What is an ETL pipeline?

An ETL pipeline is a crucial component of the data management process, and it plays a significant role in the preparation and analysis of data. ETL stands for Extract, Transform, and Load, and are the three primary stages involved in the ETL process.

ETL is different from ELT in that the Transformation step is performed before the Loading step. ELT on the other hand, performs the Loading step before the Transformation step. ETL pipeline and ELT pipeline have different benefits and purposes. In this article, we will cover ETL pipelines, how they work, examples, and benefits.

At its core, an ETL pipeline is a set of mechanisms that extract, transform, and load data from one system to another. The primary goal of an ETL pipeline is to prepare the data for use in business intelligence, data analytics, and machine learning.

It is important to note that an ETL pipeline is sometimes referred to as a data pipeline. While there are similarities between the two, there are also significant differences. We will explore these differences in more detail in the ETL Rudderstack Learning Center page ‘ETL pipeline vs data pipeline’. For now, it is important to simply understand that an ETL pipeline is a crucial component in the data management process.

We encourage you to visit the ‘Three stages of the ETL process’ to learn more about the ETL process.

In the next section, we will explore ETL pipeline features.

ETL pipeline features

ETL pipelines offer several features that make them an efficient tool for data processing and management. The primary objective of an ETL pipeline is to simplify data processing and provide a more efficient way of moving data so it's prepared for data analytics and business intelligence.

Continuous data processing: One of the main features of an ETL pipeline is its ability to provide continuous data processing. This means that as new data is created or updated, the pipeline automatically extracts, transforms, and loads it to the target system. This feature ensures that the data is always up-to-date, and insights can be gained in real-time.
Controllable data access: Another key feature of an ETL pipeline is its controllable data access. ETL pipelines allow data engineers to define the source systems from which data is extracted, the target systems where the data is loaded, and the data transformation processes that take place in between. This control over data access ensures that the right data is processed and made available for analysis.
Easy to set up and maintain: ETL pipelines are also easy to set up and maintain. They can be configured quickly, and changes can be made to the pipeline with relative ease. Set up your ETL pipeline with RudderStack for free.
Agile and easy to manipulate: ETL pipelines allow data engineers to transform data quickly and efficiently, giving them the flexibility to adapt to changing data needs.

These features make ETL data pipelines an effective tool for data processing and management. Next, we will discuss how to build an ETL pipeline.

How to build an ETL pipeline

Building an ETL pipeline requires a step-by-step approach to ensure that the pipeline is created efficiently and effectively. The approach you take will depend on the method of ETL you use. Traditional batch processing ETL pipelines and modern stream processing ETL pipelines will require different approaches.

In general, building an ETL pipeline involves following steps:

1. Create reference data

The first step is to create a reference dataset, which involves creating a list of the possible values the data could contain. This is particularly useful for fields with limited values, such as country, gender, and product type. Creating reference data or schema helps ensure data integrity and consistency across the pipeline.

2. Extract and standardize

The second step is to extract data from different sources and convert it into a single format to ensure standardization. This involves identifying data sources and using connectors and APIs to extract the data from these sources. Standardization helps ensure that data from different sources can be combined and processed correctly. Data ingestion and data streaming tools can help with collecting this real-time data from business systems.

3. Validate data

Validating data is the next step in building an ETL pipeline. This step involves ensuring that the data is clean, complete, and accurate. Data validation includes checking for duplicates, missing values, and outliers. Validating data is important because it helps ensure the accuracy and reliability of the data.

4. Transform data

After validating the data, the next step is to transform it. This involves cleaning the data to remove duplicates, checking integrity, applying business rules, data governance, aggregation, and other transformations. Transforming the data helps prepare it for analysis and ensure it's in a format that can be easily understood.

5. Stage data

The transformed data doesn't immediately enter a data warehouse. Instead, it's staged in a staging database, which acts as a buffer between the source data and the target data warehouse. Staging data helps diagnose and repair potential problems before the data is loaded into the data warehouse. It also generates an audit report that can be used for analysis.

6. Load data

The final step in the ETL pipeline is to load the data into the target data warehouse. Each published batch will either overwrite or amend the existing information, depending on the chosen preferences. It's also essential to choose how often to load a new batch of data, picking a daily, weekly, monthly, or custom range with a timestamp to indicate when publishing occurred.

To summarize, building an ETL pipeline involves creating a reference dataset, extracting and standardizing data, validating data, transforming data, staging data, and loading data into a target data warehouse. These steps are critical for ensuring that data is processed accurately, consistently, and efficiently.

While it might seem like a lot of work, there are multiple ETL tools which make it easy to build ETL data pipelines.

Benefits of ETL pipelines

ETL pipelines have numerous benefits for businesses, such as:

Standardizing data: ETL pipelines allow data to be standardized and transformed into a consistent format. This enables businesses to analyze data from multiple sources and gain valuable insights into business operations.
Migrating to a data warehouse: ETL pipelines allow businesses to migrate data from multiple sources to a single data warehouse, making it easier to manage and analyze. This enables businesses to quickly access relevant information and make informed decisions.
Deeper analytics: ETL pipelines enable businesses to access deeper analytics and insights into business operations. This can help companies identify trends, patterns, and opportunities that might otherwise go unnoticed.
Improved efficiency: ETL pipelines automate the process of extracting, transforming, and loading data, making it more efficient and less prone to error. This frees up time and resources that can be used for other tasks.
Better decision-making: By standardizing data and making it easily accessible, ETL pipelines enable businesses to make better decisions based on accurate, up-to-date information.
Cost savings: By automating the process of data extraction, transformation, and loading, ETL pipelines can help businesses save time and money. This is particularly true for businesses that need to process large volumes of data on a regular basis.

Overall, ETL pipelines provide a more efficient and effective way for businesses to manage and analyze data, enabling them to make better decisions and achieve better results.

ETL pipeline example: E-commerce use case

Let’s understand the use case of ETL pipelines for an e-commerce company. An ETL pipeline is a crucial tool for e-commerce companies as it enables them to extract, transform and load their data into a centralized location for analysis. This allows businesses to gain valuable insights into their customer behavior, sales patterns, inventory levels, and more.

In this section, we will explore a real-world example of an e-commerce company that implemented an ETL pipeline to process its data. By the end of this section, you will have a better understanding of how an ETL pipeline can help an e-commerce company make data-driven decisions to improve its business operations.

This e-commerce company had data from various sources, including:

Sales data from an online store’s database, its online marketplace listings (e.g. Amazon), and CRM tool (Customer Relationship Management) - order history, customer information, product information, etc.
Website analytics - traffic, clicks, bounce rates, etc.
Social media analytics - engagement, followers, etc.
Inventory management system data from all storefronts inventory management software and csv files uploaded by managers - stock levels, reorders, etc.
Marketing campaign data - clicks, impressions, conversion rates, etc.

Now, let’s cover how to start building an ETL pipeline for this e-commerce business:

1. Extract:

To extract the raw data from these various sources, we can use a combination of APIs, web scraping, and direct database connections. For example, we can connect to the database or csv file of the inventory management system to extract data on stock levels and reorders, and use the Google Analytics API to extract website analytics data. We can also use web scraping tools to extract social media analytics data from various platforms.

2. Transform:

Once the data is extracted, it needs to be transformed into a format that is standardized and ready for analysis. This involves cleaning the data, removing duplicates, and converting data into a consistent format. For example, customer data from the online store may need to be standardized to ensure consistency across all sources. We can also apply business rules and data governance policies to ensure the data is accurate and reliable. You can transform data in real-time with RudderStack.

3. Load:

After the data is transformed, it needs to be loaded into a data visualization tool for analysis. We can use tools like Tableau, Power BI, Grafana, Plotly, etc. to create visualizations and reports that can help us understand trends and patterns in the data. For example, we can create a dashboard that shows sales performance over time broken down by product category or geographic region. We can gather valuable business insights from those visualizations, like demography of your high-value customers. We can also create reports that show the effectiveness of marketing campaigns, or the impact of changes in inventory levels on sales.

Using an ETL pipeline in this way can help an e-commerce company gain valuable business insights into their business operations, customer behavior, and marketing effectiveness. By standardizing data and bringing it into a centralized location (such as any data lake or data warehouse), we can gain a more comprehensive view of the business and make data-driven decisions. Additionally, by automating the ETL process, we can save time and reduce errors associated with manual data entry and manipulation.

While it might seem overwhelming to do so much work to build an ETL pipeline, there are multiple tools (e.g. warehouse-native Customer Data Platforms) which can make building ETL pipelines less complex. Try RudderStack for free and build ETL pipelines in minutes.

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