What is a Data Pipeline? Guide & Examples

The process involves extracting raw data from different sources, transforming and enriching it, and loading it into a or database for analysis and reporting.

In short, it takes data from its original source to actionable insights.

Data pipelines enable organizations to automate the movement and reshaping of data from its initial location to data-driven applications, like (BI) tools or data repositories.

They handle batch or real-time movement and can process large data volumes consistently and reliably.

Pipelines provide a configurable framework for businesses to schedule, monitor, and manage their data integration flows.

Using data pipelines offers many advantages over traditional, manual data integration techniques.

Flexibility

Data pipelines provide the flexibility to add, modify, or remove data sources, transformation logic, and destination systems as needed.

You can onboard new data sources without rebuilding entire workflows. The modular design allows you to make changes without affecting the rest of the pipeline.

This agility is essential for adapting to evolving business needs.

High-quality data

Organizations can implement data quality checks, validation, error handling, and auditing with data pipelines. This leads to higher quality and more trustworthy data for future projects. Early detection and resolution of data issues at the source are possible, improving and compliance.

Improved efficiency

Data pipelines eliminate the need for repetitive manual data manipulation.

The automated workflows reduce the human effort required for data integration tasks. This allows teams to focus on higher-value analysis and data applications, resulting in faster and more consistent data delivery.

Faster and comprehensive data integration

Pipelines enable the integration of data from more sources and the delivery of data to more targets in a scalable way. This creates a single source of truth rather than fragmented data sets. Complete and rapid data integration provides businesses with a competitive edge.

Facilitates improved decision-making

By providing accurate, timely data, data pipelines enable data-driven decision-making. Organizations can make faster and more effective decisions based on comprehensive insights, which is vital for everything from real-time operational choices to long-term strategic planning.

Data pipelines typically follow two main architectures: batch processing and streaming data.

Many modern data stacks blend batch and streaming pipelines to achieve better . Each type has its own strengths and weaknesses, and understanding these can help businesses gain deeper insights.

Here’s a closer look at each type of data pipeline to help you choose the best approach for your project.

Batch processing

Batch processing pipelines run on a schedule to handle large volumes of data. They extract data from source systems, transform it, and load it into a destination data store. Depending on your business needs, this process might occur hourly, daily, weekly, or at another cadence.

For example, a batch pipeline may extract daily sales data from a database at midnight, cleanse the data, aggregate metrics like total revenue, and load it into a data warehouse and BI dashboards.

Batch pipelines are straightforward to scale and manage, making them ideal for tasks that aren’t real-time critical, such as generating daily sales reports or calculating end-of-month KPIs.

However, the tradeoff is latency—batch data is only as recent as the last load cycle. While waiting 24 hours for fresh analytics is acceptable for some cases, others may require more immediate pipeline processing.

Streaming data

As data is generated continuously from source systems, the streaming pipeline consumes, transforms, and loads it instantly to the desired destination. This enables real-time data processing for time-sensitive operations like personalized recommendations, alerting, and operational monitoring.

Streaming is ideal for applications such as real-time analytics, anomaly detection, and personalized recommendations.

However, the complexity of developing and operating these pipelines increases, especially when ingesting data from multiple sources simultaneously. Additionally, streaming pipelines require more resources compared to batch pipelines.

ETL (Extract, Transform, Load) is key to data integration workflows.

However, a data pipeline offers more extensive capabilities beyond ETL. The main differences are:

• Flow: Data pipelines manage end-to-end flows from source to destination, including connectivity, dependencies, schedules, etc. ETL focuses solely on the extract, transform, and load steps.
• Uses: Pipelines handle broader data use cases like streaming and events, while ETL is primarily for batch data integration.
• Features: Pipelines include features such as workload management, scalability, and monitoring. ETL only covers the core integration tasks.
• Flexibility: Pipelines are engineered for flexibility, resilience, and ease of use, whereas ETL can involve more complex custom coding.

In short, ETL is a component within a data pipeline that concentrates on the middle tasks.

A broader data pipeline provides a complete framework to operate ETL and other data integration flows on a larger scale.

A data pipeline follows a standard architectural flow to move data from source to destination.

Here are its main stages:

Data ingestion

This stage involves extracting data from various sources such as databases, APIs, files, social media, IoT devices, and others.

The data is collected in raw format and prepared for further processing. Key steps include connectivity, data validation, and error handling.

Data transformation

Once ingested, the raw data is transformed for usage.

Tasks include cleaning, filtering, aggregating, and joining data, calculating metrics, and enriching the data.

The goal is to convert the original data into meaningful, analysis-ready information.

Data storage

The transformed data is loaded into target databases, data warehouses, or data lakes for storage and access.

Data is stored in a structured format for analysis and reporting, serving as the central source of truth for any subsequent data tasks.

While data pipelines offer considerable benefits, designing, building, and managing them present several challenges:

• Integration complexity: Connecting diverse data sources and seamlessly merging data can be complex, especially with proprietary systems, legacy apps, differing formats, and protocols.

• Maintaining data quality: As data moves through multiple stages, maintaining integrity, accuracy, and consistency is tricky. Data errors can be amplified down the pipeline.

• Dependency management: Pipeline failures can have cascading effects with data flowing across many interconnected systems. Understanding dependencies is critical.

• Monitoring: With so many moving parts, monitoring pipelines in real-time for bottlenecks, errors, and performance can be complex.

• Scalability: Pipelines capable of handling large data volumes, high-velocity data flows, and peaks are essential. Building for scale is imperative.

• Security: With data from many sources and different personnel accessing it, security risks can increase. Balancing open access and security is delicate.

• Lack of skills: As pipelines blend software engineering, data, and infrastructure skills, finding technical talent is challenging. Proper training and expertise are key.

Data pipelines are used across a range of industries. In fact, some of your favorite applications and organizations likely use a data pipeline.

They help businesses manage and rework their masses of data to provide an improved, more efficient experience.

Let’s explore some of the ways data pipelines are applied.

AI and machine learning

Artificial intelligence (AI) systems are fueled by vast volumes of training data. Data pipelines continuously ingest, process, and prepare large datasets to train and maintain AI models.

Tesla is one example. The company ingests driving data from its customer vehicles to train autonomous driving algorithms via data pipelines.

However, it’s important to remember that these AI models are only as functional as the data flowing through the pipelines.

Social media platforms

Social networks need to handle massive amounts of content posted every second. Data pipelines enable them to take in and analyze posts in real-time, helping them understand how to engage their users. This powers recommendations, trending topics, and much more.

Top social media platforms use data pipelines like this. Companies like Facebook, Instagram, and X process posts to make their suggested content more relevant. Personalized timelines result in higher engagement and happier users.

Ecommerce

From inventory and orders to customer data, ecommerce systems generate extensive transactional data flows. Pipelines help collect, transform, and explore this data to provide bespoke recommendations, fraud detection, and supply chain optimization.

Amazon’s recommendation engine relies on data pipelines to analyze customer browsing habits, past purchases, and reviews, offering product suggestions that customers are more likely to buy.

Healthcare

Integrating patient records, treatment plans, IoT sensor data, and other clinical information enhances healthcare operations and patient outcomes.

Data pipelines help securely transport, standardize, and analyze many health data sources, reducing the likelihood of errors and prioritizing data governance.

Epic Systems, a healthcare software suite, takes data from medical devices and electronic health records (EHRs) and puts it into a pipeline to identify health trends and build predictive models.

In the UK, the National Health Service (NHS) consolidates patient data nationwide into a central data lake via pipelines.

provides an end-to-end pipeline framework to efficiently collect, process, and deliver actionable insights from your company data.

With robust data governance, access controls, and security, you can build scalable and compliant data pipelines tailored to your business needs.

The tool allows easy ingestion of data from various sources, including databases, APIs, SDKs, and SaaS tools. It offers pre-built and connectors to simplify setting up data collection workflows.

Once the data is in Amplitude, you'll have access to powerful transformation features to clean, prepare, and shape the data for analysis. This includes joining datasets, filtering, converting data types, aggregating, and more.

Amplitude enables you to blend and transform , product, sales, and marketing data to create a 360-degree customer view.

You can then load the transformed data into visualization dashboards, built-in reporting tools, or business intelligence (BI) platforms like Tableau and Looker to uncover valuable insights.

Let your data be the catalyst to your success. .