Leverage the powerful combination of web scraping, AI, cloud and big data to grow your business.
Data is the new oil of the digital economy, according to Wired.
With the explosive growth of online information, your ability to leverage the combination of web scraping, artificial intelligence (AI), cloud computing and big data analytics is a game-changer for your business to capitalize on the wealth of web data.
From e-commerce and real estate to finance and travel, web scraping has the power to transform your business, by unlocking insights from the vast amount of online data, which continues to grow at an exponential pace.
Imagine being able to gather near-real-time data on your competitors, track market trends and make data-driven decisions with ease; all powered by data from across the web.
With the combination of web scraping, AI, cloud and big data, you can make such aspirations a reality.
So, buckle up and get ready to dive into the latest trends on and predictions for opportunities to use web scraping to capitalize on the massive growth of web data.
Online data is experiencing explosive growth
Here are some key stats from FinancesOnline that highlight the speed and scale of online data growth:
- Each person on earth creates 1.7 MB of data each day, on average (Source: Northeastern University).
- Everyday, a total of 2.5 quintillion bytes of data is created (Source: SG Analytics). For context, 2.5 quintillion bytes is equal to 2.5 million terabytes of data.
- The total amount of data created every single day is equivalent to stacking 10 million CDs on top of each other. The total height of the stacked CDs would be as tall as two Eiffel Towers (Source: Dihuni).
- Stored data is growing 5x faster than the world economy (Source: Dihuni).
What is Web Scraping?
Web Scraping is the process of automatically extracting data from web pages.
Websites typically render web pages on your browser in the form of HTML (HyperText Markup Language). Therefore, web scraping traverses HTML documents and strips out HTML elements, in order to extract only the useful data from the web document.
Web Scraping simulates and automates the actions of a human browsing a web page, in order to extract useful data points from the web page.
Web scraping enables you to perform such web browsing faster and across several websites at a time, so you can quickly extract data at scale from the web.
Businesses are widely utilizing web scraping to automate a variety of use cases, such as price comparison, market research, online reputation management and link building for SEO.
The list of use cases for web scraping is long and growing.
For the do-it-yourselfers, the Python language is widely-used for web scraping. Typical usage of Python for web scraping will include leveraging Python libraries – such as Requests, URLLib, Beautiful Soup and Scrapy – for HTML document navigation, requests and responses.
Businesses looking to quickly and effectively capitalize on web scraping at scale will typically partner with a web scraping service provider that handles what can be the tedious and demanding task of accurately extracting data from several websites.
Looking to capitalize on web data now?
WSaaS is your perfect solution for lightspeed web scraping.
We have built an AI-powered, cloud-based, industrial-grade web scraping engine that can quickly and effectively extract data at massive scale from any website.
Backed by professional services from some of the best, cloud-certified data engineers and data scientists on the planet, we deliver to you a 360-degree value proposition to grow your business using web data.
Grow your Business with Web Scraping: Unlock Opportunity with Several Use Cases
Web Scraping has become an indispensable tool for businesses of all sizes looking to harness the power of web data.
From online marketing to online retail; traditional investments to alternative investments; real estate and housing; to travel, hotels and airlines; web scraping provides a wealth of data that you can leverage to power data-driven decisions that revolutionize your business.
The use cases for web scraping are varied and continue to expand. For example, you can leverage web scraping to extract:
- Stock market, financial and economic data to drive investment and trading decisions
- Real estate and housing data to keep track of prices, inventory and demand
- Job listings and human capital data to gain insights into the job market and inform hiring decisions
- Historical data from multiple sources to inform research, consulting and journalism
- Machine-readable data to power on-demand data-as-a-service APIs, as well as web and mobile applications
Web Scraping is a versatile and powerful capability that can empower your business to stay ahead of the competition by enabling the generation of valuable insights from the vast world of web data.
Whether you're looking to inform your investment decisions, stay up-to-date on real estate prices, or automatically uncover news stories, web scraping can drive your data extraction use case to enable you to achieve your business goals.
Web Scraping capabilities continue to evolve along with the rest of the software industry.
The growth of cloud computing, big data and artificial intelligence/machine learning (AI/ML) technologies, in particular, have an outsized impact on the increasingly more robust capabilities of web scraping.
Read on to learn more on how web scraping, cloud, big data, AI/ML work together as a powerful combination to grow your business.
Web Scraping + The Cloud = A Perfect Match
As with other digital-native tech companies, the best web scraping companies possess first-class integrations with the cloud.
Customers are increasingly moving to the cloud so the best web scraping companies ensure that they meet their customers exactly where their customers live – which is increasingly on the cloud.
Therefore, the best web scraping companies are increasingly building their data extraction, processing, storage and analytics systems on and for the cloud.
From taking a purely Infrastructure-as-a-Service (IaaS) approach to storage (E.g., Amazon S3, Google Cloud Storage, Azure Blob Storage) and compute (E.g., Amazon EC2, Google Compute Engine, Azure Virtual Machines); to fully-managed Software-as-a-Service (SaaS), such as Data Warehouse-as-a-Service (E.g., Snowflake, Google Cloud BigQuery, Amazon Redshift); the best web scraping companies are cloud-first / cloud-native.
Web Scraping companies that fully embrace the cloud position themselves to offer faster and more reliable web scraping services to their customers.
By leveraging the scalability and reliability of the cloud, web scraping companies can deliver more data, faster and with more robust data quality guarantees, than their on-premises competitors.
Furthermore, by embracing the cloud, web scraping companies are able to shrink hardware costs, scale up operational efficiency and boost overall competitiveness.
Cloud-native web scraping companies can offer their customers access to the latest and greatest data analytics and data visualization capabilities, to enable quick and cost-effective generation of valuable insights from web data.
The integration of web scraping with the cloud is a perfect match that enables web scraping companies to provide high-quality, reliable and cost-effective value from web data for their customers.
Interested in generating data-driven insights with a cloud-based web scraping service?
We have built a web scraping service that integrates with all major cloud platforms: AWS, Google Cloud, Azure, Snowflake and Databricks.
Get started with us today to leverage the power of the cloud to scale your web data extraction and analytics!
The Best Web Scraping Companies are Big Data Analytics Engineering Companies
Given the rapid growth of online data, all data is increasingly becoming big data – data that is high velocity, high volume and high variety.
The capability of a web scraping company to process fast-moving, large amounts of data – that is unstructured (E.g., Freetext customer reviews), semi-structured (E.g., HTML documents), somewhat structured (E.g., HTML tables) or well-structured (E.g., JSON API response) – is no longer just a nice-to-have capability.
Big data analytics engineering capabilities to quickly and efficiently perform custom data transformations on web-scale data is a required core competency for the best web scraping companies.
The best web scraping companies possess a deep understanding of large-scale distributed data processing and a mastery of big data analytics engineering techniques and best practices.
The capability to quickly and efficiently extract and transform massive amounts of web data, regardless of its structure, is a core skill set for the best web scraping companies.
Interested in partnering with some of the best, cloud-certified big data analytics engineers on the planet to enable you to capitalize on web data?
We have built a web-scale web scraping and data analytics engineering platform that can extract data and generate insights from any website.
Furthermore, we complement our awesome platform with a US-based professional service that will guide you through every step of extracting raw data through generating valuable insights to grow your business.
Get started with us today to experience the power of big data analytics to grow your business!
Massive Opportunities Ahead for Web Scraping to Power the Growth of AI/ML
In 2011, Marc Andreessen, Co-Founder and General Partner of VC powerhouse Andreesen Horowitz, famously said that Software is Eating the World.
Six years later, Jensen Huang, Co-Founder and CEO of the leading company for GPU AI building blocks, Nvidia, said that AI is Going to Eat Software.
The ability of web scraping companies to leverage AI to auto-generate data extraction algorithms to extract data from websites will increasingly become the norm; particularly so, given the high velocity with which new web pages are coming online.
Furthermore, the web scraping companies that leverage the power of AI to automatically adapt their data extraction engines to changes in website structure will increasingly differentiate such forward-leaning web scraping companies from the rest of the pack.
A particularly interesting opportunity lies at the intersection of AI/ML and web scraping.
AI/ML companies require massive amounts of data to train their models. As is the case with the OpenAI phenomenon ChatGPT, AI/ML companies source much of their training data from web pages.
As the AI /ML industry continues on its massive growth trajectory, there will be increasing demand for the extraction of raw web data to train AI/ML models.
Additionally, as AI/ML use cases become more targeted, AI/ML companies will increasingly run into the challenge of limited volumes of the specific kinds of data required to train such highly-targeted AI/ML models.
Therefore, the growth of highly-targeted AI/ML models will increasingly drive increased demand for synthetic data to train such models.
Synthetic data is annotated information that computer simulations or algorithms generate as an alternative to real-world data.Nvidia
Put another way, synthetic data is created in digital worlds rather than collected from or measured in the real world.
It may be artificial, but synthetic data reflects real-world data, mathematically or statistically. Research demonstrates it can be as good or even better for training an AI model than data based on actual objects, events or people.
Web scraping companies that are AI-first and possess capabilities to extract and transform raw web data into high-quality machine learning training data are well-positioned to contribute to and grow with the rapid ascent of AI/ML.
AI-powered web scraping companies that can leverage AI to expand their footprint along the data value chain will capture immense value.
Opportunities to leverage AI, beyond the foundational demand to automate data extraction from websites, include implementing:
- Data quality assurance and correction: Use AI to automatically detect and resolve data quality problems, ensuring the accuracy and completeness of the extracted information.
- Automated data labeling: Utilize AI to automatically label and categorize web data, making it easier for AI companies to train their machine learning models.
- Data augmentation: Enhance web data by automatically enriching it with additional data sources, such as weather data, demographic data, and more.
- Custom data transformations: Use AI to perform custom data transformations on web-scale data, such as converting unstructured data into structured data or transforming data into a specific format.
- Data integration: Leverage AI to integrate web data with other data sources, such as CRM data, ERP systems, human capital data, financial data and more, to create a comprehensive data set for AI companies to use.
- Predictive analytics: Use AI to perform predictive analytics on web data, such as predicting sales trends, customer behavior and more.
- Sentiment analysis: Implement machine learning models that analyze text data from websites and social media platforms to gauge the emotions and opinions of users on various topics. Companies can use such sentiment data to better understand their customers, track brand reputation and make data-driven business decisions based on public opinion.
Interested in leveraging the power of AI to generate data-driven insights from web data that give you a competitive edge?
WSaaS has built an AI-powered web scraping engine that can extract data from any website.
Get started with us today to capitalize on the power of AI to revolutionize your business with web data.
Recent cases point to an increasingly strong legal position for web scraping companies
Given recent cases by social media companies against web data extraction companies, it is quite important to consider the legality of web scraping.
In fact, in the Meta vs Bright Data case, Meta conceded that "The collection of data from websites can serve legitimate integrity and commercial purposes, if done lawfully and in accordance with those websites' terms," in a statement from Meta Spokesman, Andy Stone.
The Future of Web Scraping: Trends and Predictions
Research from Market Research Future (MRFR) forecasts that the web scraping software market will grow at a compound annual growth rate (CAGR) of 13.48% to reach $1.73 billion by 2030.
The forecasted growth rate for the web scraping software market is not too far from the expected growth for other fast-growth markets, such as cloud computing. For context, Facts and Factors forecasts that cloud computing will grow at a CAGR of about 15.80% through 2028.
So, clearly, the prospects for web scraping are bright.
The web scraping industry is rapidly evolving, particularly so, given the rise of cloud computing, big data, and AI/ML.
The best web scraping companies are cloud-first big data engineering and AI companies.
Interested in partnering with a cloud-first, big data engineering and AI-powered web scraping company?
WSaaS is your perfect partner for lightspeed web scraping.
Get started with us today to grow your business with modern, cutting-edge web scraping capabilities backed by some of the best, cloud-certified data engineers and data scientists in the industry.
Web Scraping is a powerful and versatile capability for businesses of all kinds, from Fortune 500 companies to solopreneurs, looking to harness the wealth of data available on the web.
The combination of web scraping with the cloud, big data, and AI/ML are key drivers of the rapid growth of the web scraping market, which is forecasted to reach $1.73 billion by 2030.
With recent legal cases adding more clarity to the bounds within which web scraping is legal, web scraping is poised to play an increasingly important role in powering the growth of businesses that are looking to stay ahead of the competition by leveraging valuable data-driven insights.
Recent cases point to the fact that web scraping is legal, as long as it is performed within certain bounds.
Web Scraping companies that are cloud-first, big data engineering-capable and AI-powered are best positioned to capture value along the data value chain, from data extraction, to custom data transformation, to advanced data analytics. Such cutting-edge web scraping companies will deliver faster and more robust value-added web scraping offerings.
Web scraping is well-poised to play an increasingly important role in the growth of AI/ML.
Therefore, there is an exciting future ahead for web scraping.
Get started with a cloud-first, big data engineering and AI-powered web scraping company today to start capitalizing on the power of web data to grow your business.
Frequently Asked Questions (FAQs)
What are the most important factors to consider when choosing a web scraping service?
When choosing a web scraping service, it is important to consider the following factors:
- Data extraction capabilities: Ensure that the web scraping service can extract data from your source websites and can handle the volume of data you require.
- Data quality: Check that the service can deliver high-quality, accurate and complete data.
- Speed: Consider the speed at which the web scraping service can extract and deliver data, particularly if you need data in near-real-time.
- Technical support: Choose a service that provides excellent technical support to help you quickly and effectively resolve issues you may encounter.
- Integration with other platforms: Consider the service's ability to integrate with other platforms, such as your data warehouse or cloud managed services.
- Cost: Compare the costs of different services to ensure that you are getting the best value for your money. However, beware of cheap web scraping companies, as you will likely end up paying multiples of the advertised cost to address low-quality data or poor service levels.
For a detailed outline of steps to follow when picking a web scraping company, see How to Find the Best Web Scraping Consultant in 6 Easy Steps.
How is web scraping different from other data extraction methods?
Web Scraping is a method of extracting data from websites, while other data extraction methods may include requesting data from APIs or data scraping from PDFs.
The key difference between web scraping and other data extraction methods is that web scraping enables the automatic and scalable extraction of data from websites, whereas other methods may be limited in terms of the kinds of data they can extract.
Another key difference between web scraping and requesting data from APIs, specifically, is that web scraping enables you to extract the data you want directly from any website.
However, with APIs, you can extract only data that the API provider has decided to make available for you via the API. Therefore, web scraping gives you more control over the breadth of data you can extract from any given web data source.
Are there any risks involved in the use of web scraping for business purposes?
When using web scraping for business purposes, there are potential risks to consider.
Firstly, web scraping may violate the terms of service or policies of the source website.
To eliminate such a legal risk, it is important to choose a reputable web scraping professional service that will guide you through the entire process to ensure you follow best practices and adhere to the terms of service and policies of the source websites.
Interested in learning more on the legality of web scraping? Read Is Web Scraping Legal?
Secondly, web scraping can result in inaccurate or incomplete data if the data extraction algorithms are not properly designed and maintained.
Incomplete or inaccurate data can lead to poor decision-making and harm the performance of your business.
You can address data quality risk by working with a top-notch web scraping service with extensive data transformation experience that implements robust data quality checks on your web data.
WSaaS has a 100% data quality guarantee on every single data point we deliver.
Get started with us today!