Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Data Pipeline vs ETL: Understanding the Key Differences and Use Cases

Data pipelines and ETL (Extract, Transform, Load) are two similar concepts that are related to data movement and processing. However, ETL is a narrower thing and is actually a specific type of data pipeline. Data pipeline, in turn, is a broad concept that includes different types of data movement and processing activities, including ETL, real-time streaming, etc. ETL, as may be seen from the name, focuses solely on extracting, transforming, and loading data to make it usable for efficient data storage and analytics.

In this post, we’ll dig deeper into the difference between data pipelines and ETL, and we will illustrate some of their use cases.

What Is a Data Pipeline?

A data pipeline is a sequence of data processing steps used to safely transfer data from one system to another. Data pipelines facilitate smooth data movement from different sources to destinations like data warehouses, databases, or data lakes.

Simply put, a data pipeline is a roadmap that helps your data safely get from point A to point B in a smooth, uninterrupted way.

Data pipelines automate data handling and transformation, ensuring consistency, reliability, and timeliness. This automation supports real-time analytics, prompt decision-making, and effective data management. Without well-structured data pipelines, businesses may face challenges related to data management and integrity, which can lead to operational bottlenecks and analytical errors.

What Is an ETL Pipeline?

An ETL (Extract, Transform, Load) pipeline is a special type of data pipeline made up of three crucial steps: extracting data from various sources, transforming it into an appropriate format, and loading it into a destination system. ETL is extremely important for effective data analytics, comprehensive reporting, and strategic business intelligence.

In our example, ETL is not a point A to point B highway, but a lifecycle of consumable goods. Raw materials from different sources are taken to a factory where they are transformed into understandable products, which are then moved to a store to be consumed by buyers.

ETL is still a type of data pipeline, but its purpose is to transform the initial raw data. General data pipelines, by contrast, may include simpler processes, such as direct data transfers, without any transformation.

The three stages of an ETL pipeline look like this: The three stages of an ETL pipeline look like this:

• Extract: Raw data is collected from multiple sources, including databases, applications, or flat files. The objective of this stage is to simply collect the data.
• Transform: The extracted data undergoes various operations to ensure data cleanliness, accuracy, and compatibility with the destination system. Typical processes at this stage ensure that data fits the requirements of a target system and include data filtering, enrichment, aggregation, computational logic, and type conversions.
• Load: The transformed data is loaded into the destination location, whether it is a data warehouse, database, or data lake. This phase can be done either incrementally in batches or continuously in real-time, depending on what type suits your business needs and operations.

Key Differences Between Data Pipeline and ETL

Data pipelines and ETL are obviously different, despite sharing some conceptual similarities. Let’s take a closer look at the main differences between these two:

Purpose

ETL pipelines specialize in extracting, transforming, and loading data into target systems like data warehouses or cloud platforms, explicitly preparing data for analytics. In contrast, data pipelines transfer data directly from one system to another, often without significant transformations, facilitating smooth integration across various sources and destinations.

Data transformation

Data transformation is a core part of ETL pipelines, involving extensive data cleaning, enriching, and reformatting to ensure high-quality and meaningful results. Data pipelines may bypass these transformations entirely, simply transferring data in its original form, focusing more on seamless data movement. ETL pipelines integrate data; data pipelines generally deliver it.

Process complexity

ETL pipelines are inherently more intricate, driven by the depth of their transformation processes, which is optimal for data warehousing, business intelligence, and complex analytical tasks. On the other hand, data pipelines are typically less complex, which makes them ideal for simpler, real-time data streaming or straightforward integration scenarios that don’t require heavy data preparation.

Processing methods

ETL pipelines commonly rely on batch processing for scheduled handling of large datasets, though real-time processing is also possible. This structured approach suits periodic, substantial data updates. Data pipelines, however, comfortably accommodate both batch and real-time processing, effectively supporting applications that demand continuous and immediate data flow.

Scalability

Due to their intensive data transformation requirements, ETL pipelines tend to be less flexible and demand more resources, potentially complicating scalability but prioritizing quality. In contrast, data pipelines are more flexible, scaling easily and efficiently to manage dynamic data volumes and diverse data types.

Use cases

ETL pipelines are ideal for integrating, preparing, and centralizing data from various sources, such as disparate locations of legacy enterprise systems, into a consolidated data system (say, SAP Cloud ERP) for analytical purposes. Meanwhile, data pipelines swiftly move data across systems, such as streaming activity logs to real-time analytics platforms for immediate insights.

Data quality

Ensuring data quality and governance is fundamental to ETL pipelines, incorporating thorough data validation, cleansing, and consistency checks during transformation. Data pipelines, in turn, may prioritize speed over rigorous data quality checks, primarily ensuring rapid and efficient data transfers without extensive validation.

ETL vs Data Pipelines: Use Cases

Let’s examine how data pipelines and ETL are applied in companies to streamline processes, increase agility, allow for competitive analytics, and make relevant decisions.

Use cases of data pipelines

Real-time analytics

Data pipelines stream data from sources like websites, applications, or user interactions into analytics platforms. This allows instant updates of analytics dashboards that enable businesses to monitor performance, user behavior, and system status continuously and in real-time.

For example, this may be seen in tracking real-time user interactions on E-commerce websites to adjust recommendations dynamically.

IoT and sensor data processing

IoT devices generate vast amounts of continuous, real-time data. Data pipelines efficiently capture, move, and process this sensor data to facilitate immediate alerts, predictive maintenance, or timely operational insights.

A good example is real-time monitoring of industrial equipment to prevent downtime through proactive maintenance.

Machine learning model training

Machine learning (ML) requires consistent and continuous data streams. Data pipelines automate data ingestion into ML environments, enabling frequent training, re-training, and deployment of accurate predictive models.

Automatic ingesting of transactional and user data into ML platforms to continuously improve recommendation models is a good illustration of this use case.

Multi-cloud or SaaS integration

Data pipelines simplify integration across multiple cloud platforms or SaaS applications, efficiently synchronizing data and ensuring real-time interoperability.

For example, data pipelines ensure seamless real-time data synchronization between ERP systems and CRM platforms (e.g., SAP Cloud ERP and Salesforce integration).

ETL Pipeline Use Cases

Enterprise data warehousing and reporting

ETL pipelines consolidate data from disparate enterprise sources into centralized data warehouses, ensuring comprehensive, high-quality datasets suitable for business intelligence, detailed reporting, and long-term analytical queries.

This use case can be illustrated by combining sales, HR, and inventory data into a central data warehouse for detailed cross-departmental analytics.

Regulatory compliance and auditing

ETL pipelines ensure regulatory compliance by systematically extracting, validating, transforming, and securely storing data necessary for audit trails and regulatory reporting.

Preparing financial transaction data for quarterly audits and regulatory reporting in financial institutions is handled with ETL.

SAP data consolidation

ETL pipelines handle ERP data from various SAP and non-SAP systems, consolidating complex financial, supply chain, and operational datasets for easier, more consistent analysis and reporting.

For example, ETL helps integrate SAP data from regional offices to provide global consolidated financial statements and supply chain analytics.

Combining ETL and data pipelines

In some cases, businesses can use both ETL and data pipelines in collaboration. This approach allows each pipeline to perform its specific tasks, benefitting companies with both ETL pipelines and data pipelines.

Structured ERP/financial data (ETL)

ETL pipelines perform rigorous transformations and quality checks for structured, sensitive, and transactional ERP or financial data when moving from various sources to SAP systems, ensuring accurate, reliable insights.

Use case: Processing and integrating monthly financial data from disparate sources into SAP Cloud ERP systems to support complex reporting and budgeting analyses.

Customer behavior, IoT, and log data (data pipelines)

Real-time data pipelines stream data directly from customer interactions, sensors, or application logs, ensuring timely insights and responsiveness to changing market trends or user demands.

Use case: Capturing real-time customer interactions on mobile apps or website logs, providing instant feedback to marketing teams, and improving user satisfaction dynamically by avoiding stockouts.

How DataLark Streamlines ETL and Data Pipelines

Both ETL and data pipelines need supervision to fix operational issues in a timely manner, as well as automation to streamline data movements and increase data processing speed for timely decision-making and prompt market response.

DataLark, a versatile data pipeline automation platform, will be a good choice when it comes to pipeline automation. The solution offers a robust and unified approach to simplifying ETL and data pipeline management with the help of its no-code, intuitive drag-and-drop interface. This allows users to create, orchestrate, and manage intricate data workflows without extensive technical expertise, decreasing the IT burden. Additionally, the visualized data mapping significantly reduces implementation time, enabling businesses to quickly automate their data flows.

DataLark can be deployed on-premise, in the cloud, or in hybrid environments, which makes the solution suitable for a broad range of businesses.

DataLark’s comprehensive integration capabilities support a vast range of connectors, notably deep SAP integration (SAP ECC, S/4HANA, and others), allowing seamless bidirectional data synchronization across SAP and non-SAP systems. This is especially beneficial in ETL scenarios where structured data from various systems and legacy applications must be consolidated reliably and securely into the ERP system for further analytics and processing.

DataLark supports trigger-based and schedule-based automation, so businesses can choose the option that suits them better and set up automation easily. Additionally, comprehensive data monitoring and automated alerts provide transparency of the data pipeline and ETL processes, allowing for continuous data flow monitoring and timely issue resolution.

DataLark’s Hybrid Approach in Action: Combining ETL and Data Pipelines

Project: SAP S/4HANA Migration with Ongoing Operations

Challenge: A large enterprise migrating to S/4HANA while maintaining business operations requires both batch data migration and real-time operational data flow.

ETL component (historical data migration with complex transformations):

• Extract 10 years of transactional data from SAP ECC
• Transform to S/4HANA data model (Universal Journal, new table structures)
• Load in controlled batches with extensive validation
• Process 500M+ records over 6-month migration period

Data pipeline component (real-time operational data during migration):

• Stream current business transactions to both ECC and S/4HANA systems
• Ensure business continuity during migration phases
• Real-time synchronization of master data changes
• Handle 50,000+ daily transactions with zero business disruption

Business Impact:

• Migration completed 40% faster than with traditional approaches
• Zero business downtime during migration
• 99.8% data accuracy achieved in target S/4HANA system

Conclusion

Data pipelines and ETL are similar yet different. While data pipelines encompass broader and less specific types of data movements, ETL is focused on accumulating data from multiple sources, cleansing and transforming it according to the format of a target system, and successfully loading the data into the destination database.

We hope this guide helps you better understand the difference between ETL and data pipelines and determine when to use each (or both) and how to automate both processes for real-time data analysis, streamlined decision-making, and quick reactions to whatever market or operational changes occur.