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Data pipelines vs ETL pipelines
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ETL Pipeline vs Data Pipeline: An Overview

What Is an ETL Pipeline?

Extract, Transform and Load, significantly known as ETL pipeline is a set of processes where you extract data from a particular source, and then transform it before you load it into the destination. The source can derive itself from anywhere, be it business systems or transaction databases. The final destination of landing for this data source would be a data warehouse, database or cloud-oriented database from service providers such as Google BigQuery, RedShift and Snowflake. Most influential use Cases for ETL Pipelines comprise of centralising your company data, transferring your data sources into a data warehouse or database or internally and enriching your CRM system with a whooping amount of data. This article will summarize the difference between ETL pipeline vs Data Pipeline.

How does ETL function?

In ETL Pipeline, as the name suggests, three kinds of processes go hand in hand- Extraction, Transformation and Loading. The data extraction methodologies comprise three varieties- Partial Extraction to obtain the data easily while the source system can notify record changes. Another type would be Partial Extraction with an update notification. When it comes to updating notifications, not every system can offer a notification when there is an update. In the case of full extract, certain systems would find it difficult to identify which data changes should they prioritise with the last extract copy in the same format for designating the tweaks that happen inevitably.


ETL Pipeline vs Data Pipeline

Data Pipelines and ETL Pipelines are analogous to each other but they aren’t identical twins. The basic foundation of both pipelines would associate with moving data from a single system to another.


ETL Pipelines are the subset of Data Pipeline

An ETL Pipeline doesn’t overload itself with too much of tasks on its plate, other than data loading into a data warehouse or database. When it comes to data pipeline, the loading can activate new flows and processes by triggering different webhooks in various systems.


ETL would look forward to Transformation

ETL is a synchronous process where you extract data from a specific source, transform it and load it into the output destination. It also refers to a set of processes where you extract data from a source, transform it and load it into the destination of your output. On the other hand, Data Pipelines consist of processes where you move data between various systems without transformation involved.


ETL Pipelines would be running in batches whereas Data Pipelines doesn’t

The major difference involved in ETL Pipelines is that they run in various batches whereas, in data pipelines, we move the data in chunks regularly. The ETL pipelines would be running twice on a single day or when the traffic is comparatively low. Data Pipelines would often run in the form of real-time processes involving streaming computation while you continuously update the data.

Difference between ETL & Data pipeline

Why should you leverage ETL Pipelines?

To put it in a nutshell, ETL Pipelines transform, extract and load data. You should utilise it when you want to involve business analytics and intelligence. When you need to move your data from one spot to another, ETL Pipeline can play a paramount role. They are a good choice when you involve data migration, especially when new systems go about replacing legacy applications.

Coming back to the ETL Pipeline extraction part, the data sourcing and extraction from various systems like web services, CSVs, CRMs and social media platforms would take place effortlessly. During the process transformation, you can mould the data into a simple format to facilitate better reporting. Loading the transformed data into a central hub makes it quite accessible for different stakeholders.

The main purpose of involving ETL Pipeline is for finding the right data, which can make it reporting-ready. The storage can focus on a single point to allow easy analysis and access.

An ETL tool can help developers to focus more on rules/logic, without developing the technical implementation means. This can save a lot of time whereas your development team can work on taking your business on the front, without focusing on tool development for better analysis.


To sum it up

Most often, Data Pipelines and ETL are often mistaken for each other, but they aren’t what you can deem as the same. ETL Pipelines would imply a series of processes meant for data transformation, extraction, and loading. This shouldn’t be confused with Data Pipelines, where you move the data without involving the direct transformation.

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