Top Amazon Redshift Best Practices for Modern ETL & ELT Workflows

As more organizations turn to cloud data warehouses, they’re also finding the need to optimize them to get the best performance out of their ETL processes. Amazon Redshift, offers the speed, performance, and scalability required to handle the exponential growth in data volumes, making it a leading choice for cloud data warehousing, but modern workloads demand a fresh approach to optimization. These updated best practices help you improve performance, reduce costs, and scale data integration and transformation effectively.

1. Use the COPY Command for Efficient Loads

The COPY command is the most efficient way to load data into Redshift. It supports parallel loading, works with various formats like CSV, JSON, or Parquet, and can auto-detect compression. To maximize performance, split large datasets into multiple smaller files (ideally between 1MB and 1GB), store them in S3, and load them in parallel. The COPY command minimizes the need for intermediate transformations and scales with Redshift's Massively Parallel Processing (MPP) architecture. Tools like Matillion make it easy to automate and monitor COPY operations within orchestrated workflows.

2. Stage Data Before Loading into Final Tables

Always load incoming data into staging tables first. This lets you validate, clean, and transform data before inserting it into production tables, reducing the risk of corrupting reporting layers. Staging tables also make it easier to perform deduplication, schema validation, or enrichment before moving data downstream. In Matillion, staging-to-final-table workflows can be visually designed and reused, making pipeline design more efficient.

3. Compress Data Before Loading

Compression reduces the volume of data transferred during loads and improves query performance. Redshift automatically applies column-level compression using encodings, but compressing the source files (e.g., using GZIP) before loading can significantly speed up COPY operations. Smaller file sizes also reduce latency during parallel processing. Most ETL tools, including Matillion, support pre-compression as part of the data export or pipeline configuration process.

4. Split Large Files for Parallelism

Redshift performs best when data is loaded across multiple slices in parallel. Instead of loading a single large file, split your data into multiple smaller files to optimize parallelism. Store the files in S3 and load them using the COPY command. The optimal number of files is at least equal to the number of slices in your Redshift cluster. Matillion can orchestrate these multi-part loads by partitioning datasets during extraction.

5. Use Sort and Distribution Keys Wisely

Redshift performance is heavily influenced by how data is sorted and distributed across nodes. Sort keys help optimize query performance by minimizing the amount of data scanned, while distribution keys control how data is physically stored and joined. Choose sort keys based on frequent filter or join columns. Use distribution styles like KEY, EVEN, or ALL depending on data volume and join patterns. Matillion enables users to define or modify keys directly within transformation components.

6. Leverage ELT (Not ETL) for Performance

ELT (Extract, Load, Transform) processes take full advantage of Redshift's in-cluster compute power. Load raw data into Redshift, then run transformations using SQL. This eliminates unnecessary data movement and scales better as data volumes grow. Matillion’s push-down ELT model runs SQL transformations directly in Redshift, improving performance and reducing infrastructure complexity compared to traditional ETL.

7. Incrementally Load Data

Reloading entire tables every time is inefficient. Instead, use incremental loading techniques like timestamp-based filters, change data capture (CDC), or row hashing to identify and load only new or updated records. This approach improves performance, lowers costs, and reduces transformation overhead. With Matillion, you can visually configure delta loads, apply CDC logic, and reuse components across pipelines — all without writing complex scripts.

8. Monitor Table Stats and Vacuum Regularly

Redshift doesn’t automatically update table stats or compact storage. Run ANALYZE regularly to refresh statistics and help the query planner make better decisions. Run VACUUM to reclaim space from deleted rows and maintain sort order, especially on large or heavily updated tables. Automate these tasks during off-peak hours using Matillion or external schedulers to keep performance high.

9. Optimize WLM (Workload Management) Queues

Redshift’s Workload Management (WLM) lets you allocate system resources by query type or user group. Assign ETL workloads to a separate queue to prevent long-running jobs from blocking interactive dashboards or ad-hoc analysis. You can define memory limits, slot count, and timeout rules per queue. This keeps your ETL processes predictable and prevents resource contention.

10. Avoid SELECT * in Queries

Using SELECT * can degrade performance, especially when working with wide tables. It causes Redshift to scan more columns than necessary, increasing disk I/O and query time. Instead, always specify the columns you need. This practice improves query efficiency and reduces unnecessary data movement. Metadata-aware tools like Matillion can help users manage and update column-level selections as schemas evolve.

11. Unload Data Before Complex Joins or Exports

If you're exporting data for external tools or reporting, use the UNLOAD command to write query results to S3. This is faster and more scalable than querying Redshift directly through BI tools. It also decouples downstream processing and provides an audit trail. Matillion can automate UNLOAD operations and push them to cloud storage as part of end-to-end orchestration.

12. Use Spectrum for S3-Based Queries

Redshift Spectrum allows you to query structured data stored in S3 without loading it into Redshift. This is useful for accessing infrequently queried or historical data. Define external tables using Glue or Hive-compatible metadata and join them with internal Redshift tables. This hybrid architecture reduces storage costs while expanding your analytical reach.

13. Audit and Track ETL Jobs

Monitoring ETL job execution is crucial for reliability. Use Redshift system tables like STL_QUERY, SVL_QLOG, and STL_LOAD_COMMITS to track performance and failures. Incorporate logging and alerting to catch anomalies early. Matillion includes built-in job auditing, logging, and error notifications, giving teams full visibility into pipeline health.

14. Secure Access and Apply Role-Based Policies

Secure data access by using AWS IAM roles and Redshift role-based access control (RBAC). Apply fine-grained permissions to users and groups to restrict access to sensitive tables and operations. Encrypt data in transit and at rest using AWS KMS. Matillion supports Redshift role-based permissions, allowing secure data movement across trusted cloud environments.

15. Use Cloud-Native ETL Tools Like Matillion

Native tools like Matillion are optimized for Redshift and the cloud. Matillion provides low-code job design, native Redshift integration, and orchestration in one platform. It simplifies ELT, supports advanced transformations, and includes version control, scheduling, and monitoring features — helping teams accelerate time-to-value and scale with confidence.

Want to Learn More About Amazon Redshift?

Matillion is an industry-leading data transformation solution that was originally introduced on Amazon Web Services for the Amazon Redshift cloud data warehouse. Delivering a true end-to-end data transformation solution (not just data migration or data preparation), Matillion provides an instant-on experience to get you up and running in just a few clicks, a pay-as-you-go billing model to cut out lengthy procurement processes, as well as an intuitive user interface to minimize technical pain and speed up time to results.

Matillion ETL for Amazon Redshift, which is available on the AWS marketplace, has the platform’s best practices baked in and adds additional warehouse specific functionality, so you get the most out of Redshift.

Matillion Data Productivity Cloud allows you to reimagine the way you build and manage data pipelines, with no coding required. Accelerate data delivery for advanced AI use cases and analytics.