Data Collection, Just Another Way To Gather Information

Data collection just does not help the companies to launch new products or know about the public reaction to a specific issue, it is a very useful tool for statistical inferences, once the collected data is compiled. The process of data collection is the third step of the six step market research processes. Data collection can be done in two ways involving various technicalities. In this article, we shall give a brief overview of the same.

Data collection can be done in two ways - secondary data and primary data. Secondary data collection involves is the information available in books, journals, previous researches or studies and the Internet. It basically involves making use of the data already present to build or substantiate a concept.

On the other hand, primary data collection is the process of data collection through questionnaire by directly asking respondents of their opinions. Forming the right questionnaire is the most important aspect of data collection. The researcher conducting the data collection just has to be aware of the process. He should have a clear idea about the information sought by the concerned party.

Besides, the data collection officer should be able to construct the questionnaire in such a way so as to elicit the responses needed. Having constructed the questionnaire the researcher should identify the target sample. To illustrate the point clearly, we shall look into the following example.

Suppose, data collection is aimed from an area A, then, if all the residents of the data are given the questionnaire, it is called a census or in other words data collection is done from all the individuals of the specified area. One of the most common examples of data collection done by the government is census. For example the population census conducted by the US Census Bureau every ten years. On the other hand, if only twenty or thirty percent of the population living in area A are given the questionnaire, the mode of data collection would be called sampling.

The data collected from the target sample with a well-defined questionnaire will project the response of the entire population living in the area. Data collected from a sample helps to control the cost and time spent on collecting data from the population. Sample is a part of population.

Data collection just gets easier from the target sample with the help of a pretested questionnaire, which is later analyzed using statistical tests like ANOVA, Chi Square test and so on. These tests help the researcher to infer the result obtained from the data collection.

Market research/data collection is a fast growing and lucrative career option now days. One has to undertake a course in marketing, statistics and research before starting out. It is indeed very important to have a through understanding of various concepts and the theories related. Some basic terminologies related to data collection are: census, incidence, sample, population, parameters, sampling frames and so on.

Source: http://ezinearticles.com/?Data-Collection,-Just-Another-Way-To-Gather-Information&id=853158

Google Sheets vs Web Scraping Services

Google Sheets vs Web Scraping Services

Ever since the data on the web started multiplying in terms of quantity and quality, people have sought out ways to scrape or extract this data for a wide range of applications. Since the scope of extraction was limited back then, the extraction methods mostly comprised of manual methods like copy-pasting text into a local document.

As businesses realized the importance of web scraping as a big data acquisition channel, new technologies and tools surfaced with advanced capabilities to make web scraping easier and efficient.

Today, there are various solutions catering to the web data extraction requirements of companies; DIY tools to managed web scraping services are out there and you can choose one that suits your requirements the best.

Scraping using Google sheets

As we mentioned earlier, there are so many different ways to extract data from the web although not all of these would make sense from a business point of view. You can even use Google docs to extract data from a simple HTML page if you are looking to understand the basics of web scraping. You could check out our guide on using google sheets to scrape a website if you want to learn something that might come handy.

However, Google docs and other web data extraction tools come with their own limitations. For starters, tools aren’t meant for large-scale extraction which is what most businesses will require. Unless you are a hobbyist looking to extract a few web pages for tinkering with a new data visualization tool, you should steer clear from web scraping tools. Scraping tools cannot cater to the requirements of a business as it could be well out of their capabilities.

Enterprise-grade web data extraction

Web scraping is only a common term for the process of saving data from a web page to a local storage or cloud. However, if we consider the practical applications of the data, it’s obvious that there’s a clear distinction between mere web scraping and enterprise-grade web data extraction.

The latter is more inclined towards the extraction of data from the web for real-world applications and hence requires advanced solutions that are built for the same. Following are some of the qualities that an enterprise-grade web scraping solution should have:

- High-end customization options
- Complete automation
- Post-processing options to make the data machine-ready
- Technology to handle dynamic websites
- Capability of handling large-scale extraction

Why DaaS is the best solution for enterprise-grade web scraping

When it comes to extracting data for business use cases, there should be a stark difference in the way things are done. The speed and efficiency matters more in the business world and this demands a managed web scraping solution that takes the complexities and pain points out of the process to provide companies with just the data they need, the way they need it.

Data as a Service is exactly what businesses that are looking to extract web data without losing focus on their core business operations need. Web crawling companies like PromptCloud, that work on the DaaS model does all the heavy lifting associated with extracting web data and deliver only the needed data to the companies in a ready-to-use format.

Source:-https://www.promptcloud.com/blog/google-sheets-vs-web-scraping-services

Scraping Dynamic Websites: How We Tackle the Problem

Scraping Dynamic Websites: How We Tackle the Problem

Acquiring data from the web for business applications has already gained popularity if we look at the sheer number of use cases. Companies have realized the value addition provided by data and are looking for better and efficient ways of data extraction. However, web scraping is a niche technical process that takes years to master given the dynamic nature of the web. Since every website is different and custom coded, it’s not possible to write a single program that can handle multiple websites. The web scraping setup should be coded separately for each target site and this needs a team of skilled programmers.

Web scraping is without doubt a complex trade; however if the target site in question employs dynamic coding practices, this complexity is further multiplied. Over the years, we have understood the technical nuances of web scraping and perfected our modus operandi to to scrape dynamic websites with high accuracy and efficiency. Here are some ways how we tackle the challenge of scraping dynamic websites.

1. Proxies

Some websites have different Geo/Device/OS/browser specific versions that they serve depending on the variables. This could give a great deal of confusion to the crawlers especially while figuring out how to extract the right version. This will need some manual work in terms of finding the different versions provided by the site and configuring proxies to fetch the right version as per the requirement. For geo-specific versions, the crawler is simply deployed on a server from where the required version of the site is accessible.

2. Browser automation

When it comes to websites that use very complex and dynamic code, it’s better have all the page content rendered using a browser first. Selenium can be used for browser automation which will help us do the scraping. It is essentially a handy toolkit that can drive the browser from your favorite programming language. Although it’s primarily used for testing, it can be used for scraping dynamic web pages. It can be used to control a web browser, which is how scraping using selenium is typically done. In this case, the browser first renders the page which will help overcome the problem of reverse engineering JavaScript code to fetch the page content. Once the page content is rendered, it is saved locally to scrape the required data points later. Although this is comparatively easy, there is a high chance of encountering errors while scraping using the browser automation method.

3. Handling POST requests

Many web pages will only display the data that we need after receiving a certain input from the user. Let’s say you are looking for used cars data from a particular geo-location on a classified site. The website would first require you to enter the ZIP code of the location from where you need listings from. This ZIP code must be sent to the website as a post request while scraping. We craft the post request using the appropriate parameters so as to reach the target page that contains all the data points to be scraped.

4. Manufacturing the JSON URL

There are dynamic web pages that use AJAX calls to load and refresh the page content. These are particularly difficult to scrape and extract data from as the triggers that make up the JSON file is difficult to trace. This requires a lot of manual inspection and testing, but once the appropriate parameters are identified, a JSON file that would fetch the target page which includes the desired data points can be manufactured. This JSON file is often tweaked automatically for navigation or fetching varying data points. Manufacturing the JSON URL with apt parameters is the primary pain point with web pages that use AJAX calls.
Bottom-line

Scraping dynamic web pages is extremely complicated and demands deep expertise in the field of web scraping. It also demands an extensive tech stack and well-built infrastructure that can handle the complexities associated with web data extraction. With our years of expertise and well-evolved web scraping infrastructure, we cater to data requirements where dynamic web pages are involved on a daily basis.

Source:https://www.promptcloud.com/blog/scraping-dynamic-websites-web-scraping

How Data Scraping Help Businesses?

Gathering data from diverse internet sources like website and others, the process is called as data scraping. Around the globe such and many describe data scraping as web scraping, data harvesting. Now days the competition is very high in every business and for that the companies required to collect more useful data for their business. 

Research market trends and extracting different types of data is necessary today’s. Data scraping is one of the latest technology that collect diverse data from internet source and make use in the analysis.

By using data scraping any one can quickly classify the any kind of information and also make decision and marketing strategies. Reducing risk and also improving business profit are other advantages of data scraping. Scraping data from website by manually and also using data scraper, website scraper and website data scraper tools.

Now you want to get data scraping solutions for your business?The company offers lowest industry rate data scraping, web data scraping and website data scraping services as the need of clients with never compromise on quality and fast turn around time. For further details about the company send query at info@www.web-scraping-services.com.


Source Url : -http://3idatascraping.weebly.com/blog/how-data-scraping-help-businesses

How We Maintain Data Quality While Handling Large Scale Extraction

How We Maintain Data Quality While Handling Large Scale Extraction

The demand for high quality data is increasing along with the rise in products and services that require data to run. Although the information available on the web is increasing in terms of quantity and quality, extracting it in a clean, usable format remains challenging to most businesses. Having been in the web data extraction business for long enough, we have come to identify the best practices and tactics that would ensure high quality data from the web.

At PromptCloud, we not only make sure data is accessible to everyone, we make sure it’s of high quality, clean and delivered in a structured format. Here is how we maintain the quality while handling zettabytes of data for hundreds of clients from across the world.

Manual QA process

1. Crawler review

Every web data extraction project starts with the crawler setup. Here, the quality of the crawler code and its stability is of high priority as this will have a direct impact on the data quality. The crawlers are programmed by our tech team members who have high technical acumen and experience. Once the crawler is made, two peers review the code to make sure that the optimal approach is used for extraction and to ensure there are no inherent issues with the code. Once this is done, the crawler is deployed on our dedicated servers.

2. Data review

The initial set of data starts coming in when the crawler is run for the first time. This data is manually inspected, first by the tech team and then by one of our business representatives before the setup is finalized. This manual layer of quality check is thorough and weeds out any possible issues with the crawler or the interaction between the crawler and website. If issues are found, the crawler is tweaked to eliminate them completely before the setup is marked complete.

Automated monitoring

Websites get updated over time, quite frequently than you’d imagine. Some of these changes could break the crawler or cause it to start extracting the wrong data. This is why we have developed a fully automated monitoring system to watch over all the crawling jobs happening on our servers. This monitoring system continuously checks the incoming data for inconsistencies and errors. There are three types of issues it can look for:

1. Data validation errors

Every data point has a defined value type. For example, the data point ‘Price’ will always have a numerical value and not text. In cases of website changes, there can be class name mismatches that might cause the crawler to extract wrong data for a certain field. The monitoring system will check if all the data points are in line with their respective value types. If an inconsistency is found, the system immediately sends out a notification to the team members handling that project and the issue is fixed promptly.

2. Volume based inconsistencies

There can be cases where the volume count for records significantly drop or increase in an irregular fashion. This is a red sign as far as web crawling goes. The monitoring system will already have the expected record count for different projects. If inconsistencies are spotted in the data volumes, the system sends out a prompt notification.

3. Site changes

Structural changes happening to the target websites is the main reason why crawlers break. This is monitored by our dedicated monitoring system, quite aggressively. The tool performs frequent checks on the target site to make sure nothing has changed since the previous crawl. If changes are found, it sends out notifications for the same.
High end servers

It is understood that web crawling is a resource-intensive process that needs high performance servers. The quality of servers will determine how smooth the crawling happens and this in turn has an impact on the quality of data. Having firsthand experience in this, we use high-end servers to deploy and run our crawlers. This helps us avoid instances where crawlers fail due to the heavy load on servers.

Data cleansing

The initially crawled data might have unnecessary elements like HTML tags. In that sense, this data can be called crude. Our cleansing system does an exceptionally good job at eliminating these elements and cleaning up the data thoroughly. The output is clean data without any of the unwanted elements.

Structuring

Structuring is what makes the data compatible with databases and analytics systems by giving it a proper, machine readable syntax. This is the final process before delivering the data to the clients. With structuring done, the data is ready to be consumed either by importing it to a database or plugging to an analytics system. We deliver the data in multiple formats – XML, JSON and CSV which also adds to the convenience of handling it.

Source:https://www.promptcloud.com/blog/how-we-maintain-data-quality-web-data-extraction

4 Tools That Makes Web Data Extraction Easy

There is a huge amount of data available on the World Wide Web. Organizations and individuals find this information useful and often have to make use of it for various purposes. Traditionally, web data is retrieved by browsing and keyword searching. These methods are purely intuitive, the searches can return vast amount of unnecessary data, and it can take quite a bit of time before the searchers find what they are looking for. This data is sometimes hard to manipulate and work on as it is done in traditional databases.

But web pages written in mark-up languages like HTML and XHTML contain a wealth of knowledge. They also provide the structures that make data manipulation and analysis so easy. To extract this data some easily usable applications have been built. Though people who know nothing about coding can use some of these applications, it is always advisable to take the help of data extraction experts for help with such work, to obtain best results.

4  Tools to Improve your Web Data Extraction Efforts:

Uipath:

One of the popular web scraping applications is offered by the software automation and application integration company, Uipath. They offer free trials and also live demos for new users and potential customers. They offer website scraping from HTML, XML, AJAX, Java applets, Flash, Silverlight and PDF. Their application has powerful data transformation features and enables deduplication with SQL and LINQ queries.
Once the data has been extracted, it can be exported to various outputs like Microsoft Excel, CSV, .NET DataTable and so on. Automations can be done with web login, navigation, and even filling of forms.
This application is good for non-coders and can even be used to manipulate the interface of another application so that data transfer can take place between the two of them.
The price tag might be a tad high for individual users, but is worth it if you want a fast, accurate and simple application.

Import.io:

 Import.io offers to “instantly turn web pages into data”. They advertise their service saying that the customer does not need plugin, training or setup. Users can create custom APIs and crawl entire websites by using their desktop application. The best part is that no coding knowledge is required. Users can scrap data from an unlimited number of web pages. For the service, each page is a source that holds great potential to source application programming interface.
The extracted data is stored on Import.io’s cloud servers. It can then be downloaded in different formats that include CSV, Google sheets, Microsoft Excel and many more. The generated API enables users to integrate live web data with their own applications, third party analytics and visualization software without much difficulty. Though users do not need much technical skills to operate this service, the extraction reports arrives a good 24 hours after the request has been submitted.

Kimono:

The task of building an API to power applications, models and visualizations using live data and without the benefit of any code is done in seconds by Kimono. The service has a smart extractor. It recognizes patterns in web content. This enables the user to get the data that he or she wants, quickly and visually. The extracted APIs are hosted on a cloud. They are then run as per the schedule that is convenient for the user. While there is no problem with either the speed or the accuracy of Kimono, there is a lack of availability of page navigation, and the system requires some training before it begins to function at full capability.

Screen Scraper:

Like the other above-mentioned services, Screen Scraper works well with HTML and Javascript, extracts data precisely and provides the data in Excel and CSV fomat. However, it requires the user to have some coding skills. Only then can it be used to its optimum functionality. Even though the user will have to shell out a bit of money to use Screen Scraper, the service can handle almost any data extraction task with ease.

Source Url:-https://www.invensis.net/blog/data-processing/4-tools-makes-web-data-extraction-easy/

Web Scraping – A trending technique in data science!!!

Web Scraping – A trending technique in data science!!!

Web scraping as a market segment is trending to be an emerging technique in data science to become an integral part of many businesses – sometimes whole companies are formed based on web scraping. Web scraping and extraction of relevant data gives businesses an insight into market trends, competition, potential customers, business performance etc.  Now question is that “what is actually web scraping and where is it used???” Let us explore web scraping, web data extraction, web mining/data mining or screen scraping in details.

What is Web Scraping?

Web Data Scraping is a great technique of extracting unstructured data from the websites and transforming that data into structured data that can be stored and analyzed in a database. Web Scraping is also known as web data extraction, web data scraping, web harvesting or screen scraping.

What you can see on the web that can be extracted. Extracting targeted information from websites assists you to take effective decisions in your business.

Web scraping is a form of data mining. The overall goal of the web scraping process is to extract information from a websites and transform it into an understandable structure like spreadsheets, database or csv. Data like item pricing, stock pricing, different reports, market pricing, product details, business leads can be gathered via web scraping efforts.

There are countless uses and potential scenarios, either business oriented or non-profit. Public institutions, companies and organizations, entrepreneurs, professionals etc. generate an enormous amount of information/data every day.

Uses of Web Scraping:

The following are some of the uses of web scraping:

- Collect data from real estate listing
- Collecting retailer sites data on daily basis
- Extracting offers and discounts from a website.
- Scraping job posting.
- Price monitoring with competitors.
- Gathering leads from online business directories – directory scraping
- Keywords research
- Gathering targeted emails for email marketing – email scraping
- And many more.

There are various techniques used for data gathering as listed below:

- Human copy-and-paste – takes lot of time to finish when data is huge
- Programming the Custom Web Scraper as per the needs.
- Using Web Scraping Softwares available in market.

Are you in search of web data scraping expert or specialist. Then you are at right place. We are the team of web scraping experts who could easily extract data from website and further structure the unstructured useful data to uncover patterns, and help businesses for decision making that helps in increasing sales, cover a wide customer base and ultimately it leads to business towards growth and success.

Source:http://webdata-scraping.com/web-scraping-trending-technique-in-data-science/