Most modern enterprises make use of a suite of tools to run their business operations smoothly. For example, the marketing team might rely on Hubspot for marketing automation, the product team might use BigQuery to store insights, and the sales team might use Zendesk Sell to manage leads. That means fixed data remains under the control of one department. Sales data is only accessed by the sales team and remains isolated from the rest of the organisation.
Even if other teams like the marketing team somehow manage to extract data from all different sources by writing codes and putting it into an excel sheet for analysis, it may cause issues, such as data redundancy and data inconsistency. To establish consistency among data from source to destination, we need a fully managed system that automatically extracts data from numerous sources, transforms, validates, and saves loads into a single destination. This blog will discuss definitions, use case examples, and practical advice that will help you understand the Data Pipelines and how they differ from ETL.
What is a Data Pipeline?
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A data pipeline is a sequence of data processing steps. In the series of steps, the previous stage's output would be the input of the next step. This will go on till the pipeline is completed. It has three main essential components: a source, processing step or steps, and a target. More specifically, it is the process of moving data from source to destination, possibly transforming the data along the way.
The methods of storing and processing data in the source systems differ from target systems. Therefore, it involves software that automates the steps such as movement, transformation, and validation of the data between sources to a target repository. The data pipeline's key role is to ensure all of these processes are performed consistently to all data.
Data Pipeline Use Cases
- Data integration can be performed by using a data pipeline
- It is used to perform predictive analysis
- It is helpful for people who store data in multiple sources, require real-time analysis
- To move, process, and load data from a source to a destination.
- Helps in delivering fast, and accurate data-driven decision
- Stored data in the cloud
What is an ETL pipeline?
ETL stands for Extract, Transform, and Load. An ETL pipeline is a set of operations that include pulling data from a source, modifying the format of the data set to match the target repository and then loading it to the destination. The destination could be a database, data warehouse or data mart. The ETL would perform the following steps:
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1. Extract: Extracts the data from one or many sources
2. Transform: Convert the data into a consistent format
3. Load: Finally, load the transformed data into a target repository
ETL Pipeline Use Cases
- Centralising heterogeneous data sources in one place and provides a consolidated view of data
- Providing standardised datasets to data analytics tool
- Building a data lake
- Setting up data migration
Data Pipelines Vs. ETL
However, data pipeline and ETL are two different terms, but they are often used interchangeably. They both are responsible for loading data from a source to a destination; the key difference is in the application.
The following are the three key differences between data pipelines and ETL pipelines
1. A data pipeline is an umbrella term that encloses ETL as a subset
An ETL pipeline ends in loading the transformed data to a target repository, such as a data warehouse or database. You don't have to load the data in a data warehouse or database in a data pipeline. You can load it into any number of repository systems, such as data lake, AWS bucket. Moreover, it can also activate a webhook on another system to initiate business processes.
2. ETL always involves transformation
As you all know, ETL is a set of operations that include extraction of the data from a source, transforming it, and then loading it into the destination. Precisely, the main purpose of the data pipelines is to transfer the data from a source to a destination; transformation may or may not be involved.
3. Data Pipelines run in real-time, whereas ETL runs in batches
Another significant between them is that ETL usually moves data to a destination in chunks- for example, the pipeline could be run every 12 hours or twice a day. Whereas data pipelines are often run in real-time with streaming computing which means data is constantly updated.
Summary
Data and ETL pipelines both are data transformation techniques. They are capable to move, transforming and loading unstructured and structured data. The major advantage of using data pipelines over ETL pipelines is that you can build data pipelines for any application that uses data. Whereas, ETL pipelines are built for only data warehouse applications and data marts.
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