What Is a Data Pipeline?

A data pipeline is a series of processes that automate the movement and transformation of data from one system to another, enabling organizations to collect, process, and store data efficiently. It connects multiple data sources, applies various transformations, and delivers the refined data to target systems such as databases, data warehouses, or data lakes. 

Simply put, data pipelines streamline the entire flow of data, making it easier to manage, analyze, and visualize information across different platforms.

With today’s growing complexity in systems and data integrations, traditional data processing approaches like ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are no longer sufficient for many businesses. This is where data pipelines come in. 

Data pipelines are dynamic solutions designed to transfer, transform, and analyze data across various stages and environments. Whether you're adding context to raw data, filtering out noise, or applying complex data transformations for better visualization, data pipelines offer greater flexibility than older ETL/ELT processes.

Why data pipelines are better than ETL/ELT

Classic ETL tools move raw data from one or more sources to a staging area, where transformations occur before data is loaded into a target system like a data warehouse. ELT, a more modern approach, reverses the order, loading raw data directly into a target system and transforming it later.

While both approaches have their place, modern data pipelines take this a step further by adding more complex operations and allowing integration with external data sources. The scalability of cloud-based data engines means that organizations can retain raw data for future use without sacrificing computational efficiency, improving the overall flexibility of data processes.

Types of data pipelines

There are two primary types of data pipelines: batch processing and stream processing.

Batch processing pipelines handle large volumes of data at once, allowing for some delay in data delivery. Batch pipelines are great for optimizing costs and processing time-based data aggregations. After each transformation, the data is loaded into a target data warehouse or lake.

Stream processing pipelines deal with continuous data streams that need to be processed and delivered in real-time with minimal delay. Use cases include IoT devices, financial transactions, and other real-time analytics scenarios.

How to implement data pipelines

AWS originally offered a service called AWS Data Pipeline, but this has since been phased out in favor of newer, more specialized tools like AWS Glue, AWS Step Functions, and Amazon MWAA. These modern tools offer scalable, managed services that make it easier to implement data pipelines with greater flexibility. AWS provides a variety of templates and examples, allowing businesses to find the best fit for their specific data pipeline needs.

Best practices for building data pipelines

A well-designed pipeline not only ensures smooth data flow but also enhances data quality, reduces latency, and supports informed decision-making. Let’s discuss the key best practices for constructing robust data pipelines, so that teams are empowered to create systems that can adapt to evolving data needs and drive business success.

1. Set clear goals: Define what you want to achieve with your data pipeline. This helps guide its design and ensures you're building it with a specific purpose in mind.

2. Choose the right data sources: Consider whether you’ll be pulling from one or multiple data sources, and how those sources will be connected to the pipeline. Make sure the data is in a format that’s compatible with your pipeline tools.

3. Set a data ingestion strategy: Whether you choose a full data refresh or an incremental update approach (e.g., Change Data Capture), decide how data will be ingested into the pipeline.

4. Implement data governance: A strong data governance framework will ensure the quality, security, and performance of your data pipeline. Monitoring network congestion, ensuring data integrity, and mitigating risks are essential for maintaining the pipeline’s health.

5. Plan for data workflow and storage: Ensure your data pipeline is optimized for both performance and scalability by planning workflows and choosing the right storage solutions, such as cloud-based data lakes or warehouses.

6. Connect data pipelines with metadata tools: Integrating AWS Glue with a data catalog like Alation ensures that metadata for pipeline objects is easily searchable, reducing operational overhead. Automatic metadata updates can give you real-time visibility into your data environment.

By implementing these best practices, organizations can create a more streamlined and effective data processing framework. Prioritizing goals, selecting the right data sources, and establishing a solid governance framework will not only improve data quality and accessibility but also enhance overall operational efficiency. As data continues to grow in volume and complexity, adopting these strategies will ensure that your data pipelines remain scalable, flexible, and aligned with your business objectives.

​​How a data catalog can help you build and troubleshoot data pipelines

Data pipelines encapsulate complex data movement and transformations. For this reason, pipelines may break and data engineers are called upon to fix them. Utilizing a data catalog can significantly streamline this process. This is because a data catalog serves as a centralized repository that not only organizes metadata but also provides visibility into data lineage, enabling data professionals to track the flow of data throughout the pipeline. 

Here’s how a data catalog can aid in troubleshooting data pipelines:

• Enhanced data lineage tracking: Data catalogs maintain comprehensive records of data lineage, showing where data originates, how it transforms, and where it ultimately resides. This transparency allows users to quickly identify points of failure or bottlenecks in the pipeline.

• Streamlined collaboration: With a data catalog, data engineers, analysts, and stakeholders can collaborate more effectively by accessing shared insights about data quality and pipeline performance. This collective knowledge aids in quickly diagnosing and resolving issues.

• Automated metadata updates: Data catalogs can automatically update metadata related to data assets, ensuring that teams are working with the most current information. This reduces confusion and helps pinpoint discrepancies that may lead to pipeline errors.

• Searchable documentation: A well-maintained data catalog provides searchable documentation, including definitions, data sources, and transformation processes. This resource allows users to quickly find information relevant to troubleshooting specific issues within the pipeline.

• Quality metrics and alerts: Many data catalogs come equipped with monitoring capabilities, allowing organizations to set quality metrics and alerts for their data pipelines. If a specific metric falls outside of acceptable thresholds, stakeholders can be alerted to investigate the issue promptly.

By integrating a data catalog into your data pipeline management strategy, you can enhance your team's ability to troubleshoot effectively, ensuring that your data pipelines remain efficient and reliable. This proactive approach not only saves time but also improves overall data quality, contributing to better decision-making across the organization.

Conclusion

Data pipelines are critical for transforming and delivering data efficiently, whether in batches or real-time streams. With modern tools and best practices, businesses can build robust, scalable pipelines that not only meet current needs but can also grow as data complexity increases. Leveraging AWS services in combination with a strong data catalog like Alation ensures that your data remains organized, accessible, and ready to drive insights across your organization.

Curious to learn how a data catalog can help you deliver more successful data pipelines? Book a demo with us today to learn more.