Data Pipeline: Types & Benefits in 2024

Businesses generate or move huge amounts of data from multiple sources and systems. However, preparing the data for analysis and gaining actionable business insights is not easy as the data needs to be combined in a meaningful way. ETL pipelines and data pipelines have an important role in moving data from different sources into a database and transforming it for later analysis. 

In this research, we will focus on the data pipeline, and explain what it is and why you need it. 

What is a data pipeline?

Data pipeline is the process of moving data from a source to a destination such as data warehouses and data lakes. It includes a series of data processing steps. A data pipeline essentially consists of three steps:

1. A source: where data comes from,
2. Processing steps: data is ingested from data sources, transformed based on business use case, and stored in a data repository, 
3. A destination: targeted places for storing data such as data lakes and data warehouses.

Along the way, data is aggregated, cleansed, and transformed manually or automatically.

Due to the complexity of some data architectures, data pipelines can be time-consuming. Aggregating new data from new sources requires the creation of new data pipelines. However, different data pipelines can cause friction between business units. DataOps aims to prevent these issues by enabling employees to work with data in real-time and collaborate on data management. To allow agility in data analysis and management, each part of the data pipeline is divided into mini-data pipelines.

What are the most common types of data pipelines?

There are two main types of data pipelines:

1. Batch processing: In batch processing, data is collected and transferred periodically and regularly but not in real-time. That’s why it is the ideal solution to move large amounts of data. Organizations do not need to process data in real-time, they can process it after it has been sent to the destination system. 

For example, in order to improve customer experience, some companies collect historical data about their customers and integrate it into a system for later analysis and insights. Data is periodically captured and transformed.

2. Stream processing: Data is processed as soon as it’s created. A real life  example is when someone starts watching something on Netflix, it recommends similar contents based on the user’s tastes almost instantly. 

Data pipeline vs. ETL

Extract (ingesting data from its current location), transform (raw data is converted into a readable format), and load (sending data to a target database such as data lake, data warehouses) is a sub-process of the data pipeline. Both are responsible for moving data from a source to a destination (usually a data warehouse or data lake). One of the main differences between the two is that:

• ETL transforms data before it is loaded into the destination system, but data pipelines may or may not transform data after it is loaded into the system. Sequence is important in ETL pipelines. After data extraction, it must be transformed, then it is ready to be loaded into the target system.

Main benefits of a modern data pipeline

Centralization of data

A centralized database collects and stores data in a unified single location, and provides access to all users from different points. Some key benefits of data centralization for businesses include:

• Cross-functional collaboration: Due to a lack of data centralization, businesses struggle with managing their data. Centralized data management enables businesses to collaborate across functions and supports data transparency across the business. Therefore, users such as BI teams, marketing teams, and data analysts can access the same data through a single management system and have a deep understanding of how and where data has been collected.
• Single source of truth: Data stored in multiple locations leads to data duplication, and over time, siloed data can be generated. Centralized data with a data pipeline prevents siloed databases and enhances data consistency.

Standardization of data

Data standardization is the process of converting raw data into a common and uniform format to enable analysts and other business users to analyze and gain insights from it. It provides a comprehensive catalog of data to have a deep -understanding of how data has been transformed. Understanding data is critical in ensuring that organizational data is consistent, reliable, and secure. Here are the key steps that should be taken to standardize data:

1. Determine the data standards: Determine which data sets should be standardized and how they should be standardized.
2. Understand where the data is coming from: Find out where the incoming data is coming from. Understanding the data sources of data will assist data analysts in determining what data standardization issues they may face.
3. Clean incoming data: Ensure that the data is formatted and verified.

Real-time data analysis

Having separate systems for capturing, transforming, and storing data complicates data management and analysis. A real-time data pipeline provides users with a single unified location. In this way, it enables businesses deeper and real-time data analytics. A real-time data pipeline processes and transforms raw data as it is captured. Thus, it provides current data to decision-makers.

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