Your guide to building data pipelines

Your business thrives or fails based on its ability to turn raw data into useful insights. Building data pipelines is key for transforming endless piles of data into strategic assets. You might need a streaming data pipeline for real-time personalized content or a classic batch data pipeline to keep track of inventory every night. A well-designed data pipeline enables efficient data collection, transformation, and analysis, leading to actionable insights and informed decision-making. And a good design starts with understanding what you’re building and why.

This guide covers the key steps and best practices for building robust data pipelines. Let’s start with a simple definition.

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.

In simple terms, a data pipeline can be seen as a series of interconnected components or stages that work together to move data from its source to its destination. These stages typically include data extraction, data transformation, data storage, and data processing.

The purpose of a data pipeline is to automate and streamline the process of collecting, transforming, and analyzing data. This empowers your business to make informed decisions, gain insights, and drive data-driven initiatives. A well-designed data pipeline ensures data integrity, reliability, and timeliness, and supports ongoing improvement in data quality.

Which type of data pipeline should I be building?

From real-time data pipelines to overnight batch pipelines, there are many types of pipelines that can work for your unique use cases. If you’re building personalized content experiences, you’ll need real-time data from your website, social media, and other customer touchpoints streaming to your customer data platform (CDP) and customer experience platform (CXP).

You’ll need ETL pipelines to gather data from all sources and move it into a central cloud data warehouse and machine learning pipelines for your advanced ML models. Ultimately, you’ll be building many kinds of pipelines to run your business—including a data science pipeline for your DataOps team.

Batch data pipeline: A batch data pipeline processes data in large volumes at scheduled intervals. It collects data over a specific time period, such as daily or hourly, and processes it as a batch. This type of pipeline is suitable for scenarios where near real-time processing isn’t necessary, and it can handle large datasets efficiently.

Real-time data pipeline: Real-time data pipelines process data as it arrives, enabling immediate analysis and decision-making. They ingest, transform, and deliver data often within seconds or milliseconds. Real-time pipelines are commonly used in applications requiring real-time monitoring, fraud detection, stock market analysis, or IoT data processing.

ETL (Extract, Transform, Load) pipeline: ETL pipelines focus on extracting data from various sources, transforming it to meet specific requirements, and loading it into a destination system for storage or analysis. ETL pipelines typically involve data extraction, cleansing, filtering, aggregation, transformation, and loading steps. They’re commonly used in data warehousing and business intelligence scenarios.

ELT (Extract, Load, Transform) pipeline: ELT pipelines follow a similar process as ETL pipelines but with a different order of operations. In ELT pipelines, data is extracted from source systems and then loaded into a destination system without immediate transformation. Transformation is performed within the destination system using tools like SQL queries, data lakes, or cloud-based processing capabilities. ELT pipelines are often associated with modern data architectures and big data processing.

Streaming data pipeline: Streaming data pipelines handle continuous and high-velocity data streams in real time. They ingest, process, and analyze data as it flows, enabling immediate insights and actions. Streaming pipelines are commonly used for applications like real-time analytics, social media sentiment analysis, log monitoring, or sensor data processing.

Data integration pipeline: Data integration pipelines focus on consolidating data from multiple sources into a unified view. They extract data from various systems, transform it to ensure consistency, and integrate it into a central repository. Data integration pipelines enable data harmonization, which then enables comprehensive analysis and reporting.

Machine learning pipeline: Machine learning pipelines are specifically designed for training and deploying machine learning models. They involve data ingestion, preprocessing, feature engineering, model training, model evaluation, and model deployment stages. Machine learning pipelines enable the automation of machine learning workflows and the continuous improvement of models over time.

Event-driven pipeline: Event-driven pipelines are triggered by specific events or conditions. They react to events like data arrival, system alerts, user interactions, or business triggers. Event-driven pipelines enable responsive and automated data processing based on predefined rules or actions.

These types of data pipelines aren’t mutually exclusive. Many pipelines may combine elements from multiple types to suit specific business needs. Choosing which pipeline to build depends on factors like data characteristics, processing requirements, latency constraints, and desired outcomes.

Key components you need when building a data pipeline

There are similar components included in each type of data pipeline. For example, a streaming data pipeline has extraction, transformation, and storage steps just like an overnight batch data pipeline.

Here are the main things to account for when building your data pipelines:

Data extraction: This stage involves gathering data from different sources such as databases, APIs, log files, or external services. Data extraction methods can vary based on the source and may include techniques like querying databases, making API calls, parsing log files, or scraping web pages.

Data transformation: Once the data is extracted, it often needs to be cleaned, validated, and transformed to ensure its quality and compatibility. Data transformation involves tasks like data cleansing, filtering, aggregation, normalization, or applying business rules to standardize the data and make it suitable for analysis.

Data storage: After transformation, the data is stored in a suitable storage solution such as a relational database, a data warehouse, a data lake, or a cloud data warehouse. The choice of storage depends on factors like scalability, performance, security, and the specific requirements of the data pipeline.

Data processing: Once the data is stored, it can be further processed and analyzed to extract insights, generate reports, or perform specific operations. This stage may involve running queries, applying statistical models, running machine learning algorithms, or generating visualizations to derive meaningful information from the data.

Data delivery and consumption: The processed data is delivered to end-users or downstream applications, enabling them to access and use the insights or information generated by the data pipeline. This can be done through APIs, dashboards, reports, or data exports that provide easy access to the analyzed data.

Your business use cases will inform the type of data pipeline you use and its components. A data pipeline for social media sentiment analysis is a lot different than a data pipeline responsible for managing incoming merchandise.

Main use cases for building data pipelines

You might need an executive dashboard that shows weekly, monthly, and yearly sales numbers broken into an easily digestible view. Your sales and marketing teams need a clear view of all the data stored in their customer relationship management (CRM) platform. Wherever you look across your business, you’ll find a list of use cases that require building and orchestrating new data pipelines.

Here are some of the most common ones.

Business intelligence and reporting: Data pipelines are commonly used to collect, transform, and aggregate data from multiple sources into a centralized cloud data warehouse or data lake. This enables organizations to generate accurate and timely reports, dashboards, and visualizations for business intelligence purposes, facilitating data-driven decision-making.

Customer analytics: Data pipelines can be employed to gather and analyze customer data from diverse sources, such as CRM systems, web analytics, social media, and transactional databases. By integrating and processing this data, businesses can gain insights into customer behavior, preferences, and sentiment, allowing them to personalize marketing campaigns, improve customer experiences, and enhance customer retention strategies.

Fraud detection and security monitoring: Data pipelines play a vital role in fraud detection and security monitoring by processing real-time or near-real-time data streams. By continuously ingesting and analyzing data from various sources like transaction logs, network activity, or user behavior, organizations can identify patterns, anomalies, or suspicious activities, enabling them to proactively detect and prevent fraudulent activities or security breaches.

Operational analytics: Data pipelines can be used to collect and process operational data from systems such as manufacturing equipment, IoT devices, or supply chain management platforms. Analyzing this data in real time or at regular intervals helps businesses monitor operational efficiency, identify bottlenecks, optimize processes, and make data-driven decisions to improve overall performance.

Predictive analytics and machine learning: Data pipelines are often used to feed data into predictive analytics models and machine learning algorithms. Businesses can train models to make predictions, recommendations, and forecasts by collecting and preprocessing data from various sources. This can be applied to use cases like demand forecasting, predictive maintenance, recommendation systems, or risk assessment.

Log analysis and monitoring: Data pipelines are employed to ingest and process log data from various systems, such as server logs, application logs, or network logs. By analyzing these logs, businesses can gain insights into system performance, troubleshoot issues, detect errors or anomalies, and monitor system health in real time.

Marketing analytics: Data pipelines are used to consolidate and analyze marketing data from different channels, such as social media, email campaigns, advertising platforms, or customer surveys. By integrating and processing this data, businesses can evaluate marketing campaign effectiveness, track customer interactions, measure ROI, and optimize marketing strategies.

Data science research: Data pipelines are instrumental in data science research projects, providing a structured and automated process to collect, preprocess, and transform data for analysis. Data scientists use pipelines to handle large volumes of data, apply algorithms, extract features, evaluate models, and iteratively refine their research experiments.

Once you pick a use case and know which type of pipeline to build, there are some common steps to follow.

12 main steps to build a data pipeline

1. Define objectives and requirements: Clearly define your data pipeline objectives and the specific requirements it must meet—consider data sources, volume, update frequency, latency, quality standards, and desired analysis outcomes.
2. Identify data sources: Identify the data sources you need to collect and process, such as databases, APIs, log files, streaming data, or external services.
3. Set up data ingestion: Establish data ingestion mechanisms, including executing database queries, making API calls, parsing log files, consuming message queues, or other techniques to extract data from sources.
4. Preprocess and transform data: Clean, validate, and transform incoming data to ensure quality and consistency. This involves handling duplicates, filling in missing values, standardizing formats, applying business rules, and aggregating data.
5. Choose data storage: Select the appropriate storage solution for your pipeline, such as a relational database, data warehouse, data lake, or cloud-based storage system, considering scalability, performance, security, and cost.
6. Integrate data: Combine and reconcile data from multiple sources to create a unified view, ensuring consistency and resolving conflicts or inconsistencies.
7. Process and analyze data: Perform further processing, analysis, and transformations on the data, applying statistical models, running machine learning algorithms, generating reports, or creating visualizations to derive insights.
8. Deliver and consume data: Determine how processed data will be delivered to end-users or downstream applications, creating APIs, dashboards, reports, or data exports for effective data access and utilization.
9. Establish data governance and security: Set up data governance policies, access controls, and security measures to protect data throughout the pipeline—this ensures privacy, confidentiality, and compliance with regulations.
10. Monitor and maintain pipeline: Implement monitoring and alerting systems to detect and address issues like data quality problems, pipeline failures, latency breaches, or performance bottlenecks, and perform routine maintenance tasks like backups, upgrades, and scalability improvements.
11. Document everything: Document the design, configuration, and operation of the data pipeline to facilitate knowledge transfer, troubleshooting, and collaboration among team members.
12. Continuously improve pipeline: Continuously evaluate and enhance the data pipeline based on feedback, changing requirements, technological advancements, and evolving business needs. This enables you to incorporate new data sources, refine processing logic, improve performance, and explore new analysis techniques.

What are the industry-leading data pipeline tools?

Data pipeline tools move data from source to destination. They ingest data from multiple sources and move it to a cloud data warehouse to make raw data useful for your business. These tools are essential for keeping data organized, accessible, and usable.

Here are some of the best data pipeline tools you can get started with.

Shipyard: Shipyard is a quick-to-deploy and easy-to-use data orchestration platform and ETL+ tool for building, managing, and monitoring data pipelines and workflows. It allows you to define, schedule, and visualize complex data pipelines using a graphical interface. Shipyard integrates with popular data processing engines like Apache Airflow and provides features for managing dependencies, tracking data lineage, and monitoring pipeline performance. Unlike other tools, Shipyard allows for no code, low code, or 100 percent your code. It's your choice.

Apache Airflow: Apache Airflow is an open-source platform for orchestrating and scheduling data pipelines. It allows you to define, schedule, and monitor workflows as Python code, providing features for dependency management, retries, error handling, and task parallelism.

Apache Kafka: Apache Kafka is a distributed streaming platform that enables scalable, high-throughput, and fault-tolerant data ingestion and processing. It provides real-time event streaming capabilities, making it suitable for building streaming data pipelines.

AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services. It simplifies the process of building and managing ETL pipelines by providing capabilities for data cataloging, data transformation, and job scheduling.

Google Cloud Dataflow: Google Cloud Dataflow is a fully managed service for building batch and streaming data pipelines. It offers a serverless and scalable environment for executing data processing tasks using Apache Beam, a unified programming model for both batch and stream processing.

Microsoft Azure Data Factory: Azure Data Factory is a cloud-based data integration service offered by Microsoft Azure. It provides a visual interface and robust data connectors to build and manage data pipelines, supporting data movement, transformation, and orchestration across various sources and destinations.

Talend Data Integration: Talend Data Integration is a comprehensive data integration and ETL tool that supports building data pipelines for batch and real-time processing. It offers a graphical interface for designing data integration workflows and provides a wide range of connectors, transformations, and data quality features.

Informatica PowerCenter: Informatica PowerCenter is a data integration and ETL tool. It offers a visual development environment for building data pipelines, supporting various data sources, transformations, and data quality processes.

Your choice of tool depends on factors such as the complexity of your pipeline, the desired features, integration capabilities, scalability requirements, and the specific cloud or on-premises environment you’re working with. It's important to evaluate and choose a tool that aligns with your business needs and technical requirements.

How do I start building data pipelines?

Building a robust data pipeline is an essential step to unlock the full potential of your data. You’re going to have to build many data pipelines and orchestrate them. We built Shipyard’s data pipeline tools and integrations to work with your existing data stack or modernize your legacy systems.

If you want to see for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start building data pipelines in 10 minutes or less and automate them.