Data Pipeline Architecture: Stages, Components, Best Practices

Extracting and moving data from source to destination becomes increasingly complex and time-consuming as the volume of data grows. A data pipeline automates the collection, organization, and movement of data from source to destination. The architecture of a data pipeline depends on factors such as data type, volume, and frequency. To achieve desired results, it is crucial to implement a data pipeline architecture that meets specific business needs.

In this article, we will explore what data pipeline architecture is, its components, best practices, and a step-by-step build of a data pipeline architecture.

What is Data Pipeline Architecture?

A data pipeline is a process of extracting data from various sources and then transferring it to a data store for use in analytics and business intelligence (BI). Data pipeline architecture is a broader system of pipelines that includes stages of data collection, ingestion, preparation, and storage.

What are the Types of Data Pipeline Architecture?

There are three types of data pipelines: streaming, batch processing, and hybrid data pipeline.

1. Streaming Data Pipeline

Streaming data is constantly generated by various data sources, such as sensors. In streaming processing, data is analyzed in real time. To extract useful insights, it must be cleaned and transformed. A good example is a financial institution tracking stock market changes in real time.

A streaming data pipeline continuously collects data, transforms it, and loads it into storage where it will be kept. This allows businesses to process and store data in real time or near real time. Streaming processing is suitable when speed is the primary concern and instant data processing is required. A streaming data pipeline consists of two layers: a storage layer and a processing layer.

• Storage layer: The collected streaming data is moved to the storage layer.
• Processing layer: The processing layer receives streaming data from storage and transforms it into a data model ready for analysis.

2. Batch Processing Pipeline

In batch processing, data is loaded in batches into a storage system, such as a data lake or data warehouse, at scheduled intervals. Unlike streaming, batch processing handles large volumes of data. If you work with large data volumes that do not require real-time analysis, batch processing is the ideal solution.

3. Hybrid Data Pipeline

A hybrid data pipeline combines two data processing approaches: streaming data pipelines and batch data pipelines. The streaming data platform processes collected data for real-time or near-real-time analysis. Then the data is stored in a storage system for later use in batch processing.

What are the Key Components of Data Processing Pipelines?

1. Data Collection: Data pipelines collect data from various sources, such as IoT devices, data lakes, databases, and SaaS applications. Since data is collected from diverse sources, it can be in various formats, such as structured, semi-structured, or unstructured.
2. Data Ingestion: The first step in data pipeline architecture is data capture. Data ingestion is the process of moving data from the source where it is generated to a target system where it can be accessed by all users, such as BI analysts, developers, etc. The data ingestion process transforms different data types into unified data. This allows users to easily read and work with data. The data ingestion process is affected by factors such as speed (duration of data creation and movement), size, frequency (batch processing, real-time processing), and format (structured, semi-structured, unstructured). There are two methods of data ingestion:
   • Real-time data ingestion: Data is collected and processed from various sources in real time. Real-time data ingestion, also called streaming ingestion, is ideal for time-sensitive data.
   • Batch data ingestion: Data is collected at scheduled intervals. The collected data is then processed and stored in batches.
3. Data Preparation: After data is collected, it must be cleaned and organized. Data preparation is the process of identifying and fixing problem data, such as incomplete, duplicate, invalid, irrelevant, etc. Problem data is filtered, cleaned, and structured for use in business intelligence and analytics.
4. Data Storage: After transformation, data is placed into the appropriate storage, such as a data warehouse or data lake. This ensures centralized access to data.
5. Data Governance: Each organization has its own set of policies and rules for data management, ensuring security and proper use of organizational data. Different types of data, such as product data, customer data, and financial data, flow into different departments of the organization. It is necessary to define and standardize how data is used by different departments.

Three Best Practices for Building Data Pipeline Architecture

1. Adjust bandwidth according to business network traffic: The maximum network capacity for data transfer along a specific route is called "bandwidth." The volume of data passing through a data pipeline should not exceed the bandwidth limit. Otherwise, throttling will occur when large volumes of data pass through the bandwidth. As the bandwidth limit increases over time, the volume of transmitted data also increases. To speed up data transfer, it is necessary to understand business network traffic and measure bandwidth.
2. Know your data: What is the data volume? Is the data structured or unstructured? Before ingesting, processing, and storing data, it is necessary to assess its volume to avoid unexpected issues later. This will help determine the expected database size. Depending on data volume, you can choose a data warehouse, data lake, or data mart.
3. Understand data pipeline requirements: This makes it easier to choose the optimal data pipeline tool. For example, depending on business needs, you may require batch processing or real-time processing. Suppose your business deals with dynamic data that must be processed immediately. In that case, you need to consider this when looking for a data pipeline tool. We have listed a few additional factors to consider when choosing a data pipeline tool:
   • If you choose an open-source tool, ensure it is a well-known solution with a large community.
   • Before looking for a tool, consider the existing infrastructure. If you plan to use a proprietary/closed-source data pipeline tool, ensure it:
     • Supports existing technologies.
     • Data integration from applications and databases.
     • Supports data quality validation.
     • Has customer support.