Loading CSV data into Amazon Redshift can be tricky. CSV files seem simple, but issues like incorrect data types, null values, and character encoding can derail your imports. Thankfully, Redshift offers multiple methods to import CSV data that avoid these pitfalls. This article explores 3 easy ways to load CSV to Redshift so you can get your data online faster. Learn how to use the COPY command, AWS Data Pipeline, and ETL tools to effortlessly ingest CSVs regardless of size, complexity or current data infrastructure.

Methods to Load CSV to Redshift 

There are some standard methods devised for you to load data into Amazon Redshift. Some of these offer an added convenience for loading CSV files. While the ways listed below are independently easy to follow up with, you can choose one that fits your data requirements.

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Method 1: Load CSV to Redshift Using Amazon S3 Bucket

One of the simplest ways of loading CSV files into Amazon Redshift is using an S3 bucket. It involves two stages – loading the CSV files into S3 and consequently loading the data from S3 to Amazon Redshift.

Step 1: Create a manifest file that contains the CSV data to be loaded. Upload this to S3 and preferably gzip the files.

Step 2:  Once loaded onto S3, run the COPY command to pull the file from S3 and load it to the desired table. If you have used gzip, your code will be of the following structure:

COPY <schema-name>.<table-name> (<ordered-list-of-columns>) FROM '<manifest-file-s3-url>' 

CREDENTIALS'aws_access_key_id=<key>;aws_secret_access_key=<secret-key>' GZIP MANIFEST;

Here, using the CSV keyword is of significance to help Amazon Redshift identify the file format. You also need to specify any column arrangements or row headers to be dismissed, as shown below:

COPY table_name (col1, col2, col3, col4)
FROM 's3://<your-bucket-name>/load/file_name.csv'
credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'

-- Ignore the first line
COPY table_name (col1, col2, col3, col4)
FROM 's3://<your-bucket-name>/load/file_name.csv'
credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>'

This process will successfully load your desired CSV datasets to Amazon Redshift in a pretty straightforward way.

There is an alternate way to unload data to Redshift using S3 buckets- use UNLOAD Command to Export Data from Redshift. To use this command, follow the steps mentioned below:

  • You can choose the data you want to move from Redshift and then give a valid path to your S3 bucket.
  • Use filter to get the data using the select statement and export your data as needed. When your query is ready, use a command to proceed with the next steps:
UNLOAD ('SELECT * FROM test.sample_csv')
TO 's3://csv-redshift-221/Unload_'
credentials 'aws_access_key_id=AKIA46SFIWN5JG7KM7O3;aws_secret_access_key=d4qfQNq4zYL39jcy4r4IWAxn4qPz4j8JgULvKa2d'

Method 2: Load CSV to Redshift Using an AWS Data Pipeline

You can also use the AWS Data Pipeline to extract and load your CSV files. The benefit of using the AWS Data Pipeline for loading is the elimination for the need to implement a complicated ETL framework. Here, you can implement template activities to efficiently carry out data manipulation tasks.

Use the RedshiftCopyActivity to copy your CSV data from your host source into Redshift. This template copies data from Amazon RDS, Amazon EMR, and Amazon S3

Load CSV to Redshift: Data Pipeline to Redshift
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The limitation can be seen in a lack of compatibility with some data warehouses that could be potential host sources. This method is essentially manual as the copy activity implements after every iteration of data loading. For a more reliable approach, especially when dealing with dynamic data sets, you might want to rely on something that is self-managed.

Download the Cheatsheet on How to Set Up High-performance ETL to Redshift
Download the Cheatsheet on How to Set Up High-performance ETL to Redshift
Download the Cheatsheet on How to Set Up High-performance ETL to Redshift
Learn the best practices and considerations for setting up high-performance ETL to Redshift

Method 3: Load CSV to Redshift Using Hevo Data

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You can simulate CSV data loading with Hevo in a few simple steps:

Step 1: Configure the Source Data Warehouse:
Instead of using an intermediary channel, you can directly configure your source data warehouse. Hevo supports a vast variety of warehouses, including Redshift, Snowflake, BigQuery and several others. 

Step 2: Configure the Destination:
To load your data from the data warehouse of your choice, configure the destination warehouse by merely providing your credentials. Enter your Redshift credentials, a name for your database, host, and port number for your Redshift database, and simulate an easy integration with a few clicks.


Features of Hevo Data

Let’s look at some salient features of Hevo:

  • Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. 
  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
  • Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

Here’s what the staff software engineer of Deliverr has to say about using Hevo for their data integration needs:

One of the biggest reasons why I would recommend Hevo is because of its lowest price-performance ratio as compared to the competition. It is definitely one of the best solutions if we take into consideration 3 major aspects – scalability, productivity, and reliability.

– Emmet Murphy, Staff Software Engineer, Deliverr

Simplify your ETL process with Hevo today! 


Significance of Performing Redshift CSV Load

While data can be loaded after conversions into other formats onto your destination Warehouse, there are several benefits to load CSV to Redshift.  

CSV files are much easier to import into various storage databases irrespective of the software in use. As it’s in plain text, it makes them a standard representation of data that is also human-readable. These features make the use of CSV files an excellent option for businesses that are prone to manipulate large volumes of data for a more accessible organization with transfer and cross-platform interpretability. 

Businesses can manipulate and convert CSV files in different ways. They are not hierarchical or object-oriented. They have a structure that is easy to import, convert and export as per requirements. It makes CSV data loading into warehouses, like Redshift pretty significant, considering different businesses that are likely to deal with varying sets of data, dynamic or frequently updated. It will then need to load into its destination Warehouses for analysis and other insights. 


You can use any method to load your CSV files into Redshift. Some technical knowledge is used for manually loading data efficiently. It can be one method for quickly loading CSV data; however, for larger chunks of data, manual monitoring can be cumbersome.

To pitch in an automated Data Integration with Redshift, you can use Hevo. Hevo is a fully managed No-code Data Pipeline. It can help to stimulate an automated environment for data manipulation, transfer, and platform integration.


SIGN UP and let Hevo manage, load, and monitor your data efficiently. Hevo’s 14-day free trial can be a great bet to try out some premium integration features and see how they work for you.

Tell us about your experience with different methods to load CSV to Redshift in the comment section below.

Aman Sharma
Technical Content Writer, Hevo Data

Driven by a problem-solving approach and guided by analytical thinking, Aman loves to help data practitioners solve problems related to data integration and analysis through his extensively researched content pieces. He has experience writing bogs on data engineering and always keep himself updated about the latest trends in data industry.

No-code Data Pipeline for Amazon Redshift