Businesses today often have lots of mineable data full of insights. While access to data has many benefits, bringing it all together into a unified view for analysis and reporting can be challenging. As a result, data processing and management techniques have emerged to make uniting this information possible. 

ETL pipelines and data pipelines are two core methods for processing, managing, and transforming this data. Each has its own purposes, strengths, and weaknesses. This comprehensive guide will walk you through the objectives of ETL pipelines and data pipelines, compare their similarities and differences, and help you determine which approach best suits your needs.

What is an ETL pipeline?

ETL is short for Extract, Transform, Load. ETL is a data management and warehousing process with three main steps. 

1. Extract the data from sources such as databases, CRM systems, and spreadsheets.
2. Transform the extracted data to convert the raw data into a format suitable for analysis. This may involve cleansing and normalizing the data.
3. Load the transformed data into the target system like a data warehouse or lake. From there, it can be accessed and analyzed.

ETL pipelines help organizations manage these tasks so the data can be prepared to be analyzed for business intelligence. These pipelines can be automated to run on their own without manual human intervention. As a result, they reduce the manual load on data specialists and leave more time for value-added tasks. 

Key components of an ETL pipeline

The key components of an ETL pipeline are used to perform the three core steps detailed above.

• First, you have data sources. These are where the data is originally stored. They typically include several disparate systems, such as databases, applications, APIs, flat files, data warehouses, etc. 
• The next key component is the ETL engine. This engine automates the process of extracting, transforming, and loading the data. Some examples include Apache NiFi, AWS glue, and Talend.

• The data warehouse or data lake serves as the storage system for the data. This is the final destination for transformed data. 

Examples of ETL Pipelines

ETL pipelines can be used in several scenarios. One of the most common is with customer data. Organizations typically have customer interaction data across multiple data sources, such as email marketing platforms, web analytics programs, and customer management systems (CRMs). This information can help organizations make sense of customer preferences, intent, and related trends. 

However, when customer data is housed in multiple locations and formats, it’s difficult to achieve the unified view needed for analysis. This is where an ETL pipeline can help. By extracting data from disparate sources and transforming it into a consistent format, organizations can load the transformed data into a warehouse for business intelligence reporting and analysis. 

At an industry level, the telecommunications industry is a large use case for ETL pipelines. Telecom data often contains massive data sets about network usage, billing, outages, and customer service interactions. ETL pipelines can help organize and convert this data into a format for analysis. Telecom companies can access this data to better manage the network, customer service department, and billing processes. 

What is a data pipeline?

A data pipeline is a process used to take data from different sources, transform it, and then load it in its final destination such as a database or data lake. ETL pipelines are just one type of data pipeline. Data pipelines can also handle processing data in real-time or near real-time, a distinction from ETL pipelines.

Types of data pipelines

There are four main types of data pipelines:

1. Batch processing: An ELT pipeline is a batch processing data pipeline used to extract, transform, and load large data sets on a regularly scheduled basis. 
2. Real-time: Real-time pipelines extract data in real-time as it is generated from its source. 
3. Open source: Open source pipelines use open source tools, which are an affordable option for managing data sets. 
4. Cloud-native: A cloud-native pipeline is housed in the cloud through a third-party vendor and optimized for cloud-based data sources.

Benefits of using data pipelines

Organizations often use data pipelines to achieve the following benefits:

• Improved data quality: Data pipelines clean, validate, and transform data as it moves throughout the pipelines. This helps reduce inconsistencies, errors, and redundancies. The result is higher quality, more reliable data. 

• Standardization: Likewise, data pipelines ensure consistency across disparate data sources. As data is transformed, uniform data formats and validation rules are applied. Doing so ensures the data sets can integrate effectively for improved analysis. 

• Real-time processing: Process data in real-time or near real-time conditions so you can respond swiftly to changing information and make informed decisions.
• Flexibility: Data pipelines work well with different data formats and sources, so you can integrate all your data into one convenient location. 
• Scalability: Organizations can scale data pipelines to meet varying needs for data volume and loads.  
• Faster integration: Data pipelines make streamlining new data sources into your processing system easier. 

Common challenges in implementing data pipelines

That being said, there are some challenges to be aware of when implementing data pipelines. 

• Complexity: First, the process is not always simple. Designing and maintaining data pipelines can be complex, especially with multiple disparate systems. It requires a great deal of subject matter expertise, which may not be available in all organizations. 
• Data quality: While data quality is one of the key benefits of a well-engineered data pipeline, it isn’t a given output. Ensuring data quality throughout the process can be challenging if you don’t have the right knowledge and tools during the transformation process. Inaccurate or poor-quality data that makes its way through the pipeline can negatively impact analysis, which will affect decisions made on this analysis. 

• Latency: Due to batch processing, ETL pipelines can have more latency issues. Complex transformations and large data sets can also cause latency if the infrastructure is not robust enough to support them.

• Governance: If data logic doesn’t match an organization’s current structure, it can be difficult to monitor and control the data throughout its lifecycle. 

Examples of data pipelines

Organizations can use sentiment analysis to gauge consumers’ feelings about their brand, products, or services. Data pipelines can be used to gather information about the brand and its target keywords, preprocess and classify it, and then aggregate the information for analysis by the social media marketing team. 

E-commerce data pipelines can be used to drive product recommendations to customers by analyzing their buying patterns, preferences, and browsing history. Data pipelines process and integrate this data in real-time so e-commerce businesses can provide immediate recommendations based on personalized insights. 

Key differences between ETL and data pipelines

Trying to decide whether an ETL or data pipeline is the right approach for your data management needs? Check out some of the primary differences between the two:

• Data volume: ETL pipelines can have constraints on batch processing, which may make it challenging to scale with large data volumes. Data pipelines are typically more scalable due to flexible processing options.

• Data transformation: ETL pipelines typically work with batch processing to extract, transform, and load data. Data pipelines, on the other hand, can process in real-time or near real-time to handle continuous streams of data and transform them in an ongoing process. 

• Real-time vs. batch processing: ETL pipelines operate in batch mode to process data at scheduled intervals. Data pipelines allow for immediate data analysis through real-time or near real-time processing. 

• Latency: Due to the nature of processing in batches, ETL pipelines can have more latency issues. Data pipelines can also introduce latency if they have multiple stages but offer more dynamic integration when processed in real time. 

• Storage: Data from ETL pipelines is typically loaded into a data warehouse or lake. Pipelines may also load data into various locations, such as dashboards and analytic platforms. 

• Complexity: ETL pipelines are typically less complex to design for average-scale transformations and integrations. Complexity is introduced as the project scales. With data pipelines, complexity can be introduced with real-time processing. 

• Integration: ETL pipelines integrate well with legacy, traditional systems and warehouses, while data pipelines integrate nicely with modern data infrastructure. 

• Monitoring: For ETL pipelines, you will need to monitor batch processes, while with data pipelines, you will want to monitor data flows continuously. 

ETL vs data pipeline: Which one to choose?

Ultimately, selecting an ETL or data pipeline will depend on your use case, data needs, and overall objectives. As you review these factors, look at the common scenarios where an ETL pipeline, data pipeline, or even both works best. 

When to use an ETL pipeline

1. Batch processing needs: An ETL pipeline is a good choice if you have batch processing requirements and need periodic, scheduled updates. 
2. Centralized data consolidation: ETL pipelines work well for aggregating data from multiple sources into a centralized location for business intelligence purposes.
3. Structured reporting: Choose an ETL pipeline if you have structured reporting needs and/or want to perform complex queries on historical data.

When to use a data pipeline

1. Real-time data requirements: Need real-time or near real-time processing? Go for a data pipeline to generate near-immediate actionable insights. 
2. Continuous streaming data: Data pipelines are an ideal choice for processing and analyzing continuous data streams. 
3. Dynamic data integration: Data pipelines work well for dynamically integrating data from various sources and delivering it to different endpoints.

When to use both

1. Hybrid environments: In some scenarios, your best case may be to combine ETL and data pipelines. An ETL pipeline would work well to periodically batch process data, while a data pipeline could be leveraged for real-time data streaming and integration.
2. Complex data workflows: In this use case, you can leverage ETL to load large data sets into a central repository on a scheduled basis. Data pipelines could be used to flow, integrate, and process data in real-time.
3. Modernization of systems: To modernize data architecture, you could use ETL to maintain existing batch processes and data workflows while leaning on data pipelines to slowly integrate real-time data processing capabilities.

Whether you leverage an ETL pipeline or a data pipeline, having the right tools to support your modern data management processes is key. As you plan to implement one of these data processing pipelines, lean on modern business intelligence (BI) to streamline the steps. BI tools can be used to easily create data pipelines for businesses of all sizes and in all industries.