csv is added to the folder, the application then uploads the data into the specific MySQL tables designated in the config file.

Im wondering if it is worth re-building all this into a larger complex program that is more capable of dynamically adding content+scrapes and adjusting to format changes.

Looking for outside thoughts.

5 Answers

What you are working on is basically ETL. So at a high level you need an export component (get stuff) a transform component (map to known format) and a load (take known format and put stuff somewhere). If you are comfortable being tied to a RDBMS you could use something like SQL Server SSIS packages. What I would do is create a host application that managed common aspects of the overall process (errors, and pipeline processing). Then make the specifics of the E, T, and L pluggable. A low ceremony way to get this would be to host the powershell runtime and create each seesion with common context objects that the scripts will use to communicate. You get a built in pipe and filter model for scripts and easy, safe extensibility. This design has worked extremely for my team with a similar situation.

Resist the temptation to rewrite.

However, for new code, you could plan for what you know has already happened. Write a retrieval mechanism that you can reuse through configuration. Write a translation mechanism that you can reuse (maybe in a library that you can call with very little code). Write a saving mechanism that can be called or configured.

At this point, you've written #21(+). Now, the following ones can be handled with a tiny bit of code and configuration. Yay!

(You may want to implement this in a service that handles multiple conversions, but weight the benefits of it versus the ability to separate errors in one module from the rest.)

1

It depends - if you need the scrapers to feed into a single application/database and have a uniform data format, it makes sense to have them all in a single program (possibly inheriting from a common base scraper).

If not and they are completely unrelated to each other, might as well keep them separate so changes in one have no effect on another.

Update, following edits to question:

Don't change things just for the sake of change. You have something that works, don't mess with it too much.

Since your data sources and data sinks are all separate from each other, combining them into one application will simply create a very complicated application that will be very difficult to change when needed.

Since the scrapers are separate, keep the separation as you have it now.

As sbrenton said, this most falls in with ETL. You should check out Talend Open Studio. It specializes in handling data flows like I imagine yours are as well as other things like duplicate removal, normalization of fields; tens/hundreds of drag and drop ETL components, you can also write custom code as Talend is a code generator as well, either Java or Perl are options. You can also use Talend to execute system commands. I use it for my ETL work, although not in production, in production we will use SSIS, mostly due to lots of other Microsoft products in house.

You may want to use some good scheduling library, like Quartz.NET.

In a few words, here's what you can expect:

•     Your tasks are represented by classes and not processes
•     You can set and forget tasks and scale across multiple servers
•     You have an out-of-the-box system to actually take care of what is needed to be run when, what failed and needs to be re-run, etc. etc.

Source: http://programmers.stackexchange.com/questions/118077/data-scraping-one-application-or-multiple/118098#118098

Web scraping using Python without using large frameworks like Scrapy

scrapy-big-logoIf you need publicly available data from scraping the Internet, before creating a webscraper, it is best to check if this data is already available from public data sources or APIs. Check the site’s FAQ section or Google for their API endpoints and public data.

Even if their API endpoints are available you have to create some parser for fetching and structuring the data according to your needs.

Scrapy is a well established framework for scraping, but it is also a very heavy framework. For smaller jobs, it may be overkill and for extremely large jobs it is very slow.

So if you would like to roll up your sleeves and build your own scraper, continue reading.

Here are some basic steps performed by most webspiders:

1) Start with a URL and use a HTTP GET or PUT request to access the URL
2) Fetch all the contents in it and parse the data
3) Store the data in any database or put it into any data warehouse
4) Enqueue all the URLs in a page
5) Use the URLs in queue and repeat from process 1
Here are the 3 major modules in every web crawler:
1) Request/Response handler.
2) Data parsing/data cleansing/data munging process.
3) Data serialization/data pipelines.

Lets look at each of these modules and see what they do and how to use them.

Request/Response handler

Request/response handlers are managers who make http requests to a url or a group of urls, and fetch the response objects as html contents and pass this data to the next module. If you use Python for performing request/response url-opening process libraries such as the following are most commonly used

1) urllib(20.5. urllib – Open arbitrary resources by URL – Python v2.7.8 documentation) -Basic python library yet high-level interface for fetching data across the World Wide Web.

2) urllib2(20.6. urllib2 – extensible library for opening URLs – Python v2.7.8 documentation) – extensible library of urllib, which would handle basic http requests, digest authentication, redirections, cookies and more.

3) requests(Requests: HTTP for Humans) – Much advanced request library

which is built on top of basic request handling libraries.

Data parsing/data cleansing/data munging process

This is the module where the fetched data is processed and cleaned. Unstructured data is transformed into structured during this processing. Usually  a set of Regular Expressions (regexes) which perform pattern matching and text processing tasks on the html data are used for this processing.

In addition to regexes, basic string manipulation and search methods are also used to perform this cleaning and transformation. You must have a thorough knowledge of regular expressions and so that you could design the regex patterns.

Data serialization/data pipelines

Once you get the cleaned data from the parsing and cleaning module, the data serialization module will be used to serialize the data according to the data models that you require. This is the final module that will output data in a standard format that can be stored in databases, JSON/CSV files or passed to any data warehouses for storage. These tasks are usually performed by libraries listed below

1) pickle (pickle – Python object serialization) –  This module implements a fundamental, but powerful algorithm for serializing and de-serializing a Python object structure

2) JSON (JSON encoder and decoder)

3) CSV (https://docs.python.org/2/library/csv.html)

4) Basic database interface libraries like pymongo (Tutorial – PyMongo),mysqldb ( on python.org), sqlite3(sqlite3 – DB-API interface for SQLite databases)

And many more such libraries based on the format and database/data storage.

Basic spider rules

The rules to follow while building a spider are to be nice to the sites you are scraping and follow the rules in the site’s spider policies outlined in the site’s robots.txt.

Limit the  number of requests in a second and build enough delays in the spiders so that  you don’t adversely affect the site.

It just makes sense to be nice.

We will cover more techniques in future articles

Source: http://learn.scrapehero.com/webscraping-using-python-without-using-large-frameworks-like-scrapy/

Social Media Crawling & Scraping services for Brand Monitoring

Crawling social media sites for extracting information is a fairly new concept – mainly due to the fact that most of the social media networking sites have cropped up in the last decade or so. But it’s equally (if not more) important to grab this ever-expanding User-Generated-Content (UGC) as this is the data that companies are interested in the most – such as product/service reviews, feedback, complaints, brand monitoring, brand analysis, competitor analysis, overall sentiment towards the brand, and so on.

Scraping social networking sites such as Twitter, Linkedin, Google Plus, Instagram etc. is not an easy task for in-house data acquisition departments of most companies as these sites have complex structures and also restrict the amount and frequency of the data that they let out to crawlers. This kind of a task is best left to an expert, such as PromptCloud’s Social Media Data Acquisition Service – which can take care of your end-to-end requirements and provide you with the desired data in a minimal turnaround time. Most of the popular social networking sites such as Twitter and Facebook let crawlers extract data only through their own API (Application Programming Interface), so as to control the amount of information about their users and their activities.

PromptCloud respects all these restrictions with respect to access to content and frequency of hitting their servers to make sure that user information is not compromised and their experience with the site is unhindered.

Social Media Scraping Experts

At PromptCloud, we have developed an expertise in crawling and scraping social media data in real-time. Such data can be from diverse sources such as – Twitter, Linkedin groups, blogs, news, reviews etc. Popular usage of this data is in brand monitoring, trend watching, sentiment/competitor analysis & customer service, among others.

Our low-latency component can extract data on the basis of specific keywords, categories, geographies, or a combination of these. We can also take care of complexities such as multiple languages as well as tweets and profiles of specific users (based on keywords or geographies). Sample XML data can be accessed through this link – demo.promptcloud.com.

Structured data is delivered via a single REST-based API and every time new content is published, the feed gets updated automatically. We also provide data in any other preferred formats (XML, CSV, XLS etc.).

If you have a social media data acquisition problem that you want to get solved, please do get in touch with us.

Source: https://www.promptcloud.com/social-media-networking-sites-crawling-service/

Scraping Twitter Lists To Boost Social Outreach (+ Free Tool!)

I published a post a few weeks ago describing how to build your own twitter custom audience list, outlining a variety of techniques to build up your list.

This post outlines another method (hat tip to Ade Lewis for the idea) which requires you to scrape Twitter directly.

If you want to skip all the explanations and just want to download the Twitter List Scraper tool, here you go…

Download the Twitter Scraper Tool for Windows or Mac (completely free)

Disclaimer: Scraping Twitter is against their Terms of Service, so if you decide to do this you do it at your own risk.

Some Benchmarks

Building custom audiences on Twitter requires you to identify Twitter usernames that might be interested in your service or product.

In my previous posts, one of the methods I employed was to pull a competitor’s link profile and scrape social accounts from the linking domains.

Once you upload a custom list, Twitter goes through a process of ‘matching’ against profiles in their system, to make sure the user exists and hasn’t opted out of tailored ads.

As our data was scraped from a list of unqualified websites, the data matching wasn’t likely to be perfect.

Experiments

Since I published that post, I have been experimenting a fair bit with list building, and have built up around 10 custom audience lists. I‘ve uploaded a total of 48,857 Twitter usernames using this method, but only 29,260 were matched by Twitter (just less than 60% match rate).

From some other experiments where I have had better control over the input data, this match rate was between 70-80%.

Since we’ll be scraping Twitter directly, I expect our match rate to be much higher – 90%+

Finding Relevant Twitter Lists

So, we’re going to scrape Twitter, and the first step is to find Twitter lists that will contain users potentially interested in what we have to offer.

As an example, we’ll pretend we’re marketing a music website, and we’ve produced a survey we want to collect responses for.

An advanced Google query can give us lists of music bloggers: site:twitter.com inurl:lists inurl:members inurl:music “music blogger”

Source: http://urlprofiler.com/blog/scraping-twitter/

Web Scraping - Data Collection or Illegal Activity?

Web Scraping Defined

We've all heard the term "web scraping" but what is this thing and why should we really care about it?  Web scraping refers to an application that is programmed to simulate human web surfing by accessing websites on behalf of its "user" and collecting large amounts of data that would typically be difficult for the end user to access.  Web scrapers process the unstructured or semi-structured data pages of targeted websites and convert the data into a structured format.  Once the data is in a structured format, the user can extract or manipulate the data with ease.  Web scraping is very similar to web indexing (used by most search engines), but the end motivation is typically much different.  Whereas web indexing is used to help make search engines more efficient, web scraping is typically used for different reasons like change detection, market research, data monitoring, and in some cases, theft.

Why Web Scrape?

There are lots of reasons people (or companies) want to scrape websites, and there are tons of web scraping applications available today.  A quick Internet search will yield numerous web scraping tools written in just about any programming language you prefer.  In today's information-hungry environment, individuals and companies alike are willing to go to great lengths to gather information about all sorts of topics.  Imagine a company that would really like to gather some market research on one of their leading competitors...might they be tempted to invoke a web scraper that gathers all the information for them?  Or, what if someone wanted to find a vulnerable site that allowed otherwise not-so-free downloads?  Or, maybe a less than honest person might want to find a list of account numbers on a site that failed to properly secure them.  The list goes on and on.

I should mention that web scraping is not always a bad thing.  Some websites allow web scraping, but many do not.  It's important to know what a website allows and prohibits before you scrape it.

The Problem With Web Scraping

Web scraping rides a fine line between collecting information and stealing information.  Most websites have a copyright disclosure statement that legally protects their website information.  It's up to the reader/user/scraper to read these disclosure statements and follow along legally and ethically.  In fact, the F5.com website presents the following copyright disclosure:  "All content included on this site, such as text, graphics, logos, button icons, images, audio clips, and software, including the compilation thereof (meaning the collection, arrangement, and assembly), is the property of F5 Networks, Inc., or its content and software suppliers, except as may be stated otherwise, and is protected by U.S. and international copyright laws."  It goes on to say, "We reserve the right to make changes to our site and these disclaimers, terms, and conditions at any time."

So, scraper beware!  There have been many court cases where web scraping turned into felony offenses.  One case involved an online activist who scraped the MIT website and ultimately downloaded millions of academic articles.  This guy is now free on bond, but faces dozens of years in prison and $1 million if convicted.  Another case involves a real estate company who illegally scraped listings and photos from a competitor in an attempt to gain a lead in the market.  Then, there's the case of a regional software company that was convicted of illegally scraping a major database company's websites in order to gain a competitive edge.  The software company had to pay a $20 million fine and the guilty scraper is serving three years probation.  Finally, there's the case of a medical website that hosted sensitive patient information.  In this case, several patients had posted personal drug listings and other private information on closed forums located on the medical website.  The website was scraped by a media-rese
arch firm, and all this information was suddenly public.

While many illegal web scrapers have been caught by the authorities, many more have never been caught and still run loose on websites around the world.  As you can see, it's increasingly important to guard against this activity.  After all, the information on your website belongs to you, and you don't want anyone else taking it without your permission.

The Good News

As we've noted, web scraping is a real problem for many companies today.  The good news is that F5 has web scraping protection built into the Application Security Manager (ASM) of its BIG-IP product family.  As you can see in the screenshot below, the ASM provides web scraping protection against bots, session opening anomalies, session transaction anomalies, and IP address whitelisting.

The bot detection works with clients that accept cookies and process JavaScript.  It counts the client's page consumption speed and declares a client as a bot if a certain number of page changes happen within a given time interval.  The session opening anomaly spots web scrapers that do not accept cookies or process JavaScript.  It counts the number of sessions opened during a given time interval and declares the client as a scraper if the maximum threshold is exceeded.  The session transaction anomaly detects valid sessions that visit the site much more than other clients.  This defense is looking at a bigger picture and it blocks sessions that exceed a calculated baseline number that is derived from a current session table.  The IP address whitelist allows known friendly bots and crawlers (i.e. Google, Bing, Yahoo, Ask, etc), and this list can be populated as needed to fit the needs of your organization.

I won't go into all the details here because I'll have some future articles that dive into the details of how the ASM protects against these types of web scraping capabilities.  But, suffice it to say, ASM does a great job of protecting your website against the problem of web scraping.

I'm sure as you studied the screenshot above you also noticed lots of other protection capabilities the ASM provides...brute force attack prevention, customized attack signatures, Denial of Service protection, etc.  You might be wondering how it does all that stuff as well.  Give us a little feedback on the topics you would like to see, and we'll start posting some targeted tech tips for you!

Thanks for reading this introductory web scraping article...and, be sure to come back for the deeper look into how the ASM is configured to handle this problem. For more information, check out this video from Peter Silva where he discusses ASM botnet and web scraping defense.

Source: https://devcentral.f5.com/articles/web-scraping-data-collection-or-illegal-activity

Lawyers & Attorneys Website Data Scraping Services

There are so many instances where one end’s up needing information from lawyers or bar associations. However, if you approach them directly or look for other ways to get information it might either be difficult or you might not get the information you are looking for. Thus, the best way to go about the scraping lawyer data.

Scraping lawyer data allow you to get information from various attorney websites, bar association websites, or other related websites. Using web scraping tools for getting such information makes it much easier to get all the relevant and important information without actually having to worry about the same.

If you wish to scrape data from lawyer, you are entitled to information such as lawyer name, firm names, address, contact details, history about the lawyers, educational qualifications, the bar association they are part of and much more.

Scraping lawyer data ensure that you also have images of the lawyer you are concentrating on. The result of scrape data form lawyer can be obtained in any format the user wants such as csv, excel, MySql etc. Scraping lawyer data also ensures that none of the information provided are repetitive or redundant.

If you are in need of information regarding any lawyer such as their contact details, address etc. it could end up being a huge and difficult task to get it manually or physically. Thus, taking off the help of scraping tools would ensure that you get all the needed information without actually having to bother about anything at all. The presence of lots of attorney websites and the fact that more and more lawyers are moving to the internet makes getting information easy with the help of some great tools. Scraping data is a very useful and handy method in which one can get all the required and relevant information and that too in a very easy to read format, which makes the method even worthier.

There are quite a few tools or services that you can take help of to get lawyers data scraped. Most of these services also provide with a sample demo and that free of cost. From the sample one can decide if they wish to continue with the services or try some other services. Thus, if you want any information from attorney websites or information about any lawyers, data scraping is a great way to get the same.

Source: https://3idatascraping.wordpress.com/2014/03/18/lawyers-attorneys-website-data-scraping-services/