ETL best practices: 14 ways to build modern data pipelines

Yet designing, running and maintaining ETL—short for extract, transform, load—pipelines remains a challenging endeavor. Data engineering teams regularly contend with accommodating data extraction from diverse sources. They’re also managing growing data volumes, ensuring data quality and reliability, dismantling data silos, fixing pipeline bottlenecks and addressing upstream failures before they become downstream disasters.

Following ETL pipeline best practices can help enterprises mitigate the risks of slowdowns, failures and time-consuming manual interventions to keep data infrastructure and pipelines running smoothly.

“ETL is at the heart of any organization,” said Wilson Shamim, a Solution Architect at IBM. “With documented best practices for ETL processes, teams can work, collaborate and build successful use cases that will help companies achieve their goals.”

Here are 14 common best practices for planning, building and optimizing ETL pipelines.

A foundational ETL best practice is determining whether an ETL pattern is the right choice in the first place. For instance, the “ETL versus ELT” question comes up with increasing frequency as more enterprises migrate their data workflows into cloud environments for data warehousing. In recent years, more enterprises have chosen ELT (extract, load, transform) to achieve data transformation at scale within target systems.

In ELT, data is loaded into data lakes or cloud data warehouses such as Snowflake and Amazon Redshift and then transformed through solutions such as Data Build Tool, commonly known as dbt. In contrast, enterprises might opt for ETL architecture because it is compatible with on-premises or legacy systems, or because it can transform and secure sensitive data before it reaches its destination.

Organizations that use ETL can select between batch processing or streaming ETL. Batch ETL might be more efficient for some workloads that are not time-sensitive, such as weekly reports. Enterprises can also choose to use incremental loading—loading only changes to existing datasets instead of reloading full datasets—to optimize resource use.

Though ETL was once known primarily as a batch-oriented data integration pattern, distributed event streaming platforms such as Apache Kafka extend ETL patterns from batch processing to continuously arriving event streams. This approach is also known as streaming ETL.

Streaming ETL is often deployed to meet real-time business intelligence needs, though it might be more resource-intensive than batch ETL. “We need to look at the tradeoffs and decide what is right for running this workload,” Shamim said.

The transformation phase of an ETL pipeline includes data profiling, data cleansing, data aggregation and data validation. These important steps can improve the quality of datasets loaded into a target system. However, taking a proactive approach to data quality in an ETL pipeline means to go beyond reliance on the transformation phase.

Often, data quality assurance starts at the source. Enterprises can use data contracts to detail data quality rules, schema definitions and service-level agreements for data producers to follow. The creation and enforcement of such contracts can enable data consumers to successfully integrate and use reliable data from various sources.

Pipeline observability measures, meanwhile, are critical to maintaining data quality. Pipeline and data observability tools can alert enterprises and data teams to anomalies, enabling them to address such issues before they propagate downstream. In addition, data lineage capabilities and audit trails can help identify the source of data quality issues so that organizations can prevent problems from recurring.

Even the most expertly designed ETL pipelines can be vulnerable to failure. Realistically, it’s not a question of if a pipeline will fail, but when. When it happens, error-handling practices can get pipelines back up and running while minimizing consequences for the rest of the enterprise.

Error handling includes building retry logic where necessary so that jobs can restart on their own a fixed number of times. If retries continue to fail, error-handling mechanisms can send alerts to data engineering teams to trigger manual intervention.

Another element of error-handling is checkpointing: recording information during specific intervals in the workflow. If there is a failure, pipeline execution can restart at the last point that it was functioning successfully, rather than going back to the very beginning.

A critical but often overlooked principle in ETL pipeline design is idempotency. This aspect represents the guarantee that running a pipeline multiple times (with the same inputs) produces the same result without creating duplicate or inconsistent data. Idempotency becomes especially important in the context of error-handling: when retry logic automatically reruns a failed job, a pipeline that is not idempotent can load duplicate records into the target system, corrupting downstream analytics and reports.

Data engineers can build idempotency into pipelines by using upsert operations—inserting new records while updating existing ones—rather than appending data blindly. Checkpointing, already a common error-handling technique, also supports idempotency by ensuring that a restarted pipeline resumes from a known safe state rather than reprocessing data that was already successfully loaded.

“Design your pipeline so that rerunning it is always safe,” Shamim said. “Idempotency is what gives you confidence that retries won’t make things worse.”

Version control is often associated with software development: It is the process of documenting and tracking changes made to code. However, it can also be applied to ETL data pipeline development and maintenance, yielding important benefits: it can help facilitate collaboration within data teams and allow teams to revert to earlier pipeline versions as necessary.

For instance, if changes to transformation logic create unintended consequences, data engineers can restore or “roll back” to a previous version of that logic. This action can help to keep the ETL pipeline operational while they consider a longer-term solution.

Version control can also help accelerate innovation and improvements to data pipelines. Data engineers can change or update code while working off from different data pipeline versions, known as branches, without affecting the main data pipeline. Then, when the change is ready, it can be merged back into the main pipeline.

Source systems change over time. Columns get added, renamed or removed; data types shift; new fields appear without warning. Without a strategy for schema evolution, ETL pipelines can break silently or load malformed data into target systems—one of the most common and costly failure modes in production data engineering.

A proactive approach to schema evolution begins with schema validation at ingestion: automatically comparing incoming data against an expected schema and alerting data teams when deviations are detected.

Enterprises can also adopt schema registries—such as the ones used alongside Apache Avro—to adapt and enforce schema contracts between data producers and consumers. Data testing frameworks such as dbt schema tests and Great Expectations can further validate that incoming data conforms to expected structures before transformation and loading occur.

A modular approach to ETL data pipeline design—also known as pipeline decomposition—can support scalability and flexibility. Complicated processes and tasks can be broken down into smaller ones within the ETL pipeline. Then, if a pipeline requires modification to accommodate evolving needs—such as data ingestion from a new source or a new transformation requirement—data teams can modify small tasks rather than rewrite the entire pipeline.

To use modular designs also means that data engineers can reuse different components and insert new ones as necessary, making it easier to build new pipelines without affecting existing ones.

“I can update a flow without affecting other flows,” Shamim said. “The modular approach gives me that level of flexibility.”

Breaking down complex tasks into smaller ones doesn’t just boost flexibility in pipeline design and maintenance—it can also accelerate and improve performance. When tasks are split into smaller operations that are performed simultaneously, this action is known as parallel processing or parallelism. By deploying parallel processing engines, enterprises can enhance ETL data pipeline throughput and scalability.

Often, parallel processing in data pipelines is accompanied by partitioning: breaking large datasets into smaller subsets of data. In other words, the smaller tasks executed simultaneously through parallel processing are focused on smaller amounts of data, further improving performance and scalability.

When the volume of incoming data spikes unexpectedly, it can overwhelm limited memory resources. Techniques for optimizing memory can improve enterprises’ ability to process and load more data.

One such technique is chunking, which entails breaking data into smaller, more manageable pieces during the extract and load phases. Another technique is to convert data from one type to another—specifically, a type that requires less memory.

Traditional ETL pipelines often perform full dataset reloads: extracting entire tables from source systems on a scheduled basis. As data volumes grow, full reloads become increasingly resource-intensive, slow and disruptive to source systems. Change data capture (CDC) addresses this challenge by tracking and extracting only the rows that have been inserted, updated or deleted since the last pipeline run.

By processing only changed data rather than full datasets, CDC reduces load on source systems, lowers pipeline latency and improves overall throughput. CDC is well suited for use cases that require near-real-time data synchronization between operational databases and analytics environments. Common CDC tools include Debezium, AWS Database Migration Service (DMS) and IBM CDC. CDC can be used alongside incremental loading strategies to further optimize resource use across the pipeline.

ETL pipelines can be useful for enterprises managing sensitive data, especially within industries subject to the European Union’s General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA) and other data security regulations. The ETL transformation phase often encompasses data masking and encryption, helping to secure data before it is loaded into target systems.

However, other security practices are also important. A data governance framework can delineate how data is accessed and by whom. A data management team can create mechanisms to provide secure access, often through role-based access control (RBAC). And continuous monitoring and threat detection can help enterprises prevent data breaches.

Data engineers can run different tests to determine whether ETL pipelines meet quality and performance expectations. One typical testing method is unit testing, which is the practice of isolating individual components of code to evaluate their functionality. Commonly associated with software development, it can also be applied to ETL components to detect coding errors before they disrupt pipeline execution.

Another common method is integration testing, which also stems from software testing practices. In contrast to unit testing, integration testing is a holistic approach: Its purpose is to assess how different pipeline components interact and whether they work together as intended. In an integration test, a functioning pipeline will deliver test data to its destination without corruption or loss.

Data pipeline automation uses software to orchestrate the movement and transformation of data with minimal human intervention. Successful pipeline automation might result in faster delivery, improved data quality and the potential for more resilient, self-healing pipelines.

For example, enterprises can use agentic data integration capabilities—offered in solutions such as IBM watsonx.data® integration—to make natural-language requests that create fully functioning data pipelines. Data quality controls and pipeline testing capabilities are often embedded directly into pipeline execution, detecting everything from row count changes to data freshness issues. And workflow orchestration tools such as Apache Airflow can manage task dependencies, execution order, retries and error-handling across pipeline components.

Other ETL solutions supporting automation include Fivetran, Informatica, Microsoft SQL Server Integration Services (SSIS) and Talend.

ETL pipeline development does not end at initial deployment. Pipelines require ongoing updates—new data sources, revised transformation logic, performance improvements and security patches. Without a structured approach to promoting changes across environments, data teams risk introducing bugs, breaking transformations or exposing sensitive data in production systems.

Environment parity is the practice of maintaining separate, consistent pipeline environments for development, staging and production. Changes to pipeline code are first built and tested in a development environment, validated against representative data in a staging environment and only then promoted to production.

This approach reduces the risk of untested changes disrupting live pipelines and aligns ETL development with established software engineering best practices. Version control systems and CI/CD pipelines can be used to automate and govern the promotion of changes across environments, further reducing the risk of manual error.

From design to execution, using current ETL best practices can result in smoothly functioning data pipelines—pipelines that support informed decision-making and transformative use cases.