A data pipeline is a series of processes that move data from one system to another, transforming and organizing it along the way to make it usable. It’s a system that collects raw data from various sources (databases, APIs, or files), then cleans, filters, and formats it before delivering it to a destination (a data warehouse or a cloud platform).
At its core, a data pipeline works in stages. It starts with data collection, then moves through transformation steps, where data is processed and structured, and finally ends with loading, where the prepared data is sent to its final destination for storage or analysis. This automated flow ensures data is always available in a clean, structured format.
In this article, we’ll break down the process and show you how to get the most out of your data.
What is a data pipeline architecture?
A data pipeline moves data through extraction, processing, and transformation, adjusting to the needs of the system it feeds. These steps range from straightforward, single-step processes to multi-layered workflows based on what the final system requires.
Now, let’s break down the key components of a typical data pipeline:
- Data source. This is where the raw data originates. It can come from databases, APIs, logs, or any other data-generating system.
- Ingestion. The first step in the pipeline, where data is collected and brought into the system. Here, Apache Kafka or AWS Kinesis are often used to handle large data volumes.
- Processing. The data undergoes cleaning, filtering, and transformation. This is where raw data is turned into a format ready for analysis or storage.
- Destination. It could be a data warehouse, cloud storage, or a system for analytics. The processed data is stored here, ready to be accessed by teams or applications.
Data pipeline vs ETL: main differences
The terms “data pipeline” and “ETL” are often used interchangeably, but there are key differences between them. While both involve moving data between systems, the scope and processes they cover can vary.
A data processing pipeline is a broad concept that refers to any system that moves data from one location to another. This could involve extracting data, transforming or processing it, and loading it into a destination. However, data pipelines aren’t limited to just these tasks—they may include real-time data streaming, machine learning workflows, and continuous data flows between systems.
ETL, which stands for Extract, Transform, Load, is a more specific type of data pipeline. It focuses on extracting data from a source, transforming it into a structured format, and then loading it into a destination. ETL is typically used in data warehousing for batch processing and organizing data for reporting.
Let’s look at key differences in more detail.
| Aspect | Data pipeline | ETL |
| Scope | More comprehensive, covers any process that moves data. It involves batch processing, real-time data streaming, or a mix of workflows. | Narrower. It’s focused on extracting, transforming, and loading data, usually for batch processing in structured environments. |
| Real-time vs. batch processing | Handles both real-time and batch data processing. It’s highly versatile and can be used to move data to various applications, including machine learning models and real-time dashboards. | Deals with batch processing. Data is moved at scheduled intervals (nightly or hourly rather than in real-time). |
| Transformation | Optional. Data may flow between systems without being transformed, especially in real-time scenarios where it’s simply moved from point A to point B. | A core component. Data is always cleaned, structured, or enriched before being loaded into its destination. |
| Loading | The final destination of data in a pipeline can vary: a database, message queue, machine learning model, or a real-time dashboard. | The main goal is to load the data into a structured environment, typically a data warehouse or database. |
| Use cases | Used in a wide range of scenarios: feeding real-time dashboards, syncing data between systems in real-time, or supporting machine learning models for predictions. | Primarily used for data warehousing, where businesses need to organize data for deep analysis, reporting, and generating insights. |
| Business value | Offers flexibility in managing different types of data workflows. Businesses can process both real-time and batch data, which is valuable for real-time analytics or fraud detection. | Provides structured, reliable data. By organizing data, it helps businesses generate dashboards and insights that drive decision-making. |
Types of data science pipelines
Data science pipelines come in various forms. Depending on functionality, method of processing, deployment environment, and automation level, data pipelines can be classified into several categories. Here’s a quick overview of the different types:
- By functionality: batch, real-time, ETL, ELT
- By processing method: synchronous, asynchronous
- By deployment environment: on-premises, cloud-based, hybrid
- By automation: manual, automated, event-driven
In the following sections, we’ll dive deeper into each type. We’ll explore their characteristics and how they can impact your data operations.
By functionality
Batch data process pipelines
These pipelines process large volumes of data at once, usually at scheduled intervals. This type of pipeline works well for businesses that don’t need immediate insights from their data and can afford to wait for periodic updates.
It’s common in data warehousing and reporting, where it’s more efficient to handle data in bulk rather than processing it as it arrives. For your infrastructure, batch pipelines simplify data handling but require planning for peak loads to ensure timely processing.
Real-time pipelines
Real-time data pipelines process data instantly as it is generated. This is crucial for applications for fraud detection, real-time analytics, or any system that requires up-to-the-minute data insights.
While real-time pipelines offer immediate visibility, they demand a highly responsive infrastructure capable of handling continuous data streams without delays or downtime. For businesses, this setup can provide a competitive edge, but it requires robust infrastructure to handle data as it flows.
ETL data pipeline
In this system, data is first extracted, transformed into a usable format, and then loaded into a destination, typically a data warehouse. ETL pipelines are ideal when you need structured, clean data ready for analysis.
For your infrastructure, ETL pipelines can be resource-intensive, requiring strong processing capabilities to handle complex transformations before loading the data.
ELT pipelines
ELT, or Extract, Load, Transform, is a variation where the data is extracted and immediately loaded into the destination without transformation. The transformation happens later, after the data has been stored.
ELT is more suited for cloud environments, where the transformation can be done as needed, taking advantage of scalable computing resources. This type of pipeline offers flexibility and faster initial data loading, but it may require more powerful computing resources to handle the transformation phase post-loading.
By processing method
Synchronous
Synchronous pipelines process data in a step-by-step manner, meaning each task must complete before the next one begins. This method ensures data is processed in a specific order, making it useful for workflows where data integrity and consistency are critical.
However, because each step waits for the previous one to finish, synchronous pipelines can introduce delays, especially with large datasets. For your infrastructure, this method demands a well-coordinated system, and while it ensures precision, it may lead to longer processing times, especially during high workloads.
Asynchronous
Asynchronous pipelines, on the other hand, allow tasks to be processed independently of one another. This means different parts of the pipeline can run simultaneously.
Asynchronous processing is ideal for real-time applications or large-scale data systems where waiting for each step to finish would cause delays. For your infrastructure, asynchronous pipelines require more robust coordination mechanisms to manage parallel processing, but they provide faster data flow and reduce bottlenecks.
By deployment environment
On-premise
On-premises pipelines are deployed within your organization’s physical servers and data centers. This means you have complete control over your data and infrastructure, including security, configuration, and maintenance. On-premises solutions are often preferred by businesses with strict regulatory requirements or those needing high levels of data privacy.
However, managing on-premises pipelines requires significant investment in hardware, software, and IT personnel. They offer full control but come with higher upfront costs and longer implementation times, along with ongoing maintenance responsibilities.
Cloud-based pipelines
These are hosted on external cloud platforms: AWS, Google Cloud, or Azure. These pipelines leverage the scalability, flexibility, and cost-effectiveness of the cloud. Cloud environments allow businesses to quickly scale their data infrastructure based on demand. So, they are ideal for handling fluctuating workloads or massive data volumes.
Cloud-based pipelines reduce the need for in-house hardware and maintenance while offering the ability to scale on-demand. However, they may come with concerns over data security and compliance, depending on the provider’s policies.
Hybrid pipelines
Hybrid pipelines combine both on-premises and cloud-based infrastructures, allowing businesses to use the strengths of each environment. For example, sensitive data may be processed and stored on-premises for security, while non-sensitive data or processing-heavy tasks can be offloaded to the cloud.
Hybrid solutions are ideal for companies that want flexibility, cost efficiency, and scalability without fully committing to either on-premises or cloud environments. However, managing a hybrid setup can add complexity to operations, requiring seamless integration between the two environments.
By automation
Manual
They rely on human intervention to initiate and manage data processes. Each step, from extraction to loading, must be triggered manually. Thus, this approach is time-consuming and prone to delays or errors. While manual pipelines offer full control over the data process, they are inefficient for large-scale or continuous data operations. They may be suitable for small-scale or ad-hoc processes.
Automated
These pipelines run without human intervention. Once configured, they automatically handle the data extraction, transformation, and loading processes, following a set schedule or predefined rules. Automation reduces manual effort, improves speed, and ensures consistency in data processing. Automated pipelines are ideal for businesses seeking to optimize workflows, minimize errors, and focus on data analysis rather than data management.
Event-driven
Event-driven pipelines are triggered by specific events— a file being uploaded, a change in a database, or a user action. They are highly responsive, allowing data to be processed as soon as relevant events occur.
This approach is ideal for real-time data processing needs, where immediate action is required based on incoming data. They require robust monitoring and event-tracking mechanisms but offer the highest level of responsiveness for dynamic data environments.
Conclusion
Data pipelines play a crucial role in how businesses handle and process information today. Whether you’re dealing with batch processing, real-time data, or more complex workflows, a well-designed pipeline keeps your data flowing and ready for analysis. The great thing is, you can choose the type of pipeline that fits your business—whether that’s based on functionality, processing methods, deployment options, or automation levels.
By setting up the right data pipeline with the assistance of the experienced big data company, you’re not only making your operations more efficient but also setting your business up to scale and gain insights faster.
