Understanding Data Pipeline Architecture

Companies today use data pipeline architectures to manage, report, and transfer data efficiently.

However, there are many aspects you should consider before choosing a particular data pipeline architecture or upgrading the existing one.

So what is data pipeline architecture? In this article, we’ll dive into the various data pipeline architecture, their fundamental parts and processes, and the crucial elements for their effectiveness.

What Is Data Pipeline Architecture?

A data pipeline architecture is a framework that determines how data is ingested, processed, transformed, and stored across the various stages of your data pipeline. It manages the flow of data to its final destination: a data warehouse, data lake, or analytics platform.

In addition, the architecture includes various components—each responsible for specific tasks—ensuring you process data in a scalable and secure manner.

Importance of Data Pipeline Architecture

Data pipeline architecture is the rudimentary system of data pipelines. As such, it’s responsible for taking, transforming, and moving data to a different system. Since most companies today have vast volumes of data flowing through them daily, having a streamlined data pipeline architecture can improve efficiency by allowing real-time access to said data.

Having this in mind, businesses and organizations need data pipeline architecture for more accessible and efficient data management, analysis, reporting, and transferring. Simply put, they allow productive data management.

Data Pipeline Architecture: Basic Parts and Processes

Every data pipeline architecture has several parts, such as a data source, processing steps, destination, standardization, corrections, etc. Below we’ll give you more details on the architecture’s essential parts and processes.

Data Source

The architecture integrates data from multiple sources. A data source is the primary server’s database from which the pipeline extracts, transforms, and transfers data.

Examples of data sources are SaaS applications, APIs, local files, or relational databases. The data ingestion pipeline architecture usually extracts the raw data from these sources through a push mechanism, webhooks, or API calls.

Extraction

The extraction is done through the “ingestion components” that enable reading the data from its data source. An example of an ingestion component is the application programming interfaces (APIs) in the data source. However, you must do data profiling before writing the API code for reading and extracting the data.

Joins

The data joins are also an essential part of the whole process. These are pieces of developers’ code defining the relationship between related tables, columns, or records in a relational data model. Joins are designed for extraction and transformation purposes.

Standardization

Standardization of processes, tools, and requirements is crucial for an effective data pipeline architecture. Each pipeline has its standardization criteria for efficiently extracting, transforming, transferring, monitoring, and analyzing data. Moreover, standardization is crucial for unifying your data in a standard format.

Correction

The data sources sometimes contain data with errors. Such data pipeline architecture examples would be the customer datasets with invalid or removed fields. In these cases, developers design the architecture to correct or remove this data through a standardized process.

Data Loading

After you extract, standardize, correct, and clean the data, it’s time to load it. Typically, we load the data in analytical systems, such as data warehouses, relational databases, or Hadoop big-data frameworks.

Automation

The automation process covers many aspects of the data pipeline architecture, and it’s usually used several times, whether continuous or on iterations. It detects data errors and clean-ups, gives status reports, etc.

Monitoring

Each data pipeline component—the hardware, the software, or the networking elements—can fail to operate. That’s why monitoring is essential; it can help you avoid functional problems at their early stages and assess performance.

Examples of Data Pipeline Architecture

The two data pipeline architecture examples are batch-based and streaming line data pipeline architecture. Below we’ll elaborate on both and show their differences and similarities.

Batch-Based Data Pipeline

Logically, a batch-based data pipeline means the data or records are extracted and managed as a group. It’s important to know that batch transferring isn’t “real-time” transferring because it needs time to read, extract, process, and transfer groups of data according to predetermined criteria by developers.

The batch-based data pipeline architecture works on a schedule or a timeframe that doesn’t recognize the new records, which is why it isn’t real-time. However, the batch-based data ingestion pipeline architecture is more popular than streaming data pipelines among companies with big data.

Streaming Data Pipeline

This one gives real-time analytics! The data often enters the pipeline in small sizes and flows uninterruptedly from multiple data sources to the desired destination. Along the way, the streaming data pipeline handles many pieces of data and their characteristics.

It’s standardized and automated to recognize errors, corruptions, breaches, invalid files, etc. On top of that, the streaming data pipeline usually connects to an analytics engine that enables organizations or users to analyze the data in real time.

6 Components of a Data Pipeline

A well-structured data pipeline consists of several key components, each playing a critical role in ensuring the smooth flow of data.

Here are the six main components to consider:

1. Data Ingestion

Data ingestion is the first step in the pipeline, where raw data is collected from various sources. This component captures data in real-time or in batches by ensuring it’s fed into the pipeline for further processing.

The significance of the data ingestion is critical; it sets the stage for all following operations. Poor data ingestion often causes data loss, delays, or inaccuracies that could compromise the entire data pipeline.

2. Data Transformation

Once ingested, your data undergoes a transformation that contains cleaning, filtering, and reformatting it into a usable format. This step includes enriching the data by adding additional context or deriving new metrics.

Transformation is also the stage where you can enrich data by integrating additional context or deriving new metrics. For example, you might be able to transform raw sales data by calculating the total revenue or adding geographic location data to understand sales patterns more.

3. Data Orchestration

Data orchestration coordinates and manages the movement of data through the pipeline. It also guarantees different processes are executed correctly and the data flows efficiently from one stage to the next.

Orchestration tools automate the scheduling and execution of tasks, allowing data engineers to focus on optimizing the pipeline rather than managing each process manually. Without effective data orchestration—even the best-designed pipeline becomes weak.

4. Data Storage

After processing, the data is stored in a repository such as a data warehouse, data lake, or cloud storage system. This is crucial for ensuring that data is organized and accessible for analysis, reporting, or other downstream applications.

5. Data Observability

Data observability focuses on monitoring the health and performance of the data pipeline. It involves tracking data quality, identifying anomalies, and ensuring that the pipeline is functioning as expected.

Data observability is essential for maintaining the reliability and accuracy of the data. Remember, your data can become relatively worthless without accuracy.

6.  Data Cataloging

Data cataloging builds a comprehensive inventory of the data assets within the pipeline—including metadata that describes the data. Therefore, it makes it easier to discover, understand, and use.

Likewise, a data catalog is a valuable tool for data governance and compliance to ensure that data is used effectively and securely.

7 Data Pipeline Architecture Best Practices

To build a robust and effective data pipeline architecture, there are the 7 best practices to follow:

1. Prioritize Data Quality

High data quality is the basis of a successful data pipeline. As a result, you must implement validation checks to detect errors and inconsistencies early. You should also clean and standardize incoming data to stop duplicates and incorrect entries.

In addition, establish audit mechanisms to continuously monitor data quality and make necessary adjustments if the findings are worrying.

2. Keep Data Pipelines Reliable

Reliability is fundamental to a data pipeline. Therefore, include redundancy at critical points in your data pipeline to prevent single points of failure. Operate automated monitoring systems that alert you to any issues in real time for quick intervention.

You should also execute failover strategies and automated recovery processes to minimize downtime and guarantee that data continues to flow smoothly (even during unexpected disruptions).

3. Maintain Scalability

As data volumes and complexity grow, your pipeline architecture must scale accordingly. Therefore, design your pipelines with scalability in mind from the outset—including leveraging distributed computing frameworks, partitioning data, and using scalable storage solutions.

Likewise, regularly assess your infrastructure to identify bottlenecks and optimize performance. This move will ensure your pipeline can handle increased loads without sacrificing efficiency.

4. Ensure Fault Tolerance and Resilience

Building fault-tolerant systems is fundamental to prevent data loss and ensure continuity. To do this, include mechanisms like data replication, checkpointing, and automated retries to recover from failures.

You must also design your architecture to handle partial failures gracefully. This will ensure processing can resume from the point of interruption without significant data loss or corruption.

5. Implement Security Measures

Data security is non-negotiable when dealing with sensitive or regulated information. You should incorporate robust security measures throughout your pipeline, including encryption for data at rest and in transit, strict access controls, and regular security audits.

Compliance with relevant regulations—such as GDPR or HIPAA—is also essential. Moreover, stay updated on emerging security threats.

6. Focus on Data Lineage and Provenance

Data lineage and provenance are imperative for transparency, accountability, and compliance. You must track the origin, transformations, and movement of data through your pipeline to provide a clear audit trail.

This method supports data governance initiatives and helps in troubleshooting, as you can easily trace errors back to their source.

7. Use a Modular Architecture

A modular architecture enhances the flexibility and maintainability of your data pipeline. This approach allows you to adopt new technologies, improve performance, and address specific needs more efficiently.

Key Elements Influencing the Effectiveness of a Data Pipeline

Data pipeline architecture best practices outline the importance of factors like time, costs, requirements compliance, and scalability on the effectiveness of the pipeline. These elements influence the effectiveness of data pipelines.

Cloud Storage May Not Always be Cost-effective

Even though we regularly praise cloud storage and cloud-based data migration, they can sometimes induce high costs. On paper, cloud storage services sound alluring and affordable.

But, businesses today operate with big data. That’s why, when designing your data pipeline architecture, you must always consider the cost-efficiency and choose the most productive architecture format for the particular platform.

Consider Compliance Requirements From the Beginning

When you build or implement your data pipeline architecture, you must consider the ever-changing business and regulatory requirements. The legislatures vary between states, so you need to make sure you follow the one where you’re based, especially concerning encryption and security.

Compliance with the requirements at the early stages is essential for avoiding legal matters, especially when using the data pipeline architecture to migrate Exchange Journal archives to cloud platforms.

Strategize for Performance and Scalability for Future Needs

Before you build and implement a particular type of data pipeline architecture, you must strategize for future needs and performance. One of the reasons for this is the constant increase and stratification of data.

Moreover, your high-quality data pipeline architecture should uninterruptedly distribute the data across multiple cloud platforms and clusters. That’s why organizations use container applications, multiple serves, and automation tools.

Final Thoughts

Raw data can have elements that may not be important for your company. In these cases, using data pipeline architecture can help you manage critical information and simplify analytics. Data pipeline architecture boosts productivity, make important data readily accessible, and save time and costs for rechecks, clean-ups, and data errors.

If you want to simplify your data management and integration, turn to professionals. Get in touch with our experts at Rivery, and learn how to build your data pipelines!