ELT vs. ETL: How to choose an approach for data parsing

When it comes to data parsing, imagine a conveyor belt in a factory. Data comes from many different sources and needs to be processed before it reaches the store shelves as a finished product. 

This is where two methods come into play: ETL and ELT. These two approaches to data processing help to cope with huge amounts of information. In this article we will tell you about these approaches and tell you which one to prefer.

ETL: the pipeline helps you deal with data

ETL (Extract, Transform, Load) is a classic data processing method, like a fine-tuned conveyor belt in a factory. Here's how it works:

Step One: Extract

Imagine you are picking fruit in an orchard. You're picking apples, pears, and cherries from the trees. In the context of ETL, this is the equivalent of collecting data from various sources, be it websites, databases or APIs.

Step Two: Transform

Now the fruit must be washed, cleaned and boxed ready for sale. In ETL, this step involves cleaning data from duplicates, standardizing formats, and combining information to create a unified and quality set.

Final step: Load

Finally, the packaged fruit is sent to the store shelves. In ETL, this means uploading the finished data to a centralized repository where it will be used for analytics and reports.

Imagine a large supermarket chain that wants to track prices and inventory on marketplaces. To do this:

• Extraction. Gathering product information from competitors' websites.
• Transformation. Cleanse and standardize the data so that all products are in the same format.
• Loading. Transfer data into the system to analyze and make pricing decisions.

The role of the proxy

If competitors limit the number of requests from a single IP address, proxies can act as additional “pipelines,” allowing you to bypass restrictions and ensure continuous data collection.

ELT: How a new approach increases flexibility and speed of data collection

ELT (Extract, Load, Transform) is like a dynamic conveyor belt with rapid in-place processing. In this approach, data is extracted and immediately loaded into storage where it is transformed.

Let's look at how ELT technology differs from ETL technology.

Step One: Extract

Imagine harvesting your crop, but not processing it right away. You simply load the fruit into a large storage unit.

Step Two: Load

Instead of cleaning and sorting the fruit on site, you send it to a large storage facility (cloud) where you will process it later.

Final step: Transform

Now that all the fruit is in one place, you can do any processing you want - from slicing to packaging. In ELT, this means that data transformation takes place in a warehouse where resources and capacity allow you to process large amounts of information.

Imagine a streaming service that collects data on user behavior:

• Extraction. Collecting information about views across devices and platforms.
• Loading. Immediately uploaded to cloud storage.
• Transformation. Based on the storage, create personalized recommendations and reports.

The role of the proxy

Just as with ETL, if data collection is limited, proxies will distribute requests and ensure continuity. Proxies allow you to bypass query limits, increasing the number of simultaneous connections and preventing IP address bans.

Now let's quickly understand what types of proxies to choose for different types of parsing.

Types of proxy servers for parsing

Each type of proxy is best suited for a specific type of parsing. Let's deal with all three types of proxies.

Mobile proxies

Mobile proxies provide IP addresses tied to mobile devices and mobile networks. 

Ideal if you collect data from sites that strictly control requests or have high anonymity requirements.

For example, for parsing social networks or marketplaces.

Residential proxies

Residential proxies provide IP addresses registered to physical devices in real homes. They mimic the behavior of real users and provide a high level of anonymity.

They are ideal for parsing sites that use sophisticated bot protection mechanisms and give access only to IP addresses that look like regular user IP addresses. They will be indispensable for collecting data from large online resources or sites with strict anti-bot protections. You can buy quality residential proxies for parsing on our website.

Server proxies

Server proxies provide IP addresses that belong to servers hosted in data centers. They can provide a high level of connection speed and stability.

They are ideal if you need high data processing speed and stable connection. For example, for parsing news sites or analytical platforms.

When choosing between ETL and ELT, it is important to consider data volume, processing speed and resource requirements. 

ETL is suitable for situations where data quality control and pre-processing are important. 

ELT, on the other hand, is better for scenarios where high flexibility and the ability to work with large amounts of data in cloud storage is required.

Proxies will improve the data collection process by providing stability and bypassing site security systems. Choose the type of proxy depending on the specific tasks and requirements for anonymity and speed. 

If you are planning large-scale data collection, check out our article on how to use APIs together with proxies. In this text, we describe how these tools work together. You can use APIs of popular sites to parsing data and save time.

We hope this article was helpful. Have fun parsing!