Load CSV to Redshift: 3 Easy Methods

on Data Integration, ETL, Tutorials • October 22nd, 2021 • Write for Hevo

Are you trying to load CSV to Redshift? Have you looked all over the internet to find the most convenient method to do it? If yes, then you are in the right place. CSV format data is used as plain text files by a majority of businesses. CSV files are easier to handle, smaller in size, and offer a range of benefits; all while holding a standard format for representation. While the compact size and simple implementation of CSV files make them a suitable format for the organization and storage of data, there can be some challenges in accurately loading this data into a Data Warehouse. 

You will face common file reader issues while loading CSV files. There might be issues of character conversion, absolute NULL values, and errors from values that are incompatible across platforms. Although these challenges have specific fixes that can resolve these issues, there are some methods of loading CSV data that can avoid these issues entirely.

CSV files are often used with warehouses, like Amazon Redshift for easy handling and manipulation of data. Several organizations rely on the use of CSV files for storage optimization, standard representation, and other benefits. This article deals with methods of loading CSV files into Redshift and the challenges they pose. You will also explore more about Redshift and the nature of CSV files, and how the two can be used in tandem efficiently. 

Table of Contents

Introduction to Amazon Redshift 

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Amazon Redshift is a Data Warehouse product by Amazon Web Services that offers a fully managed, Cloud-based service. It is well known for its use with Business Intelligence tools for easy storage, organization, and analysis of business data. Redshift offers a seamless interface for data loading and makes it a popular choice for Business Analytics and Data-Keeping.

It offers some specific features that make it a better bet than the many warehouse options available today. Amazon Redshift uses Massively Parallel Processing (MPP) that enables parallel processing, making it up to three times faster than your typical Cloud Data Warehouse. It employs query optimization techniques for faster processing of queries that occurs frequently. Thus, Redshift offers faster data processing along with an efficient interface for data handling. 

Load CSV to Redshift: Amazon Redshift
Amazon Redshift Query Processing Technique
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Introduction to CSV Load

CSV files are data sets with comma-separated values that can be further saved within a tabular format.  It has a simple structure that developers can easily interpret, thus, adding great convenience. 

A typical CSV file would contain text such as:

Name,Email,Phone Number,Address
Bob Smith,bob@example.com,123-456-7890,123 Fake Street
Mike Jones,mike@example.com,098-765-4321,321 Fake Avenue

These have a simple structure and can contain any number of lines, entries, and long strings of text. CSV loading has proven to be an efficient way of loading data with fewer memory requirements and advanced cross-platform compatibility. CSV loading into Redshift enables the use of these datasets with optimized features of Amazon Redshift. 

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. 

3 Methods to Load CSV to Redshift

You can use any of the following methods to load CSV to Redshift:

Let’s discuss these methods in detail.

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.

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.

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: AWS Data Pipeline
Amazon Web Services Data Pipelining With 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.

Method 3: Load CSV to Redshift Using Hevo Data

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Hevo is a No-code Data Pipeline. Hevo can move CSV data with an automated mechanism to Redshift. It implements a simple configuration on both end connections. It eliminates the issue of compatibility by providing over 100 sources that link with Redshift for an easy data loading process.

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 Salesforce, MongoDB, Snowflake, 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.

Simplify your ETL process with Hevo today! 



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 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.

No-code Data Pipeline for Amazon Redshift