How to Apply Data Scraping for SEO
There are various search engines, but Google is still the most popular and preferred. However, it often changes its algorithms. In such a way, Google is constantly shifting to being primarily a discovery engine for organic listings and paid as well.
Lots of people claim that SEO is dying, but it’s not so. SEO is changing the same pace as Google does and it is still effective if properly used.
To dominate the organic listings, SEO specialists should know a few things about Google:
It gathers data from everywhere, even from your device, applications, and location details.
The engine ranks the sites that users want to see.
It makes search results personalized.
Google likes brands.
So, dealing with Google’s algorithms, take all these factors into account, and outperform your competitors at the same time. For the purpose, you need to analyze a lot of data.
Fortunately, data scraping tools and services facilitate pulling the necessary information from multiple sites and getting the most of it. For SEO specialists, this technology is a mighty helper in the ongoing work of your website quality improvement.
If your site is big, and it’s already difficult to check it manually, automated scrapers come handy to verify that the content of your site is comprehensive, with the necessarily required word count and links included, that all pages have meta titles and descriptions, etc.
After checking your own site, you can proceed to retrieving the content your competitors post and generate traffic through.
What should you do?
Organic keyword results scraping
Find out the keywords your competitors rank for. Checking metadata and PPC ads is also helpful to know where their traffic comes from and use the source as well.
AdWords ad copies research
With the help of specialized tools, like SEMrush, for instance, find out the keywords purchased through Google AdWords. Using scraping tools, you will save lots of time and energy.
Scarping AdWords copy works even for the sites protected from crawling. So just sort the received list and pick up the most relative and effective keys for your ad campaign.
Potential influencers search
Searching through relative (or competitor) blog comments, you can always find people influential in your sphere, interested in your product or service, and ready to cooperate, write for or advertise you, for instance.
Automated scrapers can help you find the relative blogs, articles, comments, and contact information of the interested people.
Figure out the best performing categories
Review your best performing content and find out what makes certain articles more shareable. Is it a title, topic, category, other metrics?
Getting such an insight into your site, check also the competitor resources. Then adjust your content strategy accordingly.
Get more interesting information for your articles
Scrape statistic sites, case studies, reports, infographics, everything that may help you make your own content more valuable and engaging for your audience. Make your posts of superior quality.
Web data scraping technology is a splendid instrument for SEO when done right, so make sure you use the right tool or service provider for the purpose. Automate or outsource scraping and jump right into the thick of your content quality. Do your best to make it engaging for your readers and well ranked by Google.
#wev #data #scraping
Have you ever wondered how companies started to maintain and store big data? Well, flash drives were only prevalent at the start of the millennium. But with the advancement of the internet and technology, the big data analytics industry is projected to reach $103 billion by 2027, according to** Statista**.
As the need to store big data and access instantly increases at an alarming rate, scraping and web crawling technologies are becoming more and more useful. Today, companies mainly use web scraping technology to regulate price, calculate the consumer satisfaction index, and assess its intelligence. Read on to find the uses of cloud-based web scraping for big data apps.
#data-analytics #web-scraping #big-data #cloud based web scraping for big data applications #big data applications #cloud based web scraping
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
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When scraping a website with Python, it’s common to use the
Requestslibraries to send
GETrequests to the server in order to receive its information.
However, you’ll eventually need to send some information to the website yourself before receiving the data you want, maybe because it’s necessary to perform a log-in or to interact somehow with the page.
To execute such interactions, Selenium is a frequently used tool. However, it also comes with some downsides as it’s a bit slow and can also be quite unstable sometimes. The alternative is to send a
POSTrequest containing the information the website needs using the request library.
In fact, when compared to Requests, Selenium becomes a very slow approach since it does the entire work of actually opening your browser to navigate through the websites you’ll collect data from. Of course, depending on the problem, you’ll eventually need to use it, but for some other situations, a
POSTrequest may be your best option, which makes it an important tool for your web scraping toolbox.
In this article, we’ll see a brief introduction to the
POSTmethod and how it can be implemented to improve your web scraping routines.
#python #web-scraping #requests #web-scraping-with-python #data-science #data-collection #python-tutorials #data-scraping