Entrepreneurs and business leaders use data to improve team performance, increase revenue, and make better decisions. Analyzing and gathering data is one of the most important aspects of any data-driven business. The first thing you must do is find out where it is located. The process of extracting data from a database or another source at scale is called enterprise data extraction. This can be done manually or using software designed specifically for this purpose.
Regardless of how you extract data, learning how to do so will help you make better business decisions. It can be daunting to build a data extraction infrastructure for an enterprise, but it doesn’t have to be. In web scraping projects, there are various elements to consider, and finding a solution that meets your custom needs is essential. Our goal is to help you better understand the process by providing an outline of the key steps to creating a successful infrastructure.
For your web scraping project to be successful, you need an architecture that is well-crafted and scalable. The information in this article can be used for lead generation, price analysis, market research, etc. It will help you realize the significance of scalable architectures, efficient crawls, proxy, and automated data quality assurance.
Extraction of data refers to the process of extracting information from databases or other sources. Data can be extracted from both structured and unstructured sources using this method. The data extraction process can be done manually, but it is typically automated through a tool. If the data needs to be transformed into another format, it is stored in cloud or on-premises locations.
Depending on how much data you need to extract, the data extraction process can be quite simple or quite complex. A query and analysis are then performed on the new database to obtain any relevant information. Reports and dashboards can then be created using the data to help businesses make decisions.
The extract, transform, and load process is used when moving data between environments. Before loading data into a new target system, data that needs to be transferred between systems must be extracted. In Extract, Transform, and Load (ETL), this is the most crucial step.
Data extraction is essential whenever an organization needs to gather large amounts of data for analysis or tracking. Combining data from various sources makes it easier to standardize, organize, track, and manage information. The tool allows organizations to extract specific data points from a larger datasets. Strategic decisions can be made more effectively using data.
Organizations depend on data extraction because it improves accuracy, reduces human error, and reduces time spent on repetitive tasks. By automating manual processes, data extraction makes business processes more efficient. Data such as historical trend analysis can be stored for future analysis and reporting purposes. Streamlining business processes and reducing costs can be achieved by extracting data.
To implement a large-scale web scraping project, a scalable architecture must be developed first. You should have an index page that links to all the other pages you wish to extract. An enterprise data extraction tool can make creating index pages more accessible and faster.
It is common for an index page to contain links to other pages which need to be scrapped. Regarding e-commerce, these pages are typically category “shelf” pages containing links to numerous product pages. The individual blog posts are always linked from a blog feed for blog articles. The discovery and extraction spiders should be separated, however, if you want to scale enterprise data extraction.
In an e-commerce project, enterprise data extraction would involve developing one spider, the product discovery spider, for discovering and storing the URLs of products in target categories and another spider for scraping the product data. By using this approach, you can allocate more resources to one process over the other, and you can avoid bottlenecks by splitting the two core processes of web scraping, crawling, and scraping.
Building an enterprise data extraction infrastructure that produces high output depends heavily on spider design and crawling efficiency. When scraping at scale, you need to configure your hardware and spiders for high performance after developing a scalable architecture during the planning stages.
Enterprise data extraction projects often deal with speed issues when developing at scale. The spiders of e-commerce companies need to have scraped their competitors’ entire catalogs of products within a couple hours in order to adjust their pricing based on price intelligence data. A lot of enterprise-scale applications require spiders to finish their scrapes within a reasonable amount of time.
To configure a system, teams should consider the following steps:
a. Understand the web scraping software in depth.
b. Enhance crawling speed by fine-tuning your hardware and spiders.
c. Scalable scraping requires the appropriate hardware and crawling efficiency.
d. Make sure team efforts aren’t wasted on unnecessary tasks.
e. When deploying configurations, keep speed in mind
Developing an enterprise-level scraping infrastructure poses significant challenges due to this need for speed. Make sure your scraping team isn’t wasting fractions of a second on unnecessary processes and squeeze every last ounce of speed out of your hardware. For this reason, enterprise web scraping teams should acquire a comprehensive understanding of the market for proxy scraper software and the frameworks they use.
It would be best if you always focused on crawling efficiency and robustness to scale enterprise data extraction projects. The goal should be only to get the data you require with the fewest requests and the highest confidence level. You can crawl a website at a slower pace if you make any additional requests or extract data. Consequently, you will have to navigate hundreds of websites with sloppy code on top of constantly evolving websites.
It is advisable to expect your target website to make changes that break your spider every 2-3 months (loss of coverage or quality of data extraction). A product extraction spider should be able to handle all the different rules and schemes used by different webpage layouts instead of having multiple spiders for each layout a target website might use. Your spiders should be as configurable as possible.
To improve crawl efficiency, consider the following points:
If you don’t need images, don’t request them or extract them.
It would be best if you made your spiders as configurable as possible.
Whenever using multiple spiders, make sure you target the last possible layout of the site.
Make sure you are using a headless browser.
Your scraping should be limited to the index and category pages.
You should avoid requesting each product page if you can get the information you need from the shelf page (e.g., product names, prices, ratings, etc.) without requesting each product page. The engineering team needs to fix any broken spiders within a couple of days, which isn’t always possible for most companies that need to extract product data daily.
We have developed the best data extraction tool, Crawlbase, to use in these situations until the spider can be repaired. With the help of this tool, the target website’s fields are automatically identified (product name, price, currency, image, SKU, etc.) and returned.
Your enterprise data extraction project also requires a scalable proxy management infrastructure. You need a managed cloud based proxy to scrape the web reliably and target location-specific data at scale. Your team will spend a lot of time managing proxies without healthy and well-managed proxies and will not be able to scrape effectively at scale without them.
Obtaining enterprise data at scale requires an extensive proxy list, IP rotation, request throttling, session management, and blacklisting logic to prevent your proxies from being blocked.
A system for automated data quality assurance is essential to any enterprise data extraction project. An often overlooked aspect of web scraping is data quality assurance. When they run into serious problems, everyone is so focused on building spiders and managing proxies that they rarely think about QA.
The quality of data extracted from an enterprise is directly related to the quality of the data produced from it. Suppose you don’t have a robust system to ensure you receive a reliable stream of highly qualified information for your enterprise data extraction project. You won’t even have the most sophisticated web scraping infrastructure in that case.
When it comes to large-scale web scraping projects, making it as automated as possible is the key to ensuring data quality. Trying to manually validate the quality of your data when scraping millions of records per day is impossible.
Understanding your enterprise data extraction requirements and designing your architecture accordingly is the key to building a successful data extraction infrastructure. The crawl efficiency of such an architecture should also not be ignored.
It doesn’t matter what file format you have, what collection of content you have, or how complicated a document is; Crawlbase can handle it. With Crawlbase’s Crawler, purpose-built for data extraction, you can automatically and at scale discover, standardize, and extract the best quality data from complex documents and websites.
Analysis of reliable and valuable data will be easy once all of the elements for enterprise data extraction are in place and working smoothly with high-quality data extraction automation.