Data Pipeline Architecture: Stages, Components, Best Practices 

Extracting and moving data from a source to a destination becomes more complex and time-consuming as the size of the dataset increases. A data pipeline enables businesses to automate the collection, organization, and movement of data from a source to a destination. The architecture of a data pipeline varies depending on factors such as data type, data size, and data frequency. It is critical to implement the appropriate data pipeline architecture for your specific business needs to achieve desired business outcomes.

In this article, we will cover what data pipeline architecture is, its components, best practices, and how to build a data pipeline architecture step by step. 

What is a data pipeline architecture?

A data pipeline is the process of extracting data from multiple sources and then transferring it to a data repository for use by analytics and business intelligence (BI). A data pipeline architecture is the broader system of pipelines that includes data collection, ingestion, preparation, and storage steps.

What are the types of data pipeline architecture?

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

1. Streaming data pipeline

Streaming data is continuously generated by various data sources such as sensors. In streaming processing, data is analyzed in real-time. It must be cleaned and transformed to extract insights. A good example would be a financial institution that monitors stock market changes in real-time.

A streaming data pipeline continuously gathers data, transforms it, and loads it into a repository where it will be stored. It enables businesses to process and store data in real-time or near real-time. Stream processing is suitable when speed is your main concern, and you need to process data instantly. A streaming data pipeline consists of two layers: a storage layer and a processing layer. 

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

2. Batch processing pipeline

Batch-based processing loads batches of data into a repository such as a data lake or data warehouse at specific intervals. Batch data processing, as opposed to streaming data processing, deals with large amounts of data. If you handle a large volume of data that does not require real-time analysis, batch-based processing is an ideal solution to process the data.

3. Hybrid data pipeline

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

What are the main components of data pipelines?

1. Data collection: Data pipelines gather data from different data sources such as IoT devices, data lakes, databases, and SaaS applications. As data is gathered from various sources, it might be in various formats, such as structured, semi-structured, or unstructured. 

2. Data ingestion: The first step in the data pipeline architecture is data ingestion. Data ingestion is the process of moving data from the source where it is generated to a target system where all users, such as BI analysts, developers, etc., can access it. A data ingestion process converts various types of data into unified data. It allows users to easily read and work with data. Data velocity (how long data is generated and moved), size, frequency (batch, real-time), and format (structured, semi-structured, unstructured) all have an impact on the data ingestion process. There are two methods to ingest data:
   • Real-time data ingestion: Data is collected and processed from different data sources in real-time. Real time data ingestion, also called streaming data ingestion, is ideal for ingesting time-sensitive data.
   • Batching data ingestion: Data is collected at scheduled intervals. Then, collected data is processed and stored in batches. The collected data is then processed and stored in batches. 

3. Data preparation: Once the data is collected, it needs to be cleaned and organized. Data preparation is the process of identifying and fixing problematic data, such as incomplete, duplicate, invalid, irrelevant, etc. Problematic data is filtered, cleaned, and structured to be used for business intelligence and analytics.

4. Data storage: After data is transformed, it is placed in the desired repository, such as a data warehouse or data lake. This allows businesses to have centralized data access.

5. Data governance: Every organization has its own set of data policies and rules to ensure the security and proper use of organizational data. Different types of data, such as product data, consumer data, and financial data, flow across departments within organizations. You must identify and standardize how data must be used by various departments within the organization.

Top 3 best practices for creating a data pipeline architecture

1. Adjust bandwidth capacity in accordance with business network traffic: The maximum capacity of a network to transfer data across a given path is referred to as “bandwidth.” The amount of data that passes through a data pipeline must stay under the bandwidth limit. Otherwise, you will be throttled if a large amount of data passes through the bandwidth. As the bandwidth limit increases, the amount of transferred data also increases over time. To accelerate your data pipeline process, you must understand your business network traffic and measure your bandwidth capacity.

2. Know your data: What is the volume of data? Is the data structured or unstructured? Before ingesting, processing, and storing data, you must estimate the data size to avoid unexpected issues in subsequent steps. It will help you determine the estimated size of your database. Depending on the size of your data, you might prefer a data warehouse, data lake, or data mart.

3. Understanding your data pipeline’s requirements: This makes it easier to select the best tool for your data pipeline. For example, depending on your business requirements, you might need to process data in batch or real-time. Assume your business deals with dynamic data, and that data needs to be processed immediately. In this case, you must take this into account when looking for a data pipeline tool.We have listed a few additional factors that you should consider when selecting a data pipeline tool:
   • If you choose an open-source tool, make sure it is a well-known solution with a large community. 
   • Before searching for a tool, consider your existing infrastructure. If you intend to use a private/closed-source data pipeline tool, ensure that it:
     • Supports your existing technologies.
     • Integrates data from your applications and databases.
     • Supports data quality checks. 
     • Has customer support. 

Further Reading

If you want to learn more about web scraping and how it can benefit your business, feel free to read our articles on the topic:

• Unlock the Value of Business Data with Data Architecture
• Modernize Your Way of Data Management to be Competitive
• Data Parsing to Extract Meaningful Information From Data Sources

For guidance to choose the right tool, check out data-driven list of web scrapers, and reach out to us: