What is a data pipeline? From foundations to DevOps automation

The role of a data pipeline is to eliminate data silos and instead create a unified, interconnected, automated workflow that connects data collection sources, processing, storage, and analytics seamlessly with consistent, reliable, high-integrity data.

Its goal is to leverage the massive volumes and varieties of raw data an organization collects and creates. The pipeline taps this raw data and then processes it via Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) procedures before storing it in a target database, data lake, or data warehouse. 

These pipelines handle data ETL/ELT to ensure that the data is structured and ready for its intended use, such as reporting, machine learning, or real-time analytics. By efficiently managing these steps, data pipelines enable organizations to maintain data quality, improve processing speed, and ensure that the correct data is available for decision-making.

The purpose of a data pipeline

By connecting and transforming this data, the pipeline fulfills its core purpose: creating a unified, interconnected workflow that ensures high-quality, structured data is readily available for analysis and decision-making. This thereby enables organizations to find meaningful insights, drive innovation, and maintain a competitive edge.

Let’s look at some of the more common data pipeline use cases across categories like business intelligence (BI), data analytics, data science, machine learning (ML), artificial intelligence (AI), and more. The examples below fall into their respective categories, but it’s easy to see how these uses cross between teams and require collaboration and coordination. 

• Business intelligence (BI)
  • Reporting and dashboards to consolidate data from various sources to create comprehensive, interactive visualizations 
  • Historical analysis of past data to identify trends and inform strategic decisions
• Data analytics
  • Trend analysis to identify patterns and drive strategic business insights
  • Predictive analytics leveraging historical and external data to predict future outcomes and support decision-making
• Data science
  • Data preparation, such as cleaning and transforming raw data for complex analysis and model building
  • Exploratory data analysis (EDA), or investigating data sets to uncover underlying patterns and relationships
• Machine learning (ML)
  • Model training, including feeding clean, structured data into machine learning algorithms to train predictive models
  • Feature engineering, or creating and selecting features that enhance model performance
• Artificial intelligence (AI)
  • Natural Language Processing (NLP) for sentiment analysis, translation, and more
  • Computer vision that analyzes image and video data for facial recognition, object detection, and more
• Real-time analytics
  • Streaming data and processing it for immediate insights and actions, such as fraud detection and live user behavior analysis
  • IoT data processing to monitor and react to events as they happen

For these teams and tactics to work, the data and data sources feeding the data pipeline must be consistently reliable, high-quality, and seamlessly integrated. 

Data and sources

A data pipeline deals with various types of data from analytics, transactions, third parties, and more including:

• Structured data that is highly organized data that fits into predefined models or schemas, such as databases (SQL), spreadsheets, and data warehouses
• Semi-structured data that doesn't conform to a rigid structure but contains tags or markers to separate elements, like JSON, XML, and NoSQL databases.
• Unstructured data that is raw and unorganized, without a predefined format, including text files, emails, social media posts, images, videos, and sensor data
• Real-time data that is processed as it is generated, often from IoT devices, logs, or streaming services

These data types are extracted from various sources, such as:

• Databases, both relational (SQL) and non-relational (NoSQL) databases, such as PostgreSQL, MongoDB, and MySQL
• Cloud platforms like AWS, Google Cloud, and Azure that provide data storage and processing
• SaaS applications such as for marketing, sales, customer relationship management, inventory, logistics, etc.
• External data sources such as APIs, web services, and data from external vendors like Nielsen or Qualtrics
• IoT devices like sensors and other devices generating real-time data
• Social media and web data scraped from websites or extracted from social media platforms

This data enters the pipeline via data ingestion. 

Data ingestion

This is the first real step in a data pipeline – collecting raw data from sources and feeding it into the pipeline for what comes next, processing and analytics. 

The basic ingestion process flows as:

1. Identifying data and sources
2. Collecting and extracting data through streaming or batching
3. Validating data accuracy and completeness
4. Initial transformation or pre-processing to meet downstream schema and quality standards

From there, the pipeline has data to begin processing, analyzing, and drawing out insights. 

Data processing

While some small transformations may happen during ingestion, the real data transformation stage goes deeper to bring all the pipeline’s data into a consumable state. Depending on the type of pipeline and data store used, processing might actually happen after data reaches its storage center (covered next), but for the sake of understanding, it’s helpful to think about it as a prior step. 

While every organization has its own unique data needs and processing requirements, the general process includes:

• Cleaning data to remove inaccuracies or inconsistencies
• Converting data into a format more suitable for aggregating, enriching, and filtering
• Enriching data to add context or metadata to enhance its usefulness and depth
• Integrating data from different sources for a cohesive, consistent, unified set

To Extract, Transform, and Load (ETL) – or Extract, Load, and Transform (ELT) – data gets it working together as a consistent unit for broad analytical explorations. 

ETL/ELT is a kind of data pipeline

It can be confusing to think about, but ETL/ELT processes are themselves a data pipeline. In the broader data pipeline conversation, these are one of many parts.  

Specifically, ETL/ELT are types of data pipelines that automate the movement and transformation of data. These processes extract data from various sources, transform it into a suitable format, and load it into a target system like a data warehouse or lake. As a critical part of the broader data pipeline ecosystem, ETL/ELT ensures that data is consistently prepared and made available for analysis, reporting, and other business intelligence activities.

Data storage

Just before or just after data makes its way through transformation, it lands in storage. While traditional relational databases are a common and reliable choice, data pipelines and teams also benefit from alternative, unstructured, and emerging data store technologies. 

Data warehouses are often used for structured data, providing efficient querying and reporting capabilities. Data lakes, on the other hand, can store vast amounts of raw, unstructured, and semi-structured data, making them suitable for big data analytics and machine learning. Plenty of teams weigh the data lake vs data warehouse decision, yet many end up with both solutions serving distinct purposes from similar data pipelines. 

Cloud-based storage solutions offer scalability and flexibility, allowing organizations to manage growing data volumes without significant infrastructure investments. Effective data storage ensures that processed data is readily accessible for analysis, enabling fast and valuable decisions and insights. 

The most important aspect of data storage in a data pipeline is that it meets the needs of the people and technologies that are consuming it. Can the database, warehouse, lake, or other variation provide the discoverability, flexibility, and performance its core users demand?

Data consumption

Who and what will use all this data? Once data is ingested, processed, and stored, the final stage in the data pipeline involves accessing and utilizing it. These teams and tools generate insights, drive decisions, and support various business functions.

Business intelligence and data analytics teams and practices tend to be the primary consumers of these data pipelines, using the information to make business strategy decisions, visualize trends, and draw conclusions about past performance. Going deeper into the data, data scientists leverage the pipeline to train models and perform advanced analytics, turning data into predictive and prescriptive insights. Business intelligence, data analytics, and data science are related practices – unified by the data pipeline – but vary greatly in the information they present and the goals of each team. 

Data pipelines also feed operational systems, such as CRM (customer relationship management) and ERP (enterprise resource planning) platforms, to improve processes and enhance real-time decision-making. They also make connections with APIs, custom applications, and data services, allowing other applications and services to consume and utilize the data programmatically.

Given this broad spread of applications and use cases for data pipelines, they have a lot to live up to – and that poses some challenges to teams that want to continue moving fast and chasing innovation. 

Data pipeline challenges

Orchestrating a data pipeline that is fast and reliable while maintaining data integrity is easier said than done. As data pipelines are spun up from scratch, collaborated on by more teams, or grown from nascent streams to business-critical assets, the most common challenges come as integration, security, scalability, and governance issues. 

Integration

Connecting data sources to a single pipeline means tapping all sorts of platforms and technologies with distinct infrastructure and making them play nicely enough to make sense and have value. Each data source may use different formats and systems, making it difficult to combine and analyze data cohesively. Employing tools that support a wide range of data formats and systems can streamline this integration process, ensuring a seamless flow of information.

Change management

Managing changes in a data pipeline fed by various sources is crucial to maintaining its efficiency and reliability. As data sources, formats, and processing requirements evolve, the pipeline must adapt without causing disruptions, delays, or inaccuracies. With incoming data in a wide mix of formats, the approach to change management must be flexible enough to accommodate varied schema, yet strong enough to enforce standardization and control. 

Implementing a robust database change management process ensures that updates are thoroughly tested and deployed smoothly. This reduces the risk of errors and downtime, maintains data consistency, and allows the pipeline to evolve with the organization's needs. Effective change management also makes it easier to collaborate among teams, ensuring that everyone is aligned and informed about changes to the data structure and pipeline.

Security

Throughout the pipeline and in storage, data security is a critical consideration, especially since data collection and retention strategies might grow to capture more data and store it longer for historical analytics purposes. Protecting data throughout its journey requires robust measures, such as encryption, access controls, and regular audits. These practices help maintain data integrity and ensure database compliance, safeguarding sensitive information from breaches and unauthorized access.

Scalability

To say data pipelines need to scale quickly and efficiently is an understatement – about 90% of the world’s data was created in the past two years. Whatever pace your organization is collecting data at today is likely to seem like a snail’s pace in a year’s time. As organizations collect more data, their pipelines must scale to handle increased loads without compromising performance. 

A lack of scalability can lead to several problems, including slow data processing, which delays insights and decision-making. Bottlenecks in the pipeline, such as manual change reviews prone to human error and inefficiencies, can also cause data loss or corruption, leading to unreliable analytics. 

Plus, inefficiencies in growth can result in higher infrastructure costs as organizations struggle to manage increased data volumes with outdated systems.

Governance

With so many sources, data types, and storage targets in play, the data pipeline needs some rules – and a way to enforce them. Data governance involves establishing clear policies to ensure data quality and compliance with regulatory standards, internal policies, and technological requirements. 

Without an advanced approach to governance, data pipelines can lose most of the momentum they gain through collection, ingestion, and transformation before they reach their final storage spot. Inadequate controls that lead to errors, as well as manual quality reviews that slow velocity and introduce human error, can turn a seemingly valuable data pipeline into an outdated, inconsistent mess. 

Modernizing data pipelines: integrate and automate

Data pipelines represent the future of business: a data-driven economy in which an organization’s ability to collect, transform, and create knowledge from data will be its leading advantage. If adequately addressed, the challenges faced by teams and tools throughout the pipeline don’t need to disrupt the flow, value, or quality of data pipelines. 

DevOps concepts, including Continuous Integration/Continuous Delivery, that bring speed, efficiency, and reliability to application development pipelines can also level-up a company’s approach to data pipeline management. Liquibase, the leading database DevOps platform, brings DevOps to data pipelines through automation, governance, and observability capabilities. 

Teams using Liquibase can seamlessly integrate data sources, automate common tasks, and ensure precise handling of changes, reducing risks and enabling faster rollouts. They can also leverage change monitoring, operations reports, and pipeline analytics that help them find root causes fast, conduct audits easily, and spot trends to support continuous workflow optimization efforts. 

Liquibase’s support for over 60 data stores, including Databricks, AWS Redshift, Google BigQuery, and Snowflake, ensures comprehensive data pipeline management. This automation helps organizations quickly adapt to market changes and make confident, data-driven decisions.

Learn more about data pipeline change management or discover how Liquibase works.