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Delivering the right data at the right time is a primary need for any organization in the data-driven society. But let’s be honest: creating a reliable, scalable, and maintainable data pipeline is not an easy task. It requires thoughtful planning, intentional design, and a combination of business knowledge and technical expertise. Whether it’s integrating multiple data sources, managing data transfers, or simply ensuring timely reporting, each component presents its own challenges.
This is why today I would like to highlight what a data pipeline is and discuss the most critical components of building one.
What Is a Data Pipeline?
Before trying to understand how to deploy a data pipeline, you must understand what it is and why it is necessary.
A data pipeline is a structured sequence of processing steps designed to transform raw data into a useful, analyzable format for business intelligence and decision-making. To put it simply, it is a system that collects data from various sources, transforms, enriches, and optimizes it, and then delivers it to one or more target destinations.
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It is a common misconception to equate a data pipeline with any form of data movement. Simply moving raw data from point A to point B (for example, for replication or backup) does not constitute a data pipeline.
Why Define a Data Pipeline?
There are multiple reasons to define a data pipeline when working with data:
- Modularity: Composed of reusable stages for easy maintenance and scalability
- Fault Tolerance: Can recover from errors with logging, monitoring, and retry mechanisms
- Data Quality Assurance: Validates data for integrity, accuracy, and consistency
- Automation: Runs on a schedule or trigger, minimizing manual intervention
- Security: Protects sensitive data with access controls and encryption
The Three Core Components of a Data Pipeline
Most pipelines are built around the ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) framework. Both follow the same principles: processing large volumes of data efficiently and ensuring it is clean, consistent, and ready for use.
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Let’s break down each step:
Component 1: Data Ingestion (or Extract)
The pipeline begins by gathering raw data from multiple data sources like databases, APIs, cloud storage, IoT devices, CRMs, flat files, and more. Data can arrive in batches (hourly reports) or as real-time streams (live web traffic). Its key goals are to connect securely and reliably to diverse data sources and to collect data in motion (real-time) or at rest (batch).
There are two common approaches:
- Batch: Schedule periodic pulls (daily, hourly).
- Streaming: Use tools like Kafka or event-driven APIs to ingest data continuously.
The most common tools to use are:
- Batch tools: Airbyte, Fivetran, Apache NiFi, custom Python/SQL scripts
- APIs: For structured data from services (Twitter, Eurostat, TripAdvisor)
- Web scraping: Tools like BeautifulSoup, Scrapy, or no-code scrapers
- Flat files: CSV/Excel from official websites or internal servers
Component 2: Data Processing & Transformation (or Transform)
Once ingested, raw data must be refined and prepared for analysis. This involves cleaning, standardizing, merging datasets, and applying business logic. Its key goals are to ensure data quality, consistency, and usability and align data with analytical models or reporting needs.
There are usually multiple steps considered during this second component:
- Cleaning: Handle missing values, remove duplicates, unify formats
- Transformation: Apply filtering, aggregation, encoding, or reshaping logic
- Validation: Perform integrity checks to guarantee correctness
- Merging: Combine datasets from multiple systems or sources
The most common tools include:
- dbt (data build tool)
- Apache Spark
- Python (pandas)
- SQL-based pipelines
Component 3: Data Delivery (or Load)
Transformed data is delivered to its final destination, commonly a data warehouse (for structured data) or a data lake (for semi or unstructured data). It may also be sent directly to dashboards, APIs, or ML models. Its key goals are to store data in a format that supports fast querying and scalability and to enable real-time or near-real-time access for decision-making.
The most popular tools include:
- Cloud storage: Amazon S3, Google Cloud Storage
- Data warehouses: BigQuery, Snowflake, Databricks
- BI-ready outputs: Dashboards, reports, real-time APIs
Six Steps to Build an End-to-End Data Pipeline
Building a good data pipeline typically involves six key steps.
The six steps to building a robust data pipeline | Image by Author
1. Define Goals and Architecture
A successful pipeline begins with a clear understanding of its purpose and the architecture needed to support it.
Key questions:
- What are the primary objectives of this pipeline?
- Who are the end users of the data?
- How fresh or real-time does the data need to be?
- What tools and data models best fit our requirements?
Recommended actions:
- Clarify the business questions your pipeline will help answer
- Sketch a high-level architecture diagram to align technical and business stakeholders
- Choose tools and design data models accordingly (e.g., a star schema for reporting)
2. Data Ingestion
Once goals are defined, the next step is to identify data sources and determine how to ingest the data reliably.
Key questions:
- What are the sources of data, and in what formats are they available?
- Should ingestion happen in real-time, in batches, or both?
- How will you ensure data completeness and consistency?
Recommended actions:
- Establish secure, scalable connections to data sources like APIs, databases, or third-party tools.
- Use ingestion tools such as Airbyte, Fivetran, Kafka, or custom connectors.
- Implement basic validation rules during ingestion to catch errors early.
3. Data Processing and Transformation
With raw data flowing in, it’s time to make it useful.
Key questions:
- What transformations are needed to prepare data for analysis?
- Should data be enriched with external inputs?
- How will duplicates or invalid records be handled?
Recommended actions:
- Apply transformations such as filtering, aggregating, standardizing, and joining datasets
- Implement business logic and ensure schema consistency across tables
- Use tools like dbt, Spark, or SQL to manage and document these steps
4. Data Storage
Next, choose how and where to store your processed data for analysis and reporting.
Key questions:
- Should you use a data warehouse, a data lake, or a hybrid (lakehouse) approach?
- What are your requirements in terms of cost, scalability, and access control?
- How will you structure data for efficient querying?
Recommended actions:
- Select storage systems that align with your analytical needs (e.g., BigQuery, Snowflake, S3 + Athena)
- Design schemas that optimize for reporting use cases
- Plan for data lifecycle management, including archiving and purging
5. Orchestration and Automation
Tying all the components together requires workflow orchestration and monitoring.
Key questions:
- Which steps depend on one another?
- What should happen when a step fails?
- How will you monitor, debug, and maintain your pipelines?
Recommended actions:
- Use orchestration tools like Airflow, Prefect, or Dagster to schedule and automate workflows
- Set up retry policies and alerts for failures
- Version your pipeline code and modularize for reusability
6. Reporting and Analytics
Finally, deliver value by exposing insights to stakeholders.
Key questions:
- What tools will analysts and business users use to access the data?
- How often should dashboards update?
- What permissions or governance policies are needed?
Recommended actions:
- Connect your warehouse or lake to BI tools like Looker, Power BI, or Tableau
- Set up semantic layers or views to simplify access
- Monitor dashboard usage and refresh performance to ensure ongoing value
Conclusions
Creating a complete data pipeline is not only about transferring data but also about empowering those who need it to make decisions and take action. This organized, six-step process will allow you to build pipelines that are not only effective but resilient and scalable.
Each phase of the pipeline — ingestion, transformation, and delivery — plays a crucial role. Together, they form a data infrastructure that supports data-driven decisions, improves operational efficiency, and fosters new avenues for innovation.
Josep Ferrer is an analytics engineer from Barcelona. He graduated in physics engineering and is currently working in the data science field applied to human mobility. He is a part-time content creator focused on data science and technology. Josep writes on all things AI, covering the application of the ongoing explosion in the field.