How to Extract Amazon & Other Big E-Commerce Websites on a Big Scale?

The e-commerce business has become more and more data-driven. Extracting products data from Amazon as well as other big-scale e-commerce sites is an important piece of pricing intelligence. There is a huge data volume in Amazon only (120+ million as of now). Scraping this data daily is an enormous task.

At Retailgators, we deal with numerous customers to help them get data access.

However, some people want to set an in-house team for scraping data for different reasons. This blog helps people know how to set as well as scale your in-house team.

Understand E-Commerce Data

We have to understand data that we’re scraping. For demonstration objective – let’s select Amazon. The data fields that we need to scrape:

• Product’s URL
• Product’s Description
• Product’s Name
• Discounts
• Prices
• Image’s URL
• Stock Information
• Average Star Ratings

The Frequency

The refreshing frequency is diverse for various subcategories. From 20 subcategories, 10 subcategories require refresh every day, five require data one time in two days, three require data one time in three days as well as two require data one time in a week. The frequency might change later relying on how business team priorities change.

Understand Particular Requirements

While working with big data scraping projects for our enterprise clients - they always ask for special requirements. All these are done for making sure internal compliance strategies or improving the competence of an internal procedure.

Let’s go through some special requests:

Get a copy of scraped HTML (unparsed data) discarded into the storage system including Amazon S3 or Dropbox.

Create an integration using the tool for monitoring the development of web extraction. Integrations might be an easy slack integration for notifying while data delivery gets completed or build a hard pipeline to the BI tools.

Having screenshots from a product page.

In case, you have some requirements, you have to plan more. A general case is saving data to analyze it later.

Challenges on Data Management

Organizing a huge volume of data comes with many challenges. Might be you get data, storing, as well as utilizing data comes with the entire new level of functional and technical challenges. The data amount you are gathering would only continue for increasing. Although, without appropriate foundation in place of using a huge amount of data, the organizations won’t get the finest value out from it.

1. Data Storage

You require to store data in the database to do processing. The Q&A tools as well as other systems would scrape data from a database. Your database requires to get fault-tolerant and scalable.

2. Understand the Requirements for the Cloud-Hosted Podium

In case, the data is the must-have for a company, web scraping platform is required. You can’t work on scrapers to terminal each time. Just go through some details why you need to think about investing in creating a platform at the beginning.

3. Frequently Need Data

If you need data frequently and automate the scheduling part, you need a platform with a combined scheduler to run a data scraper. Having a graphic user interface is superior as even non-technical people might start the web scraper just by clicking on a button.

4. Dependability is a Must

Running e-commerce data scrapers on the local machine is not a very good idea. You require a cloud-hosted platform to provide a dependable data supply. Use the current services of Google cloud platform or Amazon Web services to create a cloud-hosted platform.

5. Anti-Extraction Technologies

You require the capability of integrating tools to avoid anti-extraction technologies, as well as the finest way of doing that is to connect the API to a cloud-based platform.

6. Data Sharing

Sharing data with internal stakeholders could get automated in case, you could integrate the data storage using Amazon S3 Azure storage or more. Most analytics as well as other data groundwork tools in the market are having native Google cloud or Amazon S3 platform integrations.

7. DevOps

With DevOps, any application begins as well as used to get a hectic procedure. But not anymore. Google Cloud, AWS platform or similar services offer flexible tools made to assist you create applications more reliably and quickly.

8. Change in Management

According to a way a business team utilizes scraped data, there would be changes. All the changes might be in the data structure, changes in refreshing frequency or anything else. Managing the changes are extremely process-driven. Depending on the experience – the finest way of managing changes is to perform fundamental things right.

9. Team Management

Organizing a process-driven team for a big-scale data scraping project is extremely hard. Although, here is the basic idea about how a team needs to split to deal with the data scraping project.

10. Conflict Resolution

Creating a team is extremely hard; and organizing them is harder. We’re a huge supporter of “disagree & commit” idea of Jeff Bezos. This idea breaks down to some easy ideas, which become useful while you are creating an in-house team.

How to Make an Unhappy Person Get Involved and Offer the Finest Work?

To solve this problem, you need to start preparation even before reaching the situation. While making a team, you need to clearly describe these steps to all the team members.

• Step 1: Make people realize why it is important to put interests of a company in front of the individual whereas an individual is still getting the role during the execution. We usually provide an example about how well the forces are working as well as how the idea of priority of the interests is the most important part of a mission’s success.
• Step 2: Help the team members to understand how decision-making procedure will take place in case, there is the tie-break situation. Here, we will ask them in bringing data to prove points as well as compare them alongside. We will make a decision depending on our judgment.
• Step 3: Make them realize the significance of trusting a leader’s judgment. Although they don’t settle with that, they have to commit to a decision as well as deliver the best work for making an idea successful.
• Step 4: Make people realize the significance of getting parameters as well as rules to follow and why this is important to be creative within these parameters.

Compartmentalization for Better Efficiency

It is important to compartmentalize a Business team and a data team. In case, a team member gets associated in both - the project is intended to fail. Let the data team perform what they perform the best as well as similar in case of a Business team.

Do you want to need a free consultation? Contact Retailgators now!