ETL Pipeline - Definition, Process, Use Cases & Trends
By Laurent Mauer · August 30, 2022 · 9 min read
ETL Pipeline is a set of tools or methods used to extract data from one or more sources, transform it into the desired format and load it into a target database.
This process is known as Extract, Transform and Load (ETL).
ETL Pipeline is important for companies because it helps them to overcome issues related to data quality, security and integration.
These days companies are having multiple databases and ETL pipeline helps them to integrate all their data in one place.
It differs from reverse-ETL since the output or destination is mainly database / data warehouse and not operational tools used by non-technical teams.
Let’s explore the ETL process in-depth and the key benefits and use cases that your business needs when it comes to analyzing vast and disparate sources of data.
Summary
ETL Pipeline - Overview
ETL Pipeline is a method of data analysis that involves taking raw data from disparate sources and converting it into an uniform format.
This allows it to be incorporated into an analytics platform for instance, which can then be used to produce insights about the data.
Let’s dive into 3 key steps of any ETL pipeline.
1. Extract
It is the process of extracting raw unstructured or structured data from any source like business applications, flat files, APIs, IoT, databases, web sources etc.
This step includes parsing, splitting and filtering the data that needs to be extracted from different sources.
The output of this step can be a CSV file containing the data which would be used for further processing in other steps like transformation and loading into the target database.
2. Transform
In this step, we have in our hands a dataset that is not properly fit for any use.
We perform various transformations like converting string values into numeric values, normalizing date values etc., so that we can load them in our target database without any problem later.
Here are some of the methods to perform data transformation:
- Data cleansing
The goal is to have a cleaner and more useful set of data ready-to-use in the target database, so you can use the following types of operations: remove duplicates, replace or get rid of null values, identify and replace errors…
- Formatting
When extracting raw data from their respectives sources, it could be crucial to uniformize the data at this step.
This is where you need to focus on data types like String, Number, Date (Example: ‘Month/day/year’), Double…
- Split
This operation is required when you want to divide a specific data to be more readable afterwards.
For example, you might want to split the address of a customer into 3 pieces: Street, City and State_zip instead of having all that information in one field.
- Extraction
Similar to the split operation, you might want to extract specific information that is encompassed in one data field.
Like the month of subscription of a given customer but the date of subscription is: “Month-day-year”, you need then to extract the “Month” from this field with this function.
- Aggregation
Data aggregation is a step in which relevant dimensions are combined and relevant metrics are generated.
For example, an aggregate technique may total the number of new accounts from different data sources.
Similar columns (group metrics) from several data sources (dimensions) would be found and summed in this case.
- Join
For analytics purposes, join allows relevant data from various data sources to be integrated into a single data source. For example, a company may desire to combine sales data or marketing expenditure across offices, locations, or associate firms.
- Filter
You can omit useless information in your final dataset by using this kind of operation.
3. Load
Once you have extracted and transformed your raw data from heterogeneous sources, it’s time to load it into your database / data lake or data warehouse.
This step can include creating tables or views in your database system, loading tables into those views using SQL queries, and then populating those tables with the transformed data that has been loaded from an ETL tool or manually.
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ETL Pipeline - Top 5 Advantages of Using It
ETL pipelines can be used to connect different databases and tools in order to facilitate data sharing across teams, departments, or organizations.
ETL pipelines are useful because they allow you to manage large amounts of data more easily.
They also help businesses reduce costs by making sure that the right data is being used at the right time.
It’s crucial to have a pipeline in place because it helps you make sure that the data is accurate and consistent throughout the process.
An ETL pipeline helps you:
- Reduce manual error and human error especially when this is repetitive tasks
- Reduce the processing time in your organization
- Free up time to diving and analyzing data and making it relevant, valuable and up-to-date for anyone in the organization
- Track when any issues happen so you can resolve them quickly
- Ensure consistency of semantics, definitions, metrics across different systems and applications in all teams
ETL Pipeline - Top 3 Drawbacks of Using It
Like any processing method, ETL pipeline has its own flaws.
- Heavy transformations, models generated and staging mode are increasing the data processing time and creating frustration for both technical and non-technical teams.
- High-Maintenance associated with this processing type because as business and teams’ needs evolve over time, it’s impossible to generate an ever-evolving ETL pipeline, so by essence they’re rigid and not prone to iteration by teams.
- Investment costs in both tools and teams’ adaptation are heavy since the implementation of ETL pipelines are considered as a company’s project because they affect how an entire business is managing data.
ETL Pipeline - Use Cases
ETL Pipeline is a powerful tool for data integration, and there are many use cases to consider.
Some common use cases include:
- Integrating data from multiple sources into one location, such as a data warehouse or data lake (for data migration purposes for example).
- Performing transformations on that data, making it more consistent and structured, or adding intelligence to it (by cleaning your data from any inconsistencies, duplicates and errors).
- Using machine learning tools in order to extract insights from the data (by transforming your raw data into a format that’s more suitable for analysis).
- Using your clean and transformed data to generate reports and dashboards.
ETL Pipeline vs Data Pipeline - 3 Key Differences
A data pipeline is the full collection of operations used to transport data from one system to another.
ETL pipelines are a sort of data pipeline since the word “ETL pipeline” refers to the operations of extracting, converting, and loading data into a database such as a data warehouse.
However, “data pipeline” is a broader word, and a data pipeline does not always entail data modification or even loading into a target database — the loading procedure in a data pipeline, for example, might trigger another process or workflow.
Let’s dive into the 3 main differences between ETL & Data Pipeline.
1. Purpose
A data pipeline’s goal is to move data from sources such as business processes, event tracking systems, and customer data into a data warehouse to be ready-to-use for business intelligence and analytics.
An ETL pipeline, on the other hand, extracts, transforms, and loads data into a destination system.
The sequence of the process is crucial; after extracting data from the source, you must fit it into a data model required by your business intelligence.
This is accomplished through collecting, cleaning, and converting data.
Finally, the data is put into your ETL data warehouse.
2. Transformation or Not Transformation?
By definition, an ETL Pipeline is always implying a transformation step to be performed properly whereas a data pipeline involves moving data from a point to another one but not necessarily with a transformation process included.
3. Batch vs real time?
ETL pipeline is the go-to processing mode when it comes to periodically transferring bulk data from source to destination.
A streaming data pipeline or reverse-ETL continually fuels data from distinctive sources to any destinations while translating and adapting the data in between in real-time or near it, this process is newly called operational analytics.
ETL, ELT or Reverse-ETL - Key Differences
ETL, ELT and reverse ETL are all data-processing methods to move data from Input (sources) to Output (Destinations).
ETL (Extract Transform Load) is a process of extracting data from one source, transforming it into a desired format and loading it into another system. It involves taking the data from the source and cleaning it up before moving it to its destination.
ELT (Extract Load Transform) is similar to ETL but in reverse order.
This means that the data is extracted from its current location, then transformed into the required format before being loaded back into its original location.
Reverse ETL is also known as “extract-transform-load-reverse”.
This involves taking an existing dataset and updating it with new information extracted from another source without affecting any other existing data in the system.
Reverse ETL is essentially the same as ETL except it moves data in the opposite direction.
Data is extracted from a source database, transformed according to business rules, and loaded into another database or business application.
The idea behind doing it in reverse is that you can consolidate all of your company’s information in one central location, allowing for more efficient analysis and decision-making down the road.
By centralizing your data you are also creating a single source of truth for your entire organization.
Conclusion
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We designed our next-gen data modeling editor to be intuitive and easy to use.
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