A data pipeline is an automated system that moves raw data from various collection points to a storage destination for analysis. It acts as the "piping" for business intelligence dashboards and data science projects, ensuring data is cleaned, structured, and ready for use.
Without these pipelines, teams must manually move data into analytics engines, which is slow and prone to error.
What is a Data Pipeline?
A data pipeline is an end-to-end sequence of digital processes used to collect, modify, and deliver data. It extracts information from sources like SaaS apps, APIs, or SQL databases, transforms it into a usable format, and saves it in a destination known as a "data sink."
These pipelines are essential for digital transformation because they break down information silos. They allow organizations to combine data from disparate systems that might otherwise use incompatible formats.
Why Data Pipeline matters
- Breaks down silos: Consolidates data from various tools into one storage location for a unified view.
- Improves data quality: Automates filtering and validation to remove duplicates and inconsistencies.
- Faster time to insight: Delivers the information you need to dashboards automatically without manual exports.
- Reduces engineering overhead: Automated management tools remove the need for developers to write custom scripts for every data transfer.
- Enables scalability: Provides a structured way to handle growing volumes of data that would be impossible to process manually.
How a Data Pipeline works
A standard data pipeline follows three core steps to move information from source to storage.
- Data Ingestion: The pipeline collects data from sources like IoT devices, mobile apps, or over 700 pre-built connectors (Fivetran). Sources may "push" data into the pipeline or the pipeline may "pull" it via API calls.
- Data Transformation: Raw data is cleaned and reformatted. This includes masking sensitive info, filtering unnecessary columns, or unrolling JSON formats into tables. This step ensures the data matches the "schema" (the set structure) of the destination database.
- Data Storage: The processed data is stored in a repository like a data lake or data warehouse. Once stored, it is available for stakeholders to use in charts, plots, or machine learning models.
Types of Data Pipeline
| Type | Latency | Common Use Case |
|---|---|---|
| Batch Processing | High | Monthly accounting or traditional business intelligence. |
| Streaming Data | Low (Real-time) | Fraud detection, inventory updates, or point-of-sale systems. |
| Cloud-Native | Variable | Modernizing analytics across multi-cloud environments. |
| Event-Driven | Low | Triggering a log or action after a specific transaction. |
Batch Processing
This method loads "batches" of data at specific intervals, often during off-peak hours to avoid taxing systems. In a milestone for this technology, the MapReduce batch processing algorithm was patented in 2004 (IBM) and later integrated into open-source systems like Hadoop.
Streaming Data
Also called event-driven architecture, this type processes data continuously. For example, a retail app uses streaming to update inventory immediately when a customer makes a purchase. While fast, these systems are sometimes less reliable than batch systems because messages can occasionally be dropped in the queue.
Best practices
- Automate schema drift detection: Use tools that automatically update your destination warehouse when a source database adds or changes a column.
- Land raw data first: Store a copy of raw data in your warehouse before transforming it. This allows you to re-process historical data if your business requirements change later.
- Implement data observability: Use monitoring tools to track expected events and alert you if the pipeline breaks or reports anomalies.
- Use cloud-native tools: Choose serverless environments to improve productivity and scale resources up or down based on data volume.
- Standardize transformations: Build repetitive workstreams for business reporting to ensure data is cleansed consistently every time.
Common mistakes
Mistake: Hard-coding every connector. Fix: Use pre-built connectors or a unified interface to reduce the time spent maintaining custom code.
Mistake: Ignoring technical dependencies. Fix: Map out the sequence of commands so the pipeline doesn't stall while waiting for a central queue to fill.
Mistake: Skipping the validation step. Fix: Incorporate data quality rules early in the design phase to filter out "trash" data before it reaches your dashboards.
Mistake: Overlooking business dependencies. Fix: Account for moments where the pipeline must pause for a specific business unit to cross-verify data.
Examples
- Health Care Management: Intermountain Healthcare converted approximately 5,000 batch jobs (Informatica) to modernize how they provisioned patient data across 600 different sources.
- AI Platform Integration: SparkCognition uses pre-built connectors to allow customers to discover and pull data from virtually anywhere into their AI-powered data science platform.
- Customer 360 Dashboards: A marketing team uses a pipeline to feed data from CRM, product logs, and support tickets into one central dashboard to see a complete view of customer behavior.
Data Pipeline vs ETL
While often used interchangeably, these terms have distinct differences.
| Feature | Data Pipeline | ETL (Extract, Transform, Load) |
|---|---|---|
| Scope | Broad term for any data movement. | A specific subcategory of pipelines. |
| Sequence | Can be ETL or ELT (Transform after loading). | Always follows the E-T-L sequence. |
| Processing | Includes both batch and real-time streaming. | Historically focused on batch processing. |
| Transformation | Transformations are optional. | Transformations are a core requirement. |
Rule of thumb: Every ETL process is part of a data pipeline, but not every data pipeline has to follow the ETL sequence.
FAQ
What is the difference between a data warehouse and a data sink? A data warehouse is a specific type of storage used for structured data analysis. A "data sink" is a general term for any endpoint where the data pipeline finishes, which could be a warehouse, a data lake, or another application.
How do you measure pipeline success? Success is typically measured by latency (how fast data moves), data accuracy (quality checks), and uptime (how often the pipeline runs without failing).
Do all pipelines need to transform data? No. While rare, some pipelines simply replicate data from one place to another without changing it. However, most business use cases require transformation to make the data compatible with analysis tools.
What is a technical dependency in a pipeline? This occurs when one process cannot start until another is finished. For example, a filtering command might have a technical dependency on the ingestion command being 100% complete.
Why use ELT instead of ETL? ELT (Extract, Load, Transform) has become more popular with cloud-native tools. It allows you to store raw data quickly and perform transformations using the processing power of the cloud data warehouse itself.
