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ETL stands for extract, transform, and load. It’s an independent three-stage process that moves data from one source(s) to a database. Once these actions are complete, the data can then be used for reporting, analysis, and a plethora of other cases.
Throughout this blog post, several vital sections will be discussed, such as the benefits and features of an ETL pipeline, how it differs from a data pipeline, and a brief explanation of how you can build an ETL pipeline yourself with Python.
It's unsurprising that preparing, consolidating, altering, and fitting data for analytics and business intelligence provides numerous benefits. Key advantages and use cases are:
Standardizing and centralizing data to make it easily readable and available to analysts and decision-makers.
Simplification of data migration smoothes the process of moving data from legacy systems to data warehouses.
Allowing developers to focus on other tasks that aren't data maintenance or moving-related.
ETL pipelines allow you to enrich your CRM system with additional data.
Let's analyze the ETL pipeline step-by-step to further understand where the benefits mentioned above come from.
Extraction is described as this: “The purpose of the data extraction process is to collect useful data from multiple heterogeneous data sources.” Notably, the complexities of data extraction are closely dependent on the complexities of the data source. Due to this, there is a lack of uniformity when dealing with this section of the ETL pipeline. However, there are two standard extraction methods, Incremental and Full extraction.
Incremental extraction: significantly reduced load on the system as only relevant data is extracted. Such an approach features major complexities as specific metrics have to be outlined.
Full extraction: data is extracted from the source without committing changes to the logic or conditions within the source system. The benefits of such an approach lay in the lack of complex knowledge needed to initiate, though the system load may be significant if the extracted data amount is high.
The second step encompasses the process through which extracted raw data transforms into a format easily readable for various applications. The transformation stage could also be divided into three parts as the data is cleansed, mapped, and transformed, after which it’s ready to meet operational needs.
Arguably the most crucial section of the three is data cleansing, a process through which only selective data passes. Such a stage can become rather challenging when different systems start to interact since communicating between them becomes a requirement. For example, character sets available on a single system may not be in others.
Similarly, an essential point of transformation is the ability to diagnose and repair data issues since, after moving to the load step of the ETL process, doing so becomes significantly more complex.
During this phase, the converted data is loaded into a target database or a data warehouse. Note this process is not uniform and may vary widely depending on the needs of a company. Indeed, some data warehouses may want to overwrite their existing information to incremental, i.e., updating extracted information on a daily, weekly, or monthly basis. It’s not uncommon to see intervals changed to an even more rapid basis, where data can be added hourly.
Since the goal of a successful ETL pipeline is to provide value through analytics, a well-engineered ETL pipeline should:
Generate information clarity. The ETL transformation process allows relevant data to be cleaned, moved, and saved to an easily analyzable data warehouse. As a whole, such an operation gives clarity from otherwise unstructured, raw data.
Contain quality information. Before being analyzed, unnecessary data is discarded, ensuring that conclusions are drawn from trustful and relevant sources only.
Have information completeness. An effective ETL pipeline rounds up all your needed and relevant business sources into a single place, a data warehouse, for example.
Have high data velocity. In this case, velocity triggers the ETL pipeline whenever fresh data arrives in your sources. Such a feature allows you to avoid using outdated data to conclude from.
The benefits mentioned above are undoubtedly attractive, yet building something like a real-time ETL pipeline is anything but simple. Core difficulties include slow speeds of creation, numerous code complexities, and limited reusability. This can be especially challenging if an environment is constantly changing, as that would require data engineers to effectively reinvent the pipeline each time.
Yet ETL pipeline issues go beyond just a problematic start. As these pipelines develop and become increasingly complex, their efficiency and accuracy decrease as well, resulting in poor data slipping through the cracks. Inaccurate data sets would then provide false conclusions defeating the entire purpose of building an ETL pipeline in the first place. Thus, it’s essential for data-centered firms to constantly monitor, repair, and update their pipelines, even if that results in significantly increasing load operation management.
Sometimes ETL and Data pipelines are used synonymously; however, that shouldn’t be the case. At the same time, both share some similarities but aren’t the same. “Data pipeline” is a more general umbrella term under which the ETL pipeline falls. To more thoroughly examine their differences, see the features of both below:
Data transformation is an essential process of an ETL pipeline.
The end of the ETL process is always transferring data into a database or warehouse.
ETL pipelines usually move data in batches on a scheduled basis.
Orchestration tools such as Airflow or Dagster are commonplace as they allow for tracking the entire ETL process.
Data could be transformed after the load step or not be transformed at all.
In some Data pipelines, their load step isn’t the end, merely the activation of other processes.
Modern Data pipelines can run real-time processes.
Orchestration tools aren’t necessary.
Three languages are prime choices for building an ETL pipeline: SQL, Python, and Spark. This blog post will focus on Python, which has many crucial benefits, such as the Pandas library (used within the transform stage). The combination of Pandas and Python greatly simplifies ETL operations, especially the extract and transform steps. A more detailed guide as to why this is the case can be found here.
Now it’s time to briefly look at the steps and processes you should follow to build your pipeline with Python.
Create reference data. Technically this step isn’t necessary though its application is a positive practice. Reference data has all the possible values based on static references, which is helpful within the transformation stage.
Connectors and data standardization. Connectors or dedicated tools are essential for data extraction as they are the process through which you conduct data gathering. After the data is collected, it needs to be converted into a format ready for processing. Importantly, Python has a variety of open-source connectors; thus, choosing the language simplifies this step.
Validating data. Ensure extracted data matches your expected range and discards what didn’t.
Transforming. After validation, the data must go through duplicate removals, cleansing, further standardization, integrity checks, and the use of aggregations.
Stage. Before loading, the data gets stored in this layer since the stage step allows for easy rollback if something goes wrong. Furthermore, here is when audit reports generate a diagnosis and analysis.
Loading data into a warehouse. The staging data is moved to a target data warehouse; existing information can be overwritten.
Scheduling. Arguably the most important step. Based on your needs, daily, weekly, monthly, or personal preference ranges can be set.
The entire process of building and maintaining an ETL pipeline can be riddled with complexities and high developer downtime. Yet, ETL or Data pipelines are a necessity for numerous businesses, without whom analytical conclusions cannot be made.
Thus, we hope this article provides useful information about the ETL pipeline, how various issues and challenges arise, its core processes, and what critical benefits it can gather. For more information on data gathering as a whole, check out our blog.
About the author
Danielius Radavičius is a Copywriter at Oxylabs. Having grown up in films, music, and books and having a keen interest in the defense industry, he decided to move his career toward tech-related subjects and quickly became interested in all things technology. In his free time, you'll probably find Danielius watching films, listening to music, and planning world domination.
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