Data pipelines are a crucial part of modern data infrastructure. They facilitate the flow of data from its raw state to a form where it can be analyzed and used to make important decisions. By understanding the key components, types, and uses of data pipelines, you can choose the right tools to build them and harness the power of data in your organization.
Data pipelines make sure your organization's data is accessible, reliable, and ready for analysis. Built and maintained by data engineers, and less and non-technical team members in low-code tools, your pipelines are central to your overall data management strategy. By choosing the right data pipeline tools and DataOps approaches, you can uncover valuable insights and improve your customer experience across all touchpoints.
Let’s start with understanding data pipelines.
What is a data pipeline?
A data pipeline is a framework or system that facilitates the flow of data from various sources to a destination where it can be processed, analyzed, and used for business purposes. It involves a series of steps and processes that transform raw data into valuable insights or actionable information.
This valuable component of data infrastructure transforms your large amounts of data from raw, unprocessed information into a valuable business asset. Data pipelines can process data in real time, providing immediate insights. Alternatively, they process data in batches, for routine data updates. They’re crucial components in any modern data-driven enterprise.
The anatomy of a data pipeline
While data pipeline architecture varies from use case to use case, there is a standard anatomy to a pipeline.
Here are the main components you’ll need to understand:
- Data extraction: This is where data is gathered from various source systems, which can range from databases and CRM systems to web analytics tools and social media platforms.
- Data transformation: During this stage, raw data is cleaned, validated, and transformed into a consistent format that can be used for analysis. This might involve removing duplicates, correcting errors, and converting data types.
- Data loading: After transformation, data is loaded into a target system such as a data warehouse or data lake. This process must be carefully managed to ensure data integrity.
- Data analysis: Once loaded, data is queried and analyzed to generate insights. This might involve generating reports, creating dashboards, or applying machine learning algorithms.
- Data visualization: Finally, the results of data analysis are often visualized to make them more understandable and actionable for decision-makers.
Throughout this pipeline, there's also continuous monitoring and error handling to ensure the pipeline runs smoothly and the data is reliable. This involves checking for errors at each step of the pipeline, handling those errors appropriately, and providing alerts when something goes wrong.
Depending on which type of data pipeline you’re using, requirements and data infrastructure needs will change.
What are the main types of data pipelines?
Whether you need to process information from a variety of third-party APIs to feed your customer data platform (CDP) or manage your cloud data warehouse, there’s a type of data pipeline for the job.
Batch processing pipelines are designed to process large volumes of data at regular intervals (e.g. hourly, daily, or weekly). Batch pipelines are often used when the data doesn’t need to be processed in real time and can be analyzed later. These are common in scenarios where the volume of data is extremely large and computations are complex.
Real-time (streaming) data pipelines continuously process and deliver data as it arrives. Data stream processing happens through a series of stages and is immediately sent to the destination (e.g. data lake, cloud data warehouse, etc.). This approach is typically used for real-time analytics, where the most current data is crucial. Examples include monitoring systems, real-time dashboards, or any application that requires instant responses to data changes.
Hybrid pipelines combine elements of both batch and real-time processing. They’re used when some data needs to be processed in real time, but other data can be processed in batches. For example, an e-commerce company might use real-time processing to provide personalized product recommendations while a user is browsing the site but use batch processing for analyzing overall sales trends.
ETL (Extract, Transform, Load) pipelines first extract data from the source systems, then transform this data (clean, enrich, and structure it), and finally load it into a data warehouse or a similar structured data store. ETL tools are used when the transformations are complex, the quality of data is a priority, and the destination system can't handle the transformation workload.
ELT (Extract, Load, Transform) pipelines are a type of modern data process where raw data is first extracted, then loaded directly into the cloud data warehouse. Data transformations are carried out in the warehouse (not in the pipeline). ELT takes advantage of the massive processing power of modern data warehouses. It's used when the volume of data is large, the transformations are simple, and quick loading is a priority.
Event-driven pipelines trigger specific actions in response to certain events or conditions in the data. The data is continuously monitored, and when a specific event occurs, the corresponding action is taken. This can be useful for systems that need to react in real time to changes in data, such as alert systems, real-time recommendation systems, or IoT applications.
Machine learning pipelines are specialized pipelines designed for machine learning workflows. They involve extra steps to prepare data for machine learning, train models, validate their performance, and deploy them for use. This includes feature extraction, model training, model validation, and model deployment. Machine learning pipelines are critical for automating the machine learning workflow and ensuring consistent and reliable results.
API pipelines involve pulling data from APIs (Application Programming Interfaces) and loading it into a data warehouse or other storage for analysis. This is common when working with third-party data such as social media data, weather data, or data from other cloud-based services. These pipelines often need to handle rate limits imposed by the APIs and may need to clean and transform the data to match the structure of the data warehouse.
Common business use cases for data pipelines
You might need real-time analytics from your Amazon AWS cloud platform, or you may have to cleanse data for governance and compliance needs. Whatever business problem you’re trying to solve, a data pipeline will play a crucial role. Here are the most common (not simple) use cases for data pipelines.
Business intelligence and reporting: Data pipelines feed data warehouses and data lakes that support business intelligence tools, dashboards, and reporting systems. These provide insights into business operations, sales performance, customer behavior, and more, informing strategic decision-making.
Real-time analytics: For applications that require immediate insights, such as fraud detection, real-time bidding in advertising, or monitoring user activity on a website, data pipelines process and deliver data in real time or near-real time.
Predictive analytics and machine learning: Data pipelines support the training and deployment of machine learning models for predictive analytics. This might involve forecasting sales, predicting customer churn, or automating customer segmentation.
Data governance and compliance: By cleaning, validating, and structuring data, pipelines ensure data quality and consistency across the organization. This is crucial for regulatory compliance and for maintaining trust in data-driven decisions.
Customer data 360 view: Data pipelines integrate data from various customer touchpoints (sales, customer service, social media, etc.) to provide a comprehensive view of the customer journey. This aids in personalizing customer experiences and optimizing marketing strategies.
Operational efficiency: Pipelines automate the labor-intensive process of manual data consolidation and cleaning, freeing up resources and reducing errors. This leads to more efficient operations and allows personnel to focus on higher-value tasks.
Data monetization: For businesses that sell or share data (such as data brokers or businesses with data-sharing partnerships), pipelines package and deliver the data in a usable format.
What are data pipeline tools?
Data pipeline tools are software solutions that assist in the creation, deployment, and management of data pipelines. These tools help automate the process of data extraction, transformation, and loading (ETL) from various sources to a data warehouse or other destination.
Here are a few types of data pipeline tools:
- Shipyard: A low-code data orchestration and ETL+ solution that allows data engineers to create their own self-serve pipelines (note, Shipyard also allows you to use our no code blueprints or 100 percent of your own code. It's your choice). This platform aims to democratize data access within an organization and improve the user experience of data tools by enabling team members to create reusable tasks that meet specific business needs.
- Apache Kafka: An open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications
- Microsoft SQL Server Integration Services (SSIS): A platform for building enterprise-level data integration and data transformations solutions. It can be used to solve complex business problems by copying or downloading files, loading data warehouses, cleansing and mining data, and managing SQL Server objects and data.
- Talend Data Integration: An enterprise data integration tool that connects, transforms, and manages data from different sources to deliver business value.
- Google Cloud Dataflow: A cloud-based data processing service for both batch and real-time data streaming applications. It enables developers to set up processing pipelines for integrating, preparing, and analyzing large data sets, such as those found in web analytics or big data analytics applications.
- Amazon AWS Glue: A serverless data integration service that makes it easy for analytics users to discover, prepare, move, and integrate data from multiple sources. It can be used for analytics, machine learning, and application development.
- Snowflake: Snowflake is a cloud-native platform. It eliminates the need for separate data warehouses, data lakes, and data marts, allowing secure data sharing across the organization. Because its platform sits on public clouds, organizations may unify and connect to a single copy of all their data.
- Stitch: Designed for critical analytics applications, this tool offers enterprise-grade security and advanced connectivity options to ensure confidence in your data pipeline. Stitch's features include SOC 2 and HIPAA compliance, as well as SSH tunneling to secure the data pipeline.
- Fivetran: A cloud-based data integration platform that simplifies data pipelines by automating the data collection and transfer process from various sources to a data warehouse or other target.
How do I find a data pipeline tool that does everything I need?
We built Shipyard’s data automation tools and integrations to connect and orchestrate your modern data stack with data pipelines. If you want to see a data pipeline tool for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required.
Start building automated data pipelines in 10 minutes or less and quickly learn if Shipyard fits your business needs.
