Most teams need a lot of data—the kind you can trust, query, and use without untangling a mess of exports, field mismatches, or half-broken dashboards. Beyond moving data, an extract, transform, and load (ETL) pipeline turns it into something usable—at scale and without surprises. In 2024, an estimated 149 zettabytes of data were created, captured, copied, and consumed globally, so having a pipeline that can simplify data processing is important.

Below is a guide to how ETL pipelines work, why they’re useful, and how to design one that scales with your business.

What’s in this article?

• What is an ETL pipeline?
  
• How does an ETL pipeline work?
  
• Why do businesses use ETL pipelines?
  
• What are common challenges with ETL, and how do you solve them?
  
• How can you design an ETL pipeline that scales?
  

What is an ETL pipeline?

An ETL pipeline is the system that makes raw data usable and moves it from one place to another. This is what the acronym stands for:

• Extract: Pull data from source systems.
  
• Transform: Clean and reformat that data.
  
• Load: Deliver it to a centralized destination (e.g., a data warehouse).
  

In practical terms, an ETL pipeline collects data from sources such as payments platforms, product databases, and web analytics tools. The system processes that data—cleaning it up, unifying formats, and combining systems—then pushes the final product into a place where it can be used, such as for reporting, dashboards, or modeling.

What about extract, load, and transform (ELT)?

Traditionally, ETL pipelines would transform data before loading it into the warehouse. But today, with faster computing and cheaper storage, many teams use ELT—loading raw data first, then transforming it inside the warehouse.

ELT is a different order of operations, but it serves the same purpose as ETL: moving your data into one place and in a usable state.

How does an ETL pipeline work?

ETL pipelines operate in three main stages—extract, transform, and load—but this is rarely a neat, linear process. A well-built pipeline is constantly in motion, managing different data batches, coordinating dependencies, and providing insight before the last batch finishes.

Here’s what happens at each stage:

Extract

Extraction methods vary based on the system. Rate limits and latency dictate pacing for application programming interfaces (APIs). With production databases, teams often use incremental extracts, pulling only the data that has changed since the last run to minimize load. The pipeline starts by pulling data from wherever it lives.

Sources might include:

• Relational databases (e.g., PostgreSQL, MySQL)
  
• Software-as-a-service (SaaS) platforms, via APIs from tools such as customer relationship management (CRM) systems, support software, and payment providers
  
• Flat files, logs, cloud buckets, or File Transfer Protocol (FTP) servers
  

Transform

This is the core of the pipeline and usually the most involved part. After extraction, the data lands in a staging environment to be processed. The transformation phase can involve:

• Cleaning data: Remove corrupted rows, remove duplicate records, and fill in missing values.
  
• Standardizing data: Harmonize formats and units (e.g., converting time stamps, matching currency codes).
  
• Merging data: Combine information across sources (e.g., matching user records from a CRM system with transaction history from a payment system).
  
• Deriving fields: Calculate new metrics or apply business logic (e.g., tagging “churn risk” customers based on behavior patterns).
  

You can execute these steps in programming languages such as Structured Query Language (SQL) and Python or through a transformation engine such as Apache Spark—whatever fits the size and scope of the data. The result is tidy, structured datasets that suit the business’s data model and analysis goals.

Load

Once the data is transformed, it’s ready to be moved to its final destination, which could be a:

• Cloud data warehouse (e.g., Amazon, BigQuery)
  
• Data lake
  
• Reporting database
  

The way data is loaded depends on your goals. Some teams append new records continually, while others insert rows or update them to keep tables current. Full table swaps or partition overwriting are common for data review.

Efficient pipelines handle loading in batches or bulk mode, especially at scale. This helps reduce write contention, avoid performance bottlenecks, and provide downstream systems with usable data in a predictable format.

Parallelism

In a mature pipeline, these stages don’t happen in lockstep. Instead, they’re staggered and parallelized: for instance, while Monday’s extracted data is being transformed, Tuesday’s extract can begin.

This pipeline keeps throughput high. But it also introduces possible complications: if something fails partway, you need visibility into which stage broke and how to resume without corrupting your data flow.

Orchestration

Orchestration programs such as Apache Airflow, Prefect, and cloud-native services (e.g., AWS Glue) manage these stages. They coordinate:

• Task dependencies: These determine what runs first and what follows.
  
• Scheduling: This is when each stage starts (e.g., hourly, daily, based on triggered events).
  
• Failure handling: Failure handling provides next steps when a job stalls or breaks.
  
• Resource management: This determines which computing jobs run where and how many at a time.
  

Without orchestration, ETL becomes brittle and requires manual effort. With it, your data infrastructure becomes more predictable and dependable.

Why do businesses use ETL pipelines?

Many businesses say they’re driven by data. But the real challenge is getting the right data in one place and in a state businesses can use. ETL pipelines give teams a reliable way to collect, clean, and combine data from across the business so it’s usable for analysis, reporting, forecasting, AI, audits, or investor updates.

Here’s why businesses invest in ETL pipelines:

To create a unified view across systems

Data is fragmented by default. Sales data might live in your CRM system. Transactions flow through your payments platform. Product usage is found in a log file. Each of these systems tells part of the story.

ETL pipelines extract raw data from those sources, reconcile overlapping fields (e.g., customer IDs), and load a clean, unified version into a central warehouse. For example, a SaaS business might use an ETL pipeline to combine product usage, support tickets, and billing data so it can monitor account health in one place.

This consolidated view enables better decision-making, and it’s often the only way to answer multisource questions such as, “Which marketing campaigns brought in our most valuable customers?”

To improve data quality

Raw data can be messy. Different systems use different formats, apply inconsistent labels, or contain duplicates and gaps.

ETL pipelines set a minimum standard for quality. They clean up dirty records, normalize categories and formats, and apply business rules before they send the data to software used by analysts or executives. That can mean fewer ad hoc fixes, fewer questions about mismatched fields, and more confidence in what the data is saying.

To automate manual workflows

Without ETL, teams often rely on exports, spreadsheets, and scripts that can break when someone updates a field name. This approach is slow and doesn’t scale.

ETL pipelines automate these workflows. They run on schedules or events, move data in a repeatable way, and remove the need for humans to watch over the whole process.

To support scale and intricacy

As your business grows, your data does, too. That means more customers, events, and systems. Manually combining that data becomes untenable.

ETL pipelines are built to scale. They can process large data volumes, run in parallel, and adapt as new sources and use cases emerge.

To power better analysis and decisions

Dashboards and AI models are only as good as the data that feeds them. If your pipeline is broken, so is your analysis.

ETL pipelines ensure decision-makers have timely, trustworthy data. That includes:

• Weekly revenue
  
• Customer churn trends
  
• Product performance across segments
  
• Real-time fraud signals
  

Stripe Data Pipeline lets businesses automatically push payment and financial data to platforms, without needing to build and maintain the pipeline themselves.

To manage risk and stay compliant

When data, especially sensitive data, moves between systems, there are risks—security breaches, regulatory violations, and inconsistent access controls.

With ETL pipelines, businesses have more control. They can:

• Mask or encrypt sensitive fields during processing
  
• Log access and transformations for audits
  
• Centralize data in environments with stronger security controls
  

These tasks make it easier to comply with data protection rules such as the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), and more difficult to lose sensitive data.

What are common challenges with ETL, and how do you solve them?

ETL pipelines are important, but they’re rarely simple. Their complexity comes from the real data, systems, and business logic involved. But you can solve most problems with the right architecture and habits.

Here are the most common issues with ETL and how to overcome them:

Data quality issues

The pipeline can run perfectly and still deliver poor-quality results if the source data is inconsistent or flawed.

Why it happens

• Formats or codes conflict across systems (e.g., “CA” vs. “California”).
  
• There are duplicates, missing values, or malformed entries.
  
• Downstream fields are calculated from upstream errors.
  

What helps

• Build data validation into your pipeline (not as a last step).
  
• Set thresholds and alerts for outliers or unexpected null values.
  
• Define rules for what counts as “clean,” and document them.
  
• Quarantine bad rows instead of discarding them.
  

Complex transformations

Some transformations are easy. Others get complicated fast, especially when they merge sources or apply multistep logic.

Why it happens

• Business rules change, get layered, or aren’t well documented.
  
• Joins across systems require lots of edge-case handling.
  
• Performance drops when transformations aren’t refined.
  

What helps

• Break transformations into modular steps you can test, debug, and reuse.
  
• Use version control to track logic changes over time.
  
• Move heavy computations to distributed engines, or push them to your data warehouse, if possible.
  
• Treat transformation code like production code: peer-review, test, and monitor it.
  

Performance and scalability bottlenecks

A pipeline that runs fine with 1 million records might stop at 10 million or start taking too long to finish.

Why it happens

• Processes run serially when they could be run in parallel.
  
• Systems hit limits on their input/output (I/O), central processing unit (CPU), or memory.
  
• Code processes data row by row instead of in bulk.
  
• Repeated full extracts overload source systems.
  

What helps

• Design for parallelism that makes sense for you: partition by date, region, and customer ID.
  
• Use incremental loads instead of full refreshes where possible.
  
• Off-load heavy lifting to flexible systems (e.g., distributed computing, autoscaling warehouses).
  
• Profile your pipeline regularly, and enhance the slowest steps.
  

Too many source systems and lack of standardization

Every new source adds difficulty: APIs differ, field names clash, and some sources send data once a minute while others do so once a week.

Why it happens

• Many business systems weren’t designed for integration.
  
• Source formats are inconsistent (e.g., CSV exports, APIs, legacy databases).
  
• Teams pull data in different ways without coordination.
  

What helps

• Standardize extraction methods where you can—use shared connectors or centralized ingestion tooling.
  
• Isolate logic for each source (separate modules or scripts) to make maintenance easier.
  
• Normalize field naming and metadata early in the pipeline.
  
• Use change data capture (CDC) where possible to sync just the updates.
  

Security and compliance risks

Moving sensitive data, especially customer or financial information, creates risk. Your pipeline has to account for encryption, privacy rules, and audit trails.

Why it happens

• Systems extract sensitive fields unnecessarily.
  
• Temporary storage isn’t secured.
  
• There are no logs on who accessed what and when.
  

What helps

• Mask or encrypt sensitive data during transformation.
  
• Restrict access to staging areas, and apply role-based controls.
  
• Use safe protocols for extraction and transfer.
  
• Maintain audit logs, and support deletion or redaction on request.
  

Maintenance debt and pipeline drift

Pipelines require ongoing attention as source schemata and business definitions change and jobs fail silently.

Why it happens

• Pipelines lack observability, so issues go unnoticed.
  
• No one owns the pipeline day-to-day.
  
• Logic is hardcoded and undocumented.
  

What helps

• Treat pipelines like living infrastructure: versioned, monitored, and testable.
  
• Add logging, metrics, and health checks.
  
• Use orchestration software to track dependencies and retries.
  
• Build runbooks for common failures—don’t rely on memory.
  

The right practices can mitigate these challenges and prevent them from becoming recurring emergencies. And they’ll help you build pipelines that are transparent, maintainable, and resilient enough to grow with your business.

How can you design an ETL pipeline that scales?

The real test of an ETL pipeline is how well it can function when your data increases by a factor of 10, your business model shifts, or 3 new systems come online. A flexible pipeline can absorb that change without breaking, slowing down, or becoming too complex.

Here’s how to build scalability into your pipeline:

Start with growth in mind

Scalability is about being ready for more:

• Sources
  
• Volume
  
• Teams that need access
  
• Regulatory overhead
  

Consider what might break first if this pipeline needs to support 10 times the data or populate 5 new dashboards. Build with enough capacity that you won’t be forced to do a costly rebuild six months from now.

Use architecture that handles scale

Some pipelines are doomed from the start because they rely on systems or processes that don’t scale horizontally. To avoid that:

• Choose processing engines that can run jobs in parallel across multiple machines
  
• Use databases or warehouses that can separate storage and computing, and scale each one independently
  
• Do batch loads or partitioned writes rather than row-by-row operations
  

If any part of your pipeline maxes out one machine, that’s your bottleneck.

Design for parallelism

Parallelism is how you minimize runtime and raise capacity. Serial pipelines might feel safe, but they’re slow. If you’re processing one file, customer, or region at a time, your throughput is capped—no matter how powerful your infrastructure is. Instead, you should:

• Partition data by logical units (e.g., date, region, customer ID)
  
• Run extraction, transformation, and loading steps concurrently when dependencies let you
  
• Make each stage stateless so multiple instances can run in parallel
  

Lean on cloud elasticity

Cloud infrastructure makes it easier to scale ETL without overprovisioning. You can:

• Scale computing automatically when demand peaks
  
• Use object storage services for staging without worrying about capacity
  
• Let managed ETL services handle the heavy lifting of resource allocation
  

Improve minor issues before they become urgent

In terms of scaling, small choices make a big impact. Some actions that help include:

• Using columnar file formats (e.g., Parquet) for staging to speed up reads and writes
  
• Compressing large files to reduce I/O time
  
• Writing efficient SQL queries, and avoiding unnecessary transformations
  
• Profiling your jobs to find bottlenecks early
  

Keep the pipeline modular

Modular pipelines are easier to grow, test, and troubleshoot. They scale organizationally as well as technically. When you need to add a new data source or change a transformation rule, you don’t want to unravel a 2,000-line monolith. Instead, you should:

• Break your pipeline into logical stages (e.g., ingestion, processing, loading)
  
• Encapsulate transformations so they can be updated or reused independently
  
• Document inputs, outputs, and dependencies clearly
  

Build for visibility

As the pipeline grows, so does the need to understand what’s happening inside it. You can’t fix or scale what you can’t see. Ensure you:

• Monitor job runtimes, row counts, error rates, and freshness
  
• Set alerts for failures and thresholds
  
• Track data lineage so teams know where data came from and how it changed
  
• Log events at every step with enough context to debug issues fast
  

Good visibility is what lets you scale with confidence.