Understanding the differences between the Data Pipeline and the ETL Pipeline

Today’s data-driven environment is all about data integration. It means combining data from CRMs, databases, spreadsheets, cloud services, etc., into a unified view. It’s like assembling puzzle pieces to see the whole picture, enabling better decision-making and more efficient operations.

Data pipelines and ETL pipelines are essential parts of effective data integration, helping to collect these puzzles and understand what’s going on. They automate the flow and transformation of data, ensuring that users always have reliable, up-to-date information. However, “Data pipeline” is a broader term that also includes the “ETL pipeline.”

Let’s consider both terms in detail to determine the difference and choose the method that best suits your business processes.

Table of contents

1. What is a Data Pipeline?
2. What is an ETL Pipeline?
3. Key Differences
4. Choosing between Data Pipelines and ETL Pipelines
5. Integrating ETL in Data Pipelines
6. Tools and Technologies
7. Conclusion

What is a Data Pipeline?

Imagine a big kitchen where the chef is looking for ingredients from different storage areas like the fridge, pantry, and spice rack to the prep station to start cooking. But, instead of running around grabbing things one by one, there is a system bringing everything right to the cooking place. That’s how a data pipeline works, but it uses data instead of food.

In other words, data pipeline is a general term meaning the series of processes that move data from one system to another, usually from where it’s created or stored to where it will be used or analyzed. It’s like the “conveyor belt” in the kitchen analogy. Such processes involve data extraction, transformation, loading, cleaning, aggregating, and more. The pipeline ensures data flows smoothly and efficiently through various stages, collecting, processing, and sometimes storing before reaching its final destination, whether in real-time or batches.

Let’s see how a data pipeline works:

• Data Collection. The pipeline starts by gathering data from various sources, such as databases, applications, sensors, or social media feeds.
• Data Movement. Once the data is collected, the pipeline moves it from the source to a destination, such as a database, data warehouse, or big data platform.
• Data Processing (Optional). Sometimes, the data needs a bit of prep before it’s ready to be used, like chopping vegetables before cooking. This step could involve cleaning the data, transforming it into a usable format, or combining it with other data in a data pipeline.
• Data Storage or Output. Finally, the data arrives at its destination, ready to be used. It might be stored in a database, analyzed in a report, or fed into a machine-learning model for predictions.

Let’s say one company has an online store. Its pipeline might start by extracting customer data, order details, and inventory levels from CRM, payment processors, and inventory management software. The next step is to transform this data to ensure consistency (e.g., converting different date formats to a standard one) and finally load it into a data warehouse. From there, they can run reports, analyze sales trends, or personalize marketing campaigns based on this unified data.

What is an ETL Pipeline?

ETL pipeline is a specific type of data pipeline that extracts data from various sources, transforms it into a usable format (cleaning, aggregating, or converting it as needed), and loads it into a target system, like a data warehouse. It automates the heavy lifting of data processing, ensuring that the information users need is accurate, consistent, and ready for analysis.

Imagine a company that gets financial data from different branches worldwide, all in different formats and currencies. The ETL pipeline:

• Extracts this data.
• Transforms it by converting currencies into standard ones.
• Formats it uniformly.
• Loads it into a central database. 

These steps allow the company’s financial team to generate reports accurately reflecting global operations without manually cleaning and merging data from various sources.

Key Differences

While data pipelines and ETL are essential for managing and moving data, they serve different purposes and operate differently. The table below briefly displays the key differences:

Choosing between Data Pipelines and ETL Pipelines

As we have considered before, data and ETL pipelines are powerful tools that cater to different requirements depending on the nature of the data processes. Here’s a quick guide of factors to keep in mind to help users make the best choice.

Data Processing Requirements

• ETL Pipelines are perfect for batch processing, where data needs to be transformed and cleaned before being loaded into a target system, like a data warehouse.
• Data Pipelines are the best for real-time or near-real-time data processing where immediate data flow is crucial.

Complexity and Maintenance

• Data Pipelines are generally simpler and easier to maintain, especially for straightforward data flows with minimal transformation needs.
• ETL Pipelines are more complex due to the transformation processes involved. They require regular maintenance to ensure that transformations are updated with the evolving data requirements.

Cost and Resource Availability

• Data Pipelines typically are cheaper and resource-intensive, especially if users are dealing with straightforward data flows. They can often be managed with minimal infrastructure.
• ETL Pipelines can be more costly due to the need for a robust infrastructure to handle complex transformations and large data volumes. They often require specialized resources to manage and maintain.

End-User Needs

• Data Pipelines are perfect when end-users need real-time or near-real-time data for applications like dashboards, monitoring systems, or customer-facing applications.
• ETL Pipelines may be a good choice when end-users require clean, structured data for reporting, analytics, and decision-making, usually at regular intervals.

Integrating ETL in Data Pipelines

Data zoos of different tools and services usually require a versatile approach, so real businesses most often use a hybrid approach. This approach combines a data pipeline’s flexibility with ETL’s data transformation capabilities to achieve the best of both worlds. It’s like owning a powerful Jeep: not just letting it sit in the garage but taking it out for real adventures.

A hybrid approach uses ETL processes within a data pipeline to handle specific tasks where data needs to be cleaned, transformed, or enriched before it moves to its final destination. This setup is perfect when users need the real-time data flow of a pipeline and ensure that the data is in the correct format or structure before using it.

How It Works

• Extract. Data is pulled from various sources, like databases, APIs, or cloud storage.
• Transform. Instead of performing all transformations at once, users might do them at different pipeline stages. For example, it’s possible to clean and standardize the data early on and then enrich it with additional information later.
• Load. The data is loaded into its final destination, like a data warehouse, a real-time analytics tool, or even a machine learning model.

Real-World Examples

TitanHQ Data Analytics Pipeline Automation to Get a 360-degree Customer View 

• Scenario. TitanHQ uses Skyvia to automate its data analytics pipeline, creating a 360-degree customer view. It means extracting data from multiple sources, transforming it to align with their reporting needs, and loading it into their data warehouse for analysis.
• Benefit. This automation allows TitanHQ to make data-driven decisions faster and more efficiently, with a consistent and up-to-date view of customer data.

Skyvia and Cirrus Insight: Salesforce-QuickBooks Integration

• Scenario. Cirrus Insight was adopted to integrate Salesforce with QuickBooks using Skyvia to enhance productivity and reduce costs.
• Benefit. Improved operational efficiencies, streamlined financial reporting with NetSuite, and significant cost savings.

Tools and Technologies

ETL and data pipeline tools are often called the backbone of modern data processing. They help businesses move and transform their data to make it usable for analytics, reporting, and more.

All these tools have strengths, depending on what users seek, like flexibility, integration with existing platforms, or real-time data processing. When choosing the right tool, don’t be lazy to review a lot to find your pearl.

1. The first step is to look through the tools’ comparison listings in popular blogs; for example, you can check these no-code ETL tools.
2. The next good idea is to compare the tool’s capabilities and consider your specific needs, budget, and the complexity of data workflows.
3. At the same time, check the tools’ ratings on G2 Crowd, Trustradius, and Capterra. Review users’ responses describing the pros and cons of each tool.
4. You’re also free to use freemium pricing plans and demos to select the tool of your dreams, not depending on the company size. Start-ups, small businesses, and even enterprises can use free plans and demo versions to feel how it goes.

5. The next step is to review video materials and white papers to investigate the abilities of such tools. 

Universal data integration tools like Skyvia, Apache Nifii, Talend, etc., provide users with a comprehensive pool of information.

Conclusion

Choosing between a data pipeline and an ETL pipeline depends on companies’ specific data processing needs, the complexity of data transformations, the budget, and the requirements of end users. A data pipeline is the way to go if you need real-time data flow with minimal transformation. However, an ETL pipeline will serve you better if data requires extensive cleaning and structuring before it can be used. Understanding these differences helps you make the right choice for your project.