What is a Data Pipeline? A Comprehensive Guide

What is a data pipeline?

A data pipeline is a set of processes used to ingest data from different sources and move it into a repository, usually a data warehouse or a data lake, for analysis and further use.

It streamlines the flow of data from source systems, transforms data to align it with the schema of the target system, and loads it into a data warehouse. While data undergoes processing before it moves into the destination system, it does not always require transformation, especially if it flows into data lake.

Data scientists and analysts use data pipelines to prepare data for various initiatives, such as feature engineering or feeding it into machine learning models for training and evaluation. Business users leverage a data pipeline builder—a no-code/low-code GUI based tool—to build their own pipelines without relying on IT, making it an important component of the data pipeline architecture.

How have data pipelines evolved?

Data pipelines have come a long way over the past four decades. Initially, data scientists and engineers had to manually extract, transform, and load (ETL) data into databases. These processes typically ran on a scheduled basis, usually once a day, for data ingestion and processing, making them time-consuming and prone to errors.

With the proliferation of internet-connected devices, social media, and online services, the demand for real-time data processing surged. Traditional batch processing pipelines were no longer sufficient to handle the volume and velocity of incoming data. Evolving with time, these pipelines became more flexible, facilitating data movement from cloud sources to cloud destinations, such as AWS and Snowflake.

Today, they focus on ingesting data, particularly real-time data, and making it available for use as quickly as possible, making workflow automation and process orchestration all the more important. As such, modern data pipeline tools now also incorporate robust data governance features, such as:

• Data catalog
• Data lineage tracking
• Metadata management
• Data quality management
• Role-based access control and management

Types of data pipelines

There are multiple types of data pipelines, each catering to different usage scenarios. Depending on the need and infrastructure, businesses can deploy data pipelines both on-premises and in the cloud, with the latter becoming more and more prevalent lately. Here are the different kinds of data pipelines:

Batch processing data pipelines

Batch processing pipelines process data in large volumes at scheduled intervals. They are ideal for handling historical data analysis, offline reporting, and batch-oriented tasks.

Streaming data pipelines

Also called real-time data pipelines as well as event-driven pipelines, these pipelines process data in real-time or near real-time, that is with very low latency. They are designed to ingest and move data from streaming data sources, such as sensors, logs, or social media feeds. streaming data pipelines enable immediate analysis and response to emerging trends, anomalies, or events, making them critical for applications like fraud detection, real-time analytics, and monitoring systems.

ETL Pipelines

ETL pipelines are widely used for data integration and data warehousing. They involve extracting data from various sources, transforming it into a consistent format, and loading it into a target system. ETL pipelines are typically batch-oriented but can be augmented with real-time components for more dynamic data processing.

ELT Pipelines

Extract, load, and transform (ELT) pipelines are similar to ETL pipelines, but with a different sequence of steps. In ELT, data is first loaded into a target system and then transformed using the processing power and capabilities of the target system to transform data.

Data pipeline vs. ETL pipeline

Given the similarities between a data pipeline and ETL, it’s fairly common to come across the question “what is an ETL data pipeline?” Data pipelines and ETL are closely related; in fact, a data pipeline is a broader concept that includes ETL pipeline as a sub-category. However, there are some fundamental differences between the two:

While a data pipeline doesn’t always involve data transformation, it’s a requisite step in an ETL data pipeline. Additionally, ETL pipelines generally move data via batch processing, while data pipelines also support data movement via streaming.

Data pipeline

1. Data Movement and Integration: Data pipelines are primarily focused on moving data from one system to another and integrating data from various sources. They enable the efficient and real-time transfer of data between systems or services.
2. Flexibility: They can be more flexible and versatile compared to ETL processes. They are often used for real-time data streaming, batch processing, or both, depending on the use case.
3. Streaming Data: Data pipelines are well-suited for handling streaming data, such as data generated continuously from IoT devices, social media, or web applications.
4. Use Cases: Common use cases for data pipelines include log and event processing, real-time analytics, data replication, and data synchronization.

ETL pipeline

1. Structured Process: ETL processes follow a structured sequence of tasks: data extraction from source systems, data transformation to meet business requirements, and data loading into a target repository (often a data warehouse).
2. Batch Processing: ETL processes are typically designed for batch processing, where data is collected over a period (e.g., daily or hourly) and transformed before it is loaded into the target system.
3. Complex Transformations: ETL is the right choice in case you need to perform complex data transformations, such as aggregations, data cleansing, and data enrichment.
4. Data Warehousing: You should opt for ETL processes when you need to consolidate data from multiple sources and transform it to support business intelligence and reporting.
5. Historical Analysis: ETL processes are suitable for historical data analysis and reporting, where data is stored in a structured format, optimized for querying and analysis.

Commonalities:

1. Data Transformation: Both data pipelines and ETL processes involve data transformation, but the complexity and timing of these transformations differ.
2. Data Quality: Ensuring data quality is important in both data pipelines and ETL processes.
3. Monitoring and Logging: Both require monitoring and logging capabilities to track data movement, transformation, and errors.

Read more about data pipeline vs. ETL pipeline.

Building a data pipeline

Building an efficient system for consolidating data requires careful planning and setup. There are typically six main stages in the process:

1. Identifying Data Sources: The first step is to identify and understand the data sources. These could be databases, APIs, files, data lakes, external services, or IoT devices. Determine the format, structure, and location of the data.
2. Data Integration: Extract and combine data from the identified sources using data connectors. This may involve querying databases, fetching data from APIs, reading files, or capturing streaming data.
3. Data Transformation: After extracting data, transform and cleanse it to ensure its quality and consistency. Data transformation involves tasks such as data cleaning, filtering, aggregating, merging, and enriching. This stage ensures that the data is in the desired format and structure for analysis and consumption.
4. Data Loading: After transforming, load the data into the target system or repository for storage, analysis, or further processing. During the loading stage, the pipelines transfer the transformed data to data warehouses, data lakes, or other storage solutions. This enables end-users or downstream applications to access and utilize the data effectively.
5. Automation and Scheduling: Set up automation and scheduling mechanisms to execute the data pipeline at regular intervals or in response to specific events. Automation minimizes manual intervention and ensures data is always up-to-date.
6. Monitoring and Evaluating: Implement robust monitoring and metrics to track the health and performance of the data architecture. Set up alerts to notify you of issues or anomalies that require attention. This stage helps optimize your data pipelines to ensure maximum efficiency in moving data.

Read more about building a data pipeline.

Benefits of a data pipeline

Automated data pipelines combine data from different sources. If put in simple words, a data pipeline allows organizations to unlock the full potential of their data assets. Here are some benefits of data pipelines:

1. Increased efficiency

Data pipelines automate data workflows, reducing manual effort and increasing overall efficiency in data processing. By streamlining data operations, organizations can optimize resource utilization and minimize costs associated with manual data handling.

2. Greater scalability

They can handle large volumes of data, allowing organizations to scale their operations as their data needs grow. By adopting a scalable architecture, businesses can accommodate increasing data demands without compromising performance.

3. Data quality improvements

Through data cleansing and transformation processes, they enhance data quality and ensure accuracy for analysis and decision-making. By maintaining high data quality standards, organizations can rely on trustworthy insights to drive their business activities.

4. Real-time insights

Real-time data enables organizations to receive up-to-date information for immediate action. By leveraging timely data insights, businesses can make agile and proactive decisions, gaining a competitive advantage in dynamic market conditions.

5. Cost-effectiveness

They optimize resource utilization, minimizing costs associated with manual data handling and processing. By reducing the time and effort required for data operations, organizations can allocate resources efficiently and achieve cost-effectiveness.

Data pipeline use cases

Data pipelines serve a multitude of purposes across industries, empowering organizations with timely insights and data-driven decision-making. They are utilized in numerous industries to enhance the efficiency of data flow within organizations.

For instance, in the finance sector, they help integrate stock prices and transaction records, enabling financial institutions to enhance risk management, detect fraud, and ensure regulatory compliance.

In the healthcare industry, pipelines integrate electronic health records and lab results, contributing to improved patient monitoring, population health management, and clinical research.

In the retail and e-commerce sector, they integrate customer data from e-commerce platforms and point-of-sale systems, allowing for effective inventory management, customer segmentation, and personalized marketing strategies.

Some more data pipeline use cases:

1. Real-time analytics

Data pipelines enable organizations to collect, process, and analyze data in real time. By harnessing the power of real-time analytics, businesses can make timely decisions, react swiftly to market changes, and gain a competitive edge.

2. Data integration

Data pipelines consolidate data using data connectors from various sources, including databases, APIs, and third-party platforms, into a unified format for analysis and reporting. This integration allows organizations to harness the full potential of their data assets and obtain a holistic view of their operations.

3. Data migration

They facilitate smooth and efficient data migration from legacy systems to modern infrastructure. By ensuring a seamless transition without disruption, organizations can leverage advanced technologies and drive innovation.

4. Machine learning and AI

They provide a seamless flow of data for training machine learning models. This enables organizations to develop predictive analytics, automate processes, and unlock the power of artificial intelligence to drive their business forward.

5. Business intelligence

Data pipelines support the extraction and transformation of data to generate meaningful insights. By harnessing the power of business intelligence, organizations can make data-driven decisions, identify trends, and devise effective strategies.

Working with data pipeline tools

Data pipeline tools make it easier to build data pipelines as mostly they are code-free these days and offer a visual interface. However, choosing the right tool is a critical decision for any organization. The right tool should provide connectivity to a wide range of databases, APIs, cloud destinations, etc. It should also provide support for real-time data integration and both ETL and ELT. An ideal tool should scalable; it should be able to handle growing data volumes and concurrent users. It should allow horizontal scaling to handle more nodes and increased user demand.

Here are some tools that you can consider:

• Astera Centerprise: A no-code data management solution that enables you to build enterprise-grade data pipelines in minutes. It allows you to create and schedule ETL and ELT pipelines within a simple, drag and drop interface. It also supports extensive connectivity to industry-leading databases and warehouses. Plus, you can automate all flows and monitor data in real-time and take advantage of advanced built-in transformations, data quality, version control and security features. It is a powerful tool that has been designed keeping in mind the needs of technical and non-technical users alike.
• Dataddo: Dataddo is a cloud-based code-free tool for building ETL pipelines. It connects with your existing stack seamlessly, along with support for multiple connectors.
• Informatica: Informatica is another enterprise-grade tool for building data pipelines. The tool is equipped with exhaustive features such as variety of connectivity options and different data sources. Informatica is however is a little pricier than other tools in the market.
• Hevodata: Hevodata comes with a user-friendly graphical interface which makes it easier to build data pipelines. The tool supports eady-to-use integrations across Databases, SaaS Applications, Cloud Storage. The unique thing about hevodata is its volume-based pricing, which makes it ideal for small to mid-size businesses.

Emerging trends surrounding data pipelines

Beyond the common use cases, data pipelines have applications in various advanced scenarios and emerging trends:

• Real-time Personalization: Pipelines enable real-time personalization by analyzing user behavior data and delivering personalized content or recommendations in real time.
• Internet of Things (IoT) Data Processing: With the rise of IoT devices, data integration is used to ingest, process, and analyze massive amounts of sensor data generated by IoT devices, enabling real-time insights and automation.
• Data Mesh: The data mesh concept decentralizes them and establishes domain-oriented, self-serve data infrastructure. It promotes data ownership, autonomy, and easy access to data, leading to improved scalability and agility in data processing.
• Federated Learning: They support federated learning approaches, where machine learning models are trained collaboratively on distributed data sources while maintaining data privacy and security.
• Explainable AI: They can incorporate techniques for generating explainable AI models, providing transparency and interpretability in complex machine learning models.

Conclusion

Data pipelines play a vital role in the modern data landscape, facilitating efficient data processing, integration, and analysis. By leveraging the power of an automated data pipeline builder, organizations can enhance decision-making, improve operational efficiency, and gain valuable insights from their data. Data integration tools like Astera simplify the creation of end-to-end data flows. With its user-friendly interface, pre-built connectors, and comprehensive functionality, Astera streamlines the process of building and managing data pipelines, ensuring seamless data movement and delivering actionable insights for organizations.