ETL best practices: How to build reliable, scalable data pipelines

Extract, Transform, Load (ETL) processes form the backbone of modern data infrastructure, moving data from source systems into data warehouses where it becomes useful for reporting and analytics. But ETL implementations often fail due to overlooked fundamentals such as poor data quality, inefficient loading strategies, and inadequate error handling.

The cost of these failures shows up as inaccurate reports, failed data loads, and pipelines that break under production volume. Organizations that get ETL right from the start avoid these problems and build a data infrastructure that scales reliably.

This guide covers seven essential ETL best practices covering data quality preparation, loading strategies, error handling, security, and scaling to help you build data pipelines that work reliably from day one.

ETL stands for Extract, Transform, and Load, which is the process of moving data from source systems, transforming it to match your requirements, and loading it into a destination like a data warehouse.

Here’s how it works:

• Extract: Pull data from source systems like CRMs, ERPs, databases, or applications.
• Transform: Clean, standardize, and restructure data to match your destination schema and business requirements.
• Load: Insert the transformed data into your target system, typically a data warehouse like Snowflake, BigQuery, or Redshift.

Modern cloud data warehouses have shifted many organizations toward ELT (Extract, Load, Transform) for data integration, where transformation happens inside the warehouse using its processing power.

However, ETL best practices around data quality, error handling, and scalability apply regardless of whether you transform before or after loading.

1. Prioritize data quality and preparation

Data quality issues can easily cause more ETL failures than technical problems. Bad data in means bad data out, no matter how sophisticated your transformation logic.

There are two key best practices you should implement when it comes to data quality: Assessing data quality assessments upfront and evaluating data quality on an ongoing basis.

Implement data quality checks before loading

Data quality checks should be a standard part of your ETL process, not an afterthought. Checking data quality before loading reduces problems downstream and improves scalability by ensuring you’re only moving clean, standardized data.

Common data quality issues to watch for include:

• Duplicates: Multiple records for the same entity with slight variations
• Null values: Missing required fields that break downstream processes
• Inconsistent formatting: Dates, phone numbers, or addresses in different formats across records
• Incomplete data: Records missing critical information needed for reporting
• Inaccurate data: Reported information that doesn’t follow expected protocols or formats

Make data quality an ongoing process

Data quality assessments aren’t just a one-time check.

You need ongoing validation at key points such as:

• Before any major ETL process or pipeline update
• When establishing new processes or data sources
• Before adding new datasets to existing pipelines
• Whenever you notice poor output quality in reporting or inconsistencies in your databases

Aside from key checkpoints, it’s a good idea to review data sets at least once monthly to assess overall quality and look for red-flag issues.

As a starting point, establish data quality checks as part of your initial ETL setup. Whatever you send through the pipeline should already be clean, reducing future problems and improving scalability as data volume grows.

2. Strategically optimize incremental loading vs. full loads

How you load data significantly impacts pipeline efficiency and resource consumption.

Use incremental loading as your default

Incremental loading updates only new or changed records since the last load. This approach puts far less weight on system resources and runs much more efficiently than full loads.

When to use incremental loading:

• Regular scheduled updates (daily, hourly)
• Large datasets where full loads would be resource-intensive
• Systems with good change tracking (modified timestamps, change data capture)
• Production pipelines where efficiency matters

Reserve full loads for specific scenarios

Full loads refresh the entire dataset from scratch. They’re resource-intensive but sometimes necessary.

When to use full loads:

• Initial pipeline setup or testing
• After major schema changes in source or destination systems
• When resetting a corrupted dataset
• Historical data backfills
• Systems without reliable change tracking

3. Batch processing vs. real-time processing

Both batch and real-time processing have their place depending on your business requirements and data characteristics.

Leverage batch processing for efficiency

Batch processing moves chunks of data in scheduled batches—daily, hourly, or at other intervals. This approach is more efficient when you’re moving large volumes of data that doesn’t need immediate availability.

Batch processing is ideal when there’s a big chunk of data that’s not time-sensitive. For instance, a daily, weekly, or monthly summary of financial transactions for an e-commerce company. You don’t need real-time updates for historical reporting—batch processing efficiently handles the volume.

Know when to use real-time processing for responsiveness

Real-time processing keeps data current for applications requiring recent information.

Fast processing is ideal when you need frequently updated data for operational decisions. For instance, financial transactions for e-commerce orders updating as customers place orders. Inventory systems, fraud detection, and operational dashboards also benefit from near-real-time updates.

Pro tip: True real-time data streaming is a specialized capability with different infrastructure requirements. Close-to-real-time (updates within about two minutes) handles most business use cases efficiently without the complexity and cost of full streaming infrastructure.

4. Account for security and compliance

Data pipelines move sensitive information across systems. Security can’t be an afterthought.

Implement role-based access controls

Not everyone needs access to all data pipelines. Role-based access controls (RBAC) ensure only authorized personnel can view, modify, or execute specific ETL workflows.

Access control best practices include:

• Limit pipeline modification to authorized data engineers
• Separate read access from write access
• Audit who has access to sensitive data pipelines
• Review and update permissions regularly
• Use a platform like Celigo with built-in RBAC features

Ensure compliance certifications

If you’re handling regulated data, your ETL platform needs appropriate compliance certifications. Key certifications to verify include:

• SOC 2 for general security controls
• GDPR compliance for European customer data
• Industry-specific requirements

These certifications demonstrate that security checkpoints are established as part of the process and meet industry standards.

5. Manage dependencies with platform consolidation

As your data infrastructure grows, managing dependencies between pipelines, platforms, and workflows becomes increasingly complex.

Most organizations rely on a mix of integration patterns to keep data moving. You may use app-to-app connections between systems like your CRM and ERP through an iPaaS, API-based connections with partners, EDI transactions with trading partners, and ELT/ELT pipelines feeding your data warehouse.

When each pattern runs on a different tool, you’re left managing multiple vendors, skill sets, and potential failure points.

This brings us to one of the most important best practices: unify your data flows between different applications and data pipelines through a single platform rather than managing multiple disconnected tools.

The benefits of platform centralization include:

• Greater visibility into what’s happening across different applications, pipelines, and databases
• Reduced resource load in terms of manpower and cost managing multiple platforms
• Easier dependency management when everything runs on consistent infrastructure
• Simplified troubleshooting when issues arise
• Single governance and security model across all data movement

The fewer platforms involved in data movement, the easier it becomes to understand dependencies, track data lineage, and maintain reliable operations.

6. Error handling and monitoring

ETL pipelines can fail for any number of reasons. The question is whether you’ll find out immediately with clear resolution paths or discover problems days later when executives ask about missing reports.

Build error handling into workflows

Failed records should trigger clear, actionable errors. Error handling built into workflows maintains data consistency, reduces load on extraction processes, and helps maintain data quality standards.

Error handling fundamentals include the ability to:

• Identify which records failed and why
• Log errors with enough context to troubleshoot
• Alert appropriate teams when intervention is needed
• Track error patterns to identify systemic issues

Establish recovery checkpoints

For large data movements, establish checkpoints throughout the process. If a 100GB load fails at 80GB, recovery checkpoints let you resume from the last successful checkpoint, rather than starting over from zero.

This becomes critical for large-scale data movements where reprocessing the entire dataset wastes significant time and resources.

7. Build for scalability upfront

Building ETL processes that scale requires attention to fundamentals early. Specifically, understanding data characteristics helps you design pipelines that scale.

This is relevant because most scaling practices tie back to foundational decisions:

• Data quality checks reduce error rates as volume grows
• Incremental loading handles larger datasets efficiently
• Centralized platform management simplifies operations at scale
• Proper error handling prevents small issues from becoming scaling bottlenecks
• Security and compliance built in from the start support growth without retrofitting

If you’re using a platform that accounts for the previous six best practices, you’re essentially building a strong foundation that will scale alongside your business.

How Celigo supports ETL best practices

Celigo provides a unified integration platform that handles ETL alongside application integration, workflow automation, and B2B/EDI—eliminating the need to manage separate tools for different data movement patterns.

• Centralized data pipeline management: Build and manage ETL processes using the same platform and interface you use for application integration, reducing training overhead and operational complexity.
• Built-in data quality and validation: Implement data quality checks as part of your workflow design, ensuring clean data moves through your pipelines.
• Incremental and batch loading support: Configure pipelines for incremental updates or full loads based on your requirements, with scheduling flexibility from minutes to monthly.
• Role-based access controls: Secure sensitive data pipelines with granular permissions controlling who can view, modify, or execute workflows.
• SOC 2 and GDPR compliance: Meet security and compliance requirements with certifications built into the platform infrastructure.
• Error handling and monitoring: Track pipeline health with centralized monitoring, alerts, and detailed execution logs showing exactly where issues occur.

By consolidating ETL capabilities with your broader integration needs, you reduce platform sprawl, simplify dependency management, and gain unified visibility across all data movement.

Build reliable ETL from the start

ETL failures stem from overlooked fundamentals—poor data quality, inefficient loading strategies, inadequate error handling, and fragmented tooling. Organizations that address these issues early build data infrastructure that scales reliably rather than breaking under production load.

The key practices: implement data quality checks before loading, use incremental loading as your default, match processing speed to business requirements, build error handling into workflows, centralize on unified platforms, and design for scale from the beginning.

These aren’t nice-to-have optimizations. They’re the difference between data pipelines that support business growth and pipelines that become constant firefighting exercises for your data team.

Ready to build scalable ETL pipelines on a unified platform?

→ Schedule a demo to see how Celigo handles data integration alongside your other automation needs.