A data pipeline is a system that automatically moves data from one or more source systems to a destination where it can be used. The source might be a database, an API, a file upload, or an event stream. The destination is typically a data warehouse, a reporting database, or an analytics platform where data from multiple sources can be queried together.
The "pipeline" metaphor is apt: data flows in at one end, transformations happen in the middle, and clean, structured data comes out the other end. What makes a pipeline valuable is that this happens automatically, continuously or on a schedule, without manual intervention.
Why you cannot just query the source directly
The first question most people ask is: why not query the operational database directly for reports? There are three reasons this does not work at scale.
First, your operational database is optimized for transactions: fast reads and writes of individual records. Analytical queries (aggregations, joins across large tables, time-series comparisons) are structured differently and can degrade operational performance when run against a transactional database.
Second, your data is in multiple systems. Your CRM has customer data, your billing system has revenue data, your product database has usage data, and your marketing tools have campaign data. Joining these together requires a central location that holds copies of all of them.
Third, you need historical snapshots. Operational databases store current state. If a customer upgraded their plan last month, the database shows the current plan, not what it was three months ago. A data pipeline can snapshot state over time, enabling historical analysis that is impossible from the operational system directly.
Batch pipelines vs. streaming pipelines
Most business analytics use cases run on batch pipelines: data is extracted from source systems on a schedule (every hour, every night at 2am), transformed, and loaded into the warehouse. Reports and dashboards reflect data as of the last pipeline run. For most reporting use cases, data that is a few hours old is perfectly acceptable.
Streaming pipelines process events as they occur, typically within seconds. They are appropriate when near-real-time data is genuinely required: fraud detection, real-time inventory systems, live dashboards for operations teams, or alerting systems that need to respond to events immediately.
Streaming pipelines are more complex and expensive to build and operate than batch pipelines. The common mistake is building a streaming pipeline for a use case that a batch pipeline would serve equally well. If your stakeholders check dashboards in the morning, you do not need streaming data.
Signs your business needs a data pipeline
The clearest indicator: someone on your team spends meaningful time each week manually exporting data from multiple systems and combining it in a spreadsheet before running any analysis. This process is slow, error-prone, and does not scale.
Other indicators include: you cannot answer basic business questions without pulling from multiple tools; your reports are always slightly out of date; different people have different numbers for the same metric because they pulled data differently; or you are starting to use BI tools like Tableau or Looker but do not have a clean data source to connect them to.
You probably do not need a data pipeline if all your reporting comes from a single system, that system has adequate built-in reporting, and you do not need to combine it with data from anywhere else.
What the AWS-native stack looks like
For most mid-sized businesses building their first data pipeline, the AWS stack looks like this:
Ingestion: AWS Glue crawlers or custom Lambda functions extract data from source systems on a schedule. For SaaS data sources with APIs (Salesforce, HubSpot, Stripe), managed connectors handle the extraction without custom code.
Storage: Raw data lands in S3 in a "data lake" layer. S3 is cheap, durable, and schema-agnostic. You can store any format and figure out the structure later.
Transformation: AWS Glue ETL jobs or dbt (running on a small ECS container) transform raw data into clean, structured tables. This is where business logic lives: defining what "monthly recurring revenue" means for your specific billing model, or how to join customer records across systems that use different identifiers.
Serving: Transformed data is loaded into Amazon Redshift or exposed directly through Athena for querying. BI tools connect here.
What building a pipeline actually involves
The technical work is roughly 40% infrastructure setup, 40% business logic in transformations, and 20% ongoing maintenance and evolution.
The business logic part is where most of the time goes, and it requires close collaboration with the people who understand the data. Questions like "what counts as an active customer" or "how should we handle refunds in revenue reporting" have no technical answer. They require decisions from the business.
A well-built pipeline is also not a project with an end date. Source systems change their schemas, new data sources get added, business definitions evolve. The pipeline needs to be maintained and updated accordingly. This is typically a small ongoing commitment rather than a large periodic rebuild.
For most growing businesses, the ROI of a properly built data pipeline comes from replacing hours of weekly manual reporting with reliable, automated data, and from making better decisions faster because the numbers are trustworthy and available.