Data pipeline architecture: What data engineers should know

A well-built data pipeline architecture offers fast-flowing insights, smooth integrations, and dependable information that drive real impact. But when your architecture breaks, the ripple effects will hit every team in your organization.

To build reliable, scalable pipelines, you’ll need to understand the architecture that shapes how your data flows and changes across your company. 

What is data pipeline architecture?

Data pipeline architecture is a blueprint detailing how data flows from source to destination. This framework maps out how a business captures, processes, transforms, stores, and delivers data. As the information moves through your system, the architecture defines the expected behavior of the data, turning it from raw, unstructured information into a valuable, structured resource. 

With automated data pipeline platforms like Fivetran, engineers can map out and run their data delivery systems without the heavy maintenance normally involved. 

Types of data pipelines and data pipeline design approaches 

Different types of data have specific requirements, with some services needing special processing or unique data pipeline diagrams to improve delivery.

Here are the main types of data pipeline designs, split into their two separate analytic dimensions.

Timing dimension

Batch data pipeline: Batch data pipelines are time-based systems that move data at fixed intervals or on demand. Instead of a constant flow of information, batch data works best for systems where the final outcome doesn’t depend on real-time information. 

Since these systems can process large volumes of data in a single run, they’re cost-effective and fairly simple to implement.

‍Streaming data pipeline: When you have a continuous data stream, such as a live sales dashboard, the underlying architecture uses a streaming data pipeline. In contexts where a few seconds of data latency makes a big difference, streamlining data ensures events are captured and delivered with low latency. While this enables real-time insights, it’s more expensive. 

Hosting dimension

Cloud-native data pipeline: A cloud-native data pipeline runs fully in the cloud, giving businesses access to all the cloud benefits without managing physical infrastructure. Moving data pipelines to the cloud reduces infrastructure maintenance and is well-suited for integrating with cloud-native data warehouses.

‍On-premises data pipeline: On-premises data pipelines refer to any internal company data architecture. Some heavily regulated industries require pipelines to be on-site due to security and confidentiality needs. Because this type of pipeline is kept in a physical location, there’s a large increase in overhead costs.

Importance of management for data pipeline architecture

Effective data pipeline management creates long-lasting, strong systems that don’t fail after just a few months. Keep your system healthy and running smoothly by tracking performance, identifying bottlenecks as they form, and implementing data validation checks. Prioritize pipeline management to ensure your system runs well today and far into the future — even as demand and scale increase.

Components of data pipeline architecture

While pipeline specifics may vary, the core components of data architecture remain the same. The underlying infrastructure helps collect, transform, and enrich data, then draw useful business insights from it. 

Here are the main components of data pipelines:

• Source ingestion: Pulling raw data requires sources, which can include SaaS applications, databases, IoT devices like sensors, or even automated data connectors. These sources supply the raw information (often via code like Python) that eventually impact data-driven decision-making.
• Processing and transformation: After ingestion, the raw data is wrangled into an enriched format. From there, it’s cleaned, standardized, and aggregated to improve its quality. This stage may be second or third in the process, depending on whether it’s an ELT or ETL pipeline, respectively.
• Storage: The storage stage involves saving the data within a cloud data warehouse or other large storage facility. Businesses often turn to cloud storage to overcome any potential for data siloing by creating a single source of truth for future analytics.

Once you’re finished, you’ll be able to enable these activities with your data pipeline: 

• Analysis: Once data is enriched and safely stored within company databases, you’re ready to feed it into your analysis tools. At this stage, you’ll be able to apply statistical analysis and machine learning (ML) techniques to garner insights like trends, patterns, and subtle data relationships.
• Visualization: The final stage of a data pipeline is turning insights into a visual format, usually in a graph, charts or dashboard. Data visualization lowers the barrier to deciphering complex patterns, making the data as digestible as possible.

How to build a data pipeline

Before you begin, create a data architecture diagram to map out which tools and systems support each phase of the process.

Here are the main steps to building a data pipeline:

1. Identify your sources: Make a list of all the data sources you’d like your data pipeline to ingest. Knowing if your data is structured, unstructured, or semi-structured will help distinguish which data pipeline technologies are the right choice later on.
2. Determine the structure and format: After identifying core sources, flag the specific data formats. Sources might use JSON blobs, SQL tables, CSV files, or something else entirely. 
3. Select an integration tool and storage solution: Opt for a data integration tool that best covers the data you want to use. Leveraging automation ELT solutions like Fivetran can simplify this process significantly by handling data ingestion and schema changes for you.
4. Outline processing logic: How your data is transformed in this stage will depend on your end goals. Decide how you want to clear, join, and standardize any data you work with.
5. Connect data analysis and visualization: At this phase, you’re ready to integrate BI dashboards, data analytics engines, and other systems that convert your data into actionable insights.
6. Continuously monitor and maintain: From here, focus on maintaining your data pipeline, monitoring its performance, and iterating where possible to keep improving. 

Challenges of data pipeline architecture

Data pipelines are extraordinarily useful tools, but that doesn’t mean they’re flawless. Watch out for these challenges: 

• Managing schema changes: Source systems evolve, and pipelines need to be able to keep up without breaking. Schema changes are complex to manage, making tools that automate the process a time-saver.
• Maintaining data quality: Incomplete inputs or low-quality data can pollute downstream outputs. Strong validation policies will help catch issues early on.
• Meeting compliance standards: Data pipelines may process sensitive data where security is a priority. Make sure to prioritize access controls, industry-standard encryption, and regulatory requirements at all times.

Best practices for data pipeline architecture

As your business scales, you need architecture that’s predictable, resilient, and manageable.

Follow these best practices to keep your pipelines reliable: 

• Build for scalability: Even if your company doesn’t currently have scaling demand, build your architecture as if you do. Look for autoscaling tools, flexible cloud-native storage, and a parallel processing structure so your architecture can grow with demand. It’ll save you the headache of rebuilding large infrastructural components later.
• Focus on observability: After data begins flowing, it can feel like the job’s done. In reality, monitoring throughout, identifying bottlenecks, and tracking latency are equally important. Use tools that boost visibility to learn more about how data flows through your system and how to improve that movement. 
• Use a modular design: Although each component works together, managing them independently allows you to upgrade tools or adjust logic easily. Flexible architecture lets you iterate changes in each component, reducing downtime while enhancing performance. 

How Fivetran supports your data pipeline architecture

Fivetran simplifies modern data pipeline architecture by automating the most complex and time-consuming parts of moving data. Instead of manually mapping out every stage, rely on automated ELT pipelines to ingest data from hundreds of sources, keep schemas in sync, and deliver data directly to your storage facilities without fuss.

By handling the messy parts of ingestion and processing, Fivetran lets you focus on high-value data engineering that pushes your business forward — delivering insights, building ML models, and transforming your data into actionable information. 

Get started for free by requesting a demo, or learn more about Fivetran’s reverse ETL to transport high-quality data to your business applications with ease. 

FAQs

What’s the difference between a data pipeline and ETL?

A data pipeline is the entire architecture that moves data from source to destination. An ETL pipeline is a specific configuration that extracts data, transforms it, and then loads it into storage. 

What are examples of data pipeline architecture?

Common data pipeline architecture examples include batch pipelines, cloud-native pipelines, hybrid designs, and real-time streamlining pipelines.

What are the 4 phases of the data pipeline?

The four main phases of a data pipeline are ingestion, processing, storage, and delivery. The order of processing and storage are interchangeable and depend on the specific pipeline’s configuration.

What are some of the most effective data pipeline tools?

Fivetran’s automated ELT pipelines reduce manual efforts while enhancing data architecture performance. Other tools, like cloud data warehouses for storage or transformation frameworks, can also help enhance segments of the pipeline, improving the entire structure. 

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