Raw data is often messy, incomplete, and inconsistent. Before it can tell your business something meaningful, you’ll need to clean it up, validate it, and give it structure. 

Data pipelines are responsible for moving data from source to storage, fixing and transforming it along the way. 

In this guide, we explain how data pipelines work, share some use cases, and lay out the benefits of using them.

What is a data pipeline?

A data pipeline is an automated system that moves data from data sources into centralized storage. If the intended destination requires a specific structure, the platform will transform the data accordingly. Most pipelines also include validation capabilities to ensure content entering your system is accurate and high-quality.

Data pipelines can range in complexity. Some will specialize in simple A-to-B data replication, while others include sophisticated transformations. Without pipelines, data must be manually integrated into dashboards, analytics engines, and ML systems. 

Data pipelines vs. ETL

Rather than being different concepts, “data pipeline” is the general term for moving content from source to storage, and “extract, transform, and load (ETL)” describes a specific type of pipeline.

While some data pipeline varieties (like extract, load, and transform (ELT) systems) change up the order of operations, ETL pipelines always extract data from one or more sources, transform it (cleaning, formatting, and processing it), and load it into storage. 

Data pipeline approaches

All data pipelines move data, but they don’t necessarily all act in the same way. Here are the major different approaches and when you might use them:

• Batch pipelines: ‍ Batch processing pipelines collect data over a predefined period and process it all at once. This often happens outside of peak hours to save on costs and reduce latency. Batch pipelines are best for workloads that don’t need live updates.‍
• Real-time/streaming pipelines: Anything that relies on low-latency updates, like fraud detection or event-driven dashboards, will use a streaming pipeline. Companies use these real-time data delivery systems when they need insights continuously.‍
• Event-driven pipelines: These pipelines trigger after a certain event occurs. They’re useful in scenarios where you need to log information after a specific transaction or incident, like operational analytics.

Data pipeline architecture

There are many types of data pipeline technology, but they all have a similar core structure. Here are the four main pipeline architecture building blocks.

1. Data sources

The first step in data pipeline development is identifying the right sources. This could include SaaS apps, event streams, APIs, or any connected database that generates data. Be aware that not every pipeline connects easily with every data source, so be sure to check integrations before choosing a platform. 

Fivetran’s automated ETL pipelines include over 700 pre-built connectors, allowing you to connect to sources without having to turn to complex coding in languages like Python. 

2. Ingestion

After connecting to a source, the next step is ingestion, where you move data from the source into the pipeline. Processes like change data capture (CDC), event consumption, and gathering signals from APIs will all streamline the process. You’ll also need to put a data pipeline framework in place to control how the platform processes information when moving it over from the source.

3. Transformation

Transformation is when the pipeline cleans, formats, and models data before storage. The point is to change raw data into a format that your systems can actually use. You’ll need to know which formats are best for your systems and create transformation logic that delivers the appropriate information.

4. Destination

The final step is moving the data into a storage repository like a data warehouse, data lake, or data lakehouse. Information should now be ready for further analysis. 

‍Note: Remember that not every pipeline will follow these same steps in the same order. For example, an ETL pipeline transforms data before storage, but an ELT pipeline stores it first and processes it afterward.

Data pipeline use cases

Different companies utilize data pipelines in different ways. Here are three of the most common use cases: 

• Financial reporting and compliance: Consolidating data from various financial systems into one storage location helps financial teams gather figures for audits and reports. 
• Customer 360 dashboards: Pipelines can feed data into centralized CRM, product systems, and support channels. This provides a comprehensive view of customer data.
• Machine learning model training: ML models often consume enormous amounts of data and require both real-time and historical information. Data pipelines can supply AI models with data for predictions, continued training, and additional context.

The benefits of using a data pipeline

There are many ways a reliable data pipeline can benefit your business, such as:

• Improved data quality: Better quality data results in more useful insights. Automating ingestion and standardizing transformations reduces the chance of human error when processing data. Pipelines can remove duplicates, spot inconsistencies, and enhance data quality automatically. 
• Enhanced scalability: Manually moving raw data from source to storage quickly becomes infeasible as your company grows. Pipelines provide the structure you need to scale effectively. 
• Faster time to insight: Data pipeline automation gets information from source to storage without you having to lift a finger, meaning data is where you need it, when you need it.
• Reduced engineering overhead: Without a data transfer solution in place, engineers would have to write custom scripts and build and maintain connectors. Automated data pipeline management removes these bottlenecks, letting your team focus on the tasks that matter.

Automatic data pipeline support with Fivetran

Fivetran automates the most complicated aspects of data pipeline management, so instead of working out how to get your information from A to B, you can prioritize analysis, helping you gather crucial insights faster. Fivetran’s ELT pipelines also let you benefit from low-latency, high-performance processing without having to construct anything yourself. And with over 700 pre-built connectors available, you can ingest from any information source in just a few clicks.

With automated schema drift handling and data ingestion scheduling, data arrives reliably without manual intervention. You can even incorporate transformation logic or use dbt integration to get analytics-ready data.

Learn more about how Fivetran’s data pipelines can save you time and improve the health of data in your organization by requesting a free plan or requesting a demo today.

FAQs

What tools are used to build data pipelines?

There’s a range of tools available to help with each component of data pipeline architecture, including sourcing, ingestion, data transformation, and storage. These include source connectors, ingestion tools, transformation frameworks, and storage systems to integrate into your pipelines.

What are the main 3 stages in data pipeline infrastructure?

The three main stages in a data pipeline are extract, transform, and load. Depending on the type of pipeline you choose, the order of these steps may change. For example, ELT pipelines load data into storage before transforming it.

What problems does a data pipeline solve?

By moving information from sources to storage automatically, data pipelines break down silos and help businesses centralize their data. They’re also essential to supporting data-intensive practices, like reporting, predictive analytics, and AI systems. 

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