Though often used interchangeably, data pipelines and ETL are two different methodologies for managing and structuring data. ETL tools are used for data extraction, transformation, and loading. Whereas data pipelines encompass the entire set of processes applied to data as it moves from one system to another. Sometimes data pipelines involve transformation, and sometimes they do not.
While both processes are instrumental in moving data from various sources into a single location, ETL pipelines run in batches, and data pipelines often run in real-time. Let’s explore the two processes to clear up any confusion surrounding their terminology.
A data pipeline has three separate stages:
The data source
Processing actions applied within the pipeline
The data destination
Data pipelines can facilitate one-way movements or be duplicated to return data from the destination to the source. This means data must be in the same format, structure, or schema for a data pipeline to operate, as it only facilitates data movement (not extraction or transformation).
A streaming data pipeline can allow for multiple outputs from the pipeline. For example, three applications can receive data using a single pipeline, with scheduling handled by a stream processing engine in real-time.
Other data pipeline styles include batch processing, where high volumes of data are moved at regular intervals using a job scheduler.
An extract, transform, load pipeline offers more options to data users compared to data pipelines. One option is to extract data, which is reading the data from the source.
Next, you have transform, which can change the format, structure, or schema of data to achieve compatibility with a new data destination. This occurs on the fly within the pipeline, preparing data for the next stage.
Finally, we have load. This is where transformed data is loaded into the target database or data lake.
Pro tip: ELT pipelines follow an Extract, Load, Transform process. This is typically used for data warehouse environments, where the data is extracted and loaded as-is, before being transformed by the data warehouse platform at a later stage.
Data pipelines and ETL pipelines are critical for data-driven organizations. Having a reliable pipeline saves data engineers time and resources by eliminating errors, bottlenecks, and latency to smoothly transition data from one system to another. Here are some of their primary use cases:
As a general rule of thumb, data pipelines do not include transformation in the pipeline. For this reason, data pipelines should be reserved for data translation, or movement, between matching sources and destinations. This could include an SQL database on AWS, with data transfer to an SQL database on Azure.
Extract, Transform, Load (ETL) pipelines are more suited to transformation than simply translation. This is because ETL pipelines take a source dataset, convert it into a different format, structure, or schema, and load it into the target data location. This could be an SQL database on Azure or a data warehouse on Amazon RDS for PostgreSQL.
Scaling ETL Pipelines
ETL pipelines are used to extract, transform, and load data. When the data quantities grow, scaling is a necessity. Here are a few questions to consider when setting up your ETL pipelines:
1. How much bandwidth does my enterprise currently use for data operations?
2. Can the chosen ETL pipeline tool manage these data quantities now and in the future?
Operating ETL pipelines in the cloud, rather than on-premises, is another way to ensure scalability in the long term.
Focusing on the T in ETL
Transformation is a key part of ETL pipelines. However, manipulating data can lead to unexpected outcomes such as duplicate entries, data omission, or corruption. First, monitor for human error during the configuration and deployment of an ETL pipeline. Ensure that the pipeline is rigorously tested before going live to ensure that all data arrives intact. Finally, consider the natural error rate of an ETL tool and consult an ETL specialist to avoid any headaches.
Transforming Between Data Formats, Structures, and Schemas
There are many different structures to data:
1. Unstructured Data – This is unprocessed, raw data as output by the tool, application, or software like Server log data, RAW photo file formats, and alphanumeric text.
3. Structured Data – This type is represented by columns and rows in a database. This is typically part of a relational database management system (RDBMS).
JPG / JPEG
DOC / DOCX
Data pipelines follow a standardized set of processes, meaning they can often be duplicated and reused across a variety of predictive analytics, business intelligence, and enterprise applications or services. Data pipelines also expedite data transfer, allowing enterprises to integrate new data sources at a pace ready for data analytics.
This is particularly true when using high-bandwidth cloud infrastructure. Both data quality and security can be monitored within the pipeline. Data pipelines can facilitate real-time data transfer for interactive applications. This is in contrast with ETL pipelines that operate on a batch process.
Data pipelines cannot perform advanced ETL processes within the pipeline. At most, they can perform basic transforms and data editing workflows. Misconfigured data pipelines can send bad data or leak the data to attackers. This creates an inherent security and data integrity risk if not properly governed.
Various sources can be transformed into a more desirable target format for use in business intelligence, analytics, and enterprise services. ETL pipelines allow you to skip staging areas in your data transformation workflow.
Instead, the data is transformed in real-time and output to the target. ETL is also easy to visualize, meaning your IT teams can decipher the data flow and system hierarchies to better understand how to mitigate data hurdles in the future.
Real-time applications are not suited to the ETL stack. Transforming data on the fly introduces unnecessary latency, which would result in significant performance problems. Data developers and data engineers will know how to use ETL pipelines, but laypersons cannot leverage this technology due to coding requirements. Changing target system, software, or service requirements can require a revamp of the ETL pipeline, requiring resources and investment.
Traditionally, it takes years and over $100,000 for organizations to construct a reliable ETL pipeline for transporting data from multiple sources. Luckily, Trianz developed EVOVE to expedite this process, automating extract transform load (ETL) operations in real-time during data migration.
Using our automated migration framework, Trianz is able to maintain data governance and accelerate the cataloging of legacy tool metadata for rapid transformation. Our approach to ETL migration reduces errors, avoids disruption, and enables our teams to reduce migration cycle times by up to 50%. If you are interested in learning more about ETL, check out our ETL migration with AWS Glue page.
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