Amazon Redshift ETL: 3 Best Approaches to Integrate Data in 2025

Amazon Redshift has become one of the most widely used cloud data warehouses for teams that need to store and analyze large volumes of data. Its scalability, speed, and integration with the AWS ecosystem make it a reliable choice for analytics and reporting.

To make the most of Redshift, you need more than just a place to store data. You need a structured way to move data from source systems, prepare it, and load it efficiently. This is where ETL (extract, transform, load) workflows come in. ETL ensures that your Redshift data is accurate, consistent, and ready for analysis.

In this guide, you’ll learn what Amazon Redshift is, why ETL is important, and the three main approaches to building Redshift ETL pipelines. By the end, you’ll understand when to use ETL tools like Estuary Flow, when to rely on native Redshift features, and when custom scripts might be the right choice.

What Is Amazon Redshift? An Overview

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Amazon Redshift is a fully managed cloud data warehouse offered by Amazon Web Services (AWS). It is designed to store and analyze very large datasets at scale, often reaching petabytes of data.

Redshift is built on PostgreSQL 8.0.2, which means anyone familiar with SQL can query it using standard SQL syntax. Unlike traditional row-based databases, Redshift uses a columnar storage format and massively parallel processing (MPP) to deliver fast performance for analytical workloads.

If you’re considering Redshift for advanced analytics, you may also want to explore how it connects to other platforms. For example, here’s a step-by-step guide on migrating from Amazon Redshift to BigQuery or setting up a real-time Redshift to Databricks pipeline.

Amazon Redshift: Key Features 

Here are some of the reasons why so many teams rely on Amazon Redshift: 

• Serverless architecture. Amazon Redshift provides a serverless architecture for data management. This feature enables you to handle analytic workloads of any size without the need to manage the structure of the data warehouse. As such, developers, data scientists, and analysts can collaborate to build data design and train machine learning models without configuring complex infrastructure tasks. 
  
• Petabyte-scale data warehouse. The managed storage of Redshift supports workloads of up to 8 petabytes of compressed data. This robust storage capacity enables you to add almost any number or type of nodes to your data warehouse.
  
• Federated queries. Redshift’s federated query capability helps you query live data across one or more of Amazon's Relational Database Services (RDS). This includes querying data from Aurora MySQL, RDS, and Aurora PostgreSQL databases without the need for data migration. 
  
• End-to-end encryption. With just a few clicks, you can configure Amazon Redshift to employ hardware-accelerated AES-256 encryption for data at rest and SSL for data in transit. All the data stored on the disc, including backups, will be encrypted if you decide to enable data encryption at rest. Additionally, complex tasks like key management for encryption are handled by Redshift by default. 

What Is ETL and Why Does It Matter for Redshift?

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ETL stands for Extract, Transform, Load. It is the standard process for moving data from multiple sources into a single destination like Amazon Redshift. The goal of ETL is to make raw data usable for analytics by ensuring it is accurate, consistent, and well-structured.

1. Extract – Data is pulled from various sources such as transactional databases, APIs, flat files, or spreadsheets. At this stage, the goal is to collect data into a staging area without altering it.
   
2. Transform – Raw data is cleaned, validated, and reshaped into a format that fits the target system. This may include filtering rows, applying business rules, or creating new calculated fields.
   
3. Load – The processed data is loaded into the target system, such as Amazon Redshift, where it can be queried and analyzed.

ETL is essential for analytics because it ensures your Redshift environment always has high-quality, up-to-date data. Without proper ETL, you risk inaccurate reporting, poor data governance, and slower decision-making.

If you’re comparing approaches, it’s worth understanding the difference between ETL and ELT. Many modern teams use ELT in Redshift to leverage its transformation power, but ETL remains common when data must be cleaned or standardized before loading.

Top 3 Approaches for Amazon Redshift ETL

There isn’t a single “best” way to run ETL into Amazon Redshift — the right choice depends on your team’s technical skills, resources, and data requirements. Broadly, there are three proven approaches to managing Redshift ETL:

1. Using ETL Tools for Amazon Redshift – Third-party and SaaS platforms that automate data ingestion, transformation, and loading.
   
2. Leveraging Native Amazon Redshift Functions – Built-in commands like COPY, UNLOAD, and workload management for ETL pipelines.
   
3. Redshift ETL with Custom Scripts – Writing your own code for complete control and flexibility over extraction, transformation, and loading.

Each approach has trade-offs in terms of automation, scalability, and engineering effort. In the next sections, we’ll break down these methods so you can choose the one that fits your data strategy.

1. Using ETL Tools for Amazon Redshift ETL

One of the most effective ways to move data into Redshift is by using modern ETL tools. These platforms act as a bridge between your data sources and Redshift, handling extraction, transformation, and loading with minimal manual effort. The biggest advantage is that they automate repetitive tasks such as scheduling, monitoring, schema management, and error handling.

In this section, we’ll examine some of the popular tools for Amazon Redshift ETL.

Estuary Flow 

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Estuary Flow provides a no-code data pipeline that makes Redshift integration seamless. With just a few clicks, you can capture data from databases, SaaS platforms, or event streams and materialize it directly into Redshift in real time. Flow is designed around streaming data, which means you’re not limited to batch ETL; your Redshift warehouse can stay continuously up to date without complex engineering.

Flow also enforces schemas automatically and supports Change Data Capture (CDC), ensuring high-quality, consistent data across all workloads. This makes it ideal for teams that want to modernize ETL without the overhead of writing and maintaining custom pipelines.

Amazon Glue

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Amazon Glue is an event-driven computing platform launched by AWS. It offers a service that automatically manages the computing resources needed to run code when events occur. Using Glue, you can streamline various data integration tasks, such as schema evaluation, automated data discovery, and job scheduling. Additionally, it provides a serverless data integration solution for various AWS services, including Redshift, by combining AWS Lambda and Amazon S3. 

Talend

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Talend is an open source ETL tool for data integration. It has a rich set of tools for connecting with many data sources and destinations, including data warehouses. With its visual interface, you can quickly design data pipeline workflows, enhancing the overall ETL process.

When to choose ETL tools:

• You need automation and monitoring without maintaining scripts.
• You want to integrate multiple data sources into Redshift.
• You prefer a low-code or no-code environment for faster pipeline building.

2. Using Native Amazon Redshift ETL Functions

Leveraging Amazon Redshift’s native capabilities is one of the most straightforward ways to perform ETL operations. With a robust data warehouse capacity and Amazon’s server support, Redshift offers many features to streamline the ETL process. 

If you go this route, keep these best practices in mind while using native Redshift functions for ETL: 

• Load data in bulk. Redshift was built to handle huge amounts of data. You can collect the data from all the S3-supported sources and then perform a COPY operation to load it into Redshift directly from an S3 bucket. 
  
• Extract large files using UNLOAD. Redshift allows you to extract the files using two commands: SELECT and UNLOAD. SELECT is ideal for performing extraction in small to medium-sized data files, but it operates sequentially, which puts a lot of pressure on the cluster when dealing with large files. UNLOAD, on the other hand, is designed to extract large files from Redshift efficiently. It offers many benefits, including Amazon S3 integration, parallel loading, data compression, and more.
  
• Regular table maintenance. Redshift’s capacity to quickly perform data transformation results in the constant creation of tables and rows. Even after not being used for a while, many tables might not be automatically deleted after their creation. Therefore, your cluster may become disorganized as outdated data takes up excessive space. To address this issue, you can perform regular table maintenance and functions like VACUUM and ANALYZE to keep your Redshift cluster optimized.
  
• Workload management. Use Workload Management (WLM) in Redshift to prioritize different tasks by creating a queue for each one. This feature allows you to prioritize tasks within the data pipeline, ensuring that short-running queries don’t get stuck by long-running ones. It also helps manage query concurrency and resource allocation in a data warehouse.

When to choose native functions:

• You’re already heavily invested in AWS.
• You want a low-cost option without external tools.
• Your team has the SQL expertise to maintain pipelines manually.

3. Redshift ETL with Custom Scripts

Custom scripts are pieces of code written to address an organization’s particular data integration and transformation needs. Usually, data engineers or developers write these scripts to automate data movement and enhance the ETL process from many source systems to a desired location. 

Here are some of the advantages of using ETL scripts: 

• By writing customized ETL scripts, you can define the exact execution process of data extraction, transformation, and loading according to your requirements. 
  
• Custom scripts come in handy when you have evolving data integration requirements. By writing your own scripts, you can adapt changes in data sources, business logic, and schemas.
  
• You can write custom scripts for tasks like data partitioning, parallel processing techniques, and query mechanisms to optimize the performance of data processing in Redshift. 
  
• Amazon Redshift ETL tools have predefined data governance and policies according to each tool’s guidelines. However, custom scripts give you more flexibility. You can use custom scripts to enforce data access control and governance policies in Redshift by implementing role-based access control, employing data masking and encryption techniques, and defining your data retention policies.
  
• Many ETL tools have subscription models that charge according to your usage. With the help of custom scripts, you can choose to modify resource usage. By only using the compute capacity and functionalities you require and eliminating those you don’t, you will be able to make your ETL operations more cost-effective.   

When to choose custom scripts:

• You need very specific transformations that aren’t supported by ETL platforms.
• Your data sources are highly custom or niche.
• Your engineering team has the time and expertise to maintain scripts long-term.

While scripts provide maximum control, they can also increase technical debt and slow down iteration compared to ETL tools like Estuary Flow, which automate much of this process.

The Takeaway

Amazon Redshift ETL can be approached in three main ways:

1. ETL tools like Estuary Flow, AWS Glue, or Talend for automation and scalability.
2. Native Redshift functions such as COPY, UNLOAD, and workload management for direct, cost-efficient ETL.
3. Custom scripts for teams that need complete flexibility and control.

Choosing the right method depends on your resources and goals. Scripts give flexibility, native functions are cost-effective, and ETL tools deliver speed and automation.

Estuary Flow offers the best long-term choice for many teams. It combines real-time CDC with a no-code interface, helping you sync data to Redshift without the engineering overhead of scripts or the limits of native tools.

Choosing the right Redshift ETL strategy is ultimately about balancing flexibility, cost, and time to value. Whether you prefer to manage scripts yourself, rely on Redshift’s built-in features, or automate with a no-code platform like Estuary Flow, the goal is the same: getting reliable data into Redshift to power analytics at scale.

👉 Ready to simplify Redshift ETL? Create your free Estuary Flow account and build your first Redshift pipeline today.