Loading Data to Redshift: 4 Best Methods

• October 19th, 2019

Amazon Redshift is a petabyte-scale Cloud-based Data Warehouse service. It is optimized for datasets ranging from a hundred gigabytes to a petabyte can effectively analyze all your data by allowing you to leverage its seamless integration support for Business Intelligence tools Redshift offers a very flexible pay-as-you-use pricing model, which allows the customers to pay for the storage and the instance type they use. Increasingly, more and more businesses are choosing to adopt Redshift for their warehousing needs.

In this article, you will gain information about one of the key aspects of building your Redshift Data Warehouse: Loading Data to Redshift. You will also gain a holistic understanding of Amazon Redshift, its key features, and the different methods for loading Data to Redshift. Read along to find out in-depth information about Loading Data to Redshift.

Table of Contents

What is Amazon Redshift?

loading Data into Redshift: Redshift Logo| Hevo Data
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Redshift’s biggest advantage is its ability to run complex queries over millions of rows and return ultra quick results. The querying layer is implemented based on the PostgreSQL standard. This is made possible by Redshift’s massively parallel processing architecture which uses a collection of compute instances for storage and processing. Redshift enables the customers to choose among different types of instances according to their budget and whether they have a storage-intensive use case or a compute-intensive use case. Redshift’s dense compute instances have SSDs and the dense storage instances come with HDDs.

In Redshift’s massively parallel processing architecture, one of the instances is designated as a leader node. Other nodes are known as compute nodes and are responsible for actually executing the queries. Leader nodes handle the client communication, prepared query execution plans and assign work to the compute nodes according to the slices of data they handle.

Redshift can scale seamlessly by adding more nodes, upgrading nodes or both. The limit of Redshift scaling is fixed at 2PB of data. The latest generation of Redshift nodes is capable of reducing the scaling downtimes to a few minutes. Redshift offers a feature called concurrency scaling which can scale the instances automatically during high load times while adhering to the budget and resource limits predefined by customers.

Concurrency scaling is priced separately, but users are provided with a free hour of concurrent scaling for every 24 hours a Redshift cluster stays operational. Redshift Spectrum is another unique feature offered by AWS, which allows the customers to use only the processing capability of Redshift. The data, in this case, is stored in AWS S3 and not included as Redshift tables.

Key Features of Amazon Redshift

The key features of Amazon Redshift are as follows:

1) Massively Parallel Processing (MPP)

Massively Parallel Processing (MPP) is a distributed design approach in which the divide and conquer strategy is applied by several processors to large data jobs. A large processing job is broken down into smaller jobs which are then distributed among a cluster of Compute Nodes which perform functions parallelly. As a result, there is a considerable reduction in the amount of time Redshift requires to complete a single, massive job. Scalability is a crucial factor while designing a Data Pipeline and by using Automated tools like Hevo Data, you can send almost any amount of data and the application will scale efficiently. Live support is also available 24×7 to help you out according to your needs.

2) Fault Tolerance

Data Accessibility and Reliability are of paramount importance for any user of a database or a Data Warehouse. Amazon Redshift monitors its Clusters and Nodes around the clock. When any Node or Cluster fails, Amazon Redshift automatically replicates all data to healthy Nodes or Clusters.

3) Redshift ML

Amazon Redshift houses a functionality called Redshift ML that gives data analysts and database developers the ability to create, train and deploy Amazon SageMaker models using SQL seamlessly.

4) Column-Oriented Design

Amazon Redshift is a Column-oriented Data Warehouse. This makes it a simple and cost-effective solution for businesses to analyze all their data using their existing Business Intelligence tools. Amazon Redshift achieves optimum query performance and efficient storage by leveraging Massively Parallel Processing (MPP), Columnar Data Storage, along with efficient and targeted Data Compression Encoding schemes.

Explore Numerous Ways to Set Up Loading Data to Redshift

Method 1: Loading Data to Redshift using the Copy Command

The Redshift Copy Command is one of the most popular ways that helps in loading data To Redshift with the support of data formats such as CSV, JSON, AVRO, etc. This method makes use of DynamoDB, S3, or the EMR cluster to facilitate the data load process and works well with bulk data loads.

Method 2: Seamlessly Loading Data to Redshift Using Hevo’s No-Code Data Pipeline

Hevo Data, an Automated No-code Data Pipeline, helps you load data to Amazon Redshift in real-time and provides you with a hassle-free experience. You can easily ingest data using Hevo’s Data Pipelines and replicate it to your Redshift warehouse without writing a single line of code.

Get Started with Hevo for Free

Hevo supports direct integrations of 100+ sources (including 40+ free sources) and its Data Mapping feature works continuously to replicate your data to Redshift and builds a single source of truth for your business. Hevo takes full charge of the data transfer process, allowing you to focus your resources and time on other key business activities.

Experience an entirely automated hassle-free process of loading data to Redshift. Try our 14-day full access free trial today!

Method 3: Loading Data to Redshift using the Insert Into Command

This method makes use of the Redshift Insert Into Command. The Insert Into Command is based on PostgreSQL and allows users to insert single or multiple records of data into their Redshift tables.

Method 4: Loading Data to Redshift using AWS Services

Using various utilities provided by Amazon Web Service for loading to Redshift is one such way. AWS supports bringing in data from a variety of sources and transferring it, either by using the AWS data pipeline or AWS Glue.

Methods for Loading Data to Redshift

There are multiple ways of loading data to Redshift from various sources. On a broad level, data loading mechanisms to Redshift can be categorized into the below methods:

Method 1: Loading Data to Redshift using the Copy Command

The Redshift COPY command is the standard way of loading bulk data TO Redshift. COPY command can use the following sources for loading data.

Other than specifying the locations of the files from where data has to be fetched, the COPY command can also use manifest files which have a list of file locations. It is recommended to use this approach since the COPY command supports the parallel operation and copying a list of small files will be faster than copying a large file. This is because, while loading data from multiple files, the workload is distributed among the nodes in the cluster. 

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

COPY command accepts several input file formats including CSV, JSON, AVRO, etc.

It is possible to provide a column mapping file to configure which columns in the input files get written to specific Redshift columns.

COPY command also has configurations to simple implicit data conversions. If nothing is specified the data types are converted automatically to Redshift target tables’ data type.

The simplest COPY command for loading data from an S3 location to a Redshift target table named product_tgt1 will be as follows. A redshift table should be created beforehand for this to work.

copy product_tgt1

from 's3://productdata/product_tgt/product_tgt1.txt'

iam_role 'arn:aws:iam::<aws-account-id>:role/<role-name>'

region 'us-east-2';

Method 2: Loading Data to Redshift Using Hevo’s no code data pipeline

Loading Data To Redshift: Hevo Data Pipeline| Hevo Data
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Hevo’s Automated No Code Data Pipeline can help you move data from 100+ sources swiftly to Amazon Redshift. You can set up the Redshift Destination on the fly, as part of the Pipeline creation process, or independently. The ingested data is first staged in Hevo’s S3 bucket before it is batched and loaded to the Amazon Redshift Destination. Hevo can also be used to perform smooth transitions to Redshift such as DynamoDB load data from Redshift and to load data from S3 to Redshift.

Hevo’s fault-tolerant architecture will enrich and transform your data in a secure and consistent manner and load it to Redshift without any assistance from your side. You can entrust us with your data transfer process by both ETL and ELT processes to Redshift and enjoy a hassle-free experience.

Hevo Data focuses on two simple steps to get you started:

Step 1: Authenticate Source

  • Connect Hevo Data with your desired data source in just a few clicks. You can choose from a variety of sources such as MongoDB, JIRA, Salesforce, Zendesk, Marketo, Google Analytics, Google Drive, etc., and a lot more.

Step 2: Configure Amazon Redshift as the Destination

You can carry out the following steps to configure Amazon Redshift as a Destination in Hevo:

  • Click on the “DESTINATIONS” option in the Asset Palette.
  • Click the “+ CREATE” option in the Destinations List View.
  • On the Add Destination page, select the Amazon Redshift option.
  • In the Configure your Amazon Redshift Destination page, specify the following: Destination Name, Database Cluster Identifier, Database Port, Database User, Database Password, Database Name, Database Schema.
  • Click the Test Connection option to test connectivity with the Amazon Redshift warehouse.
  • After the is successful, click the “SAVE DESTINATION” button.
Loading Data to Redshift: Hevo Destination| Hevo Data
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Here are more reasons to try Hevo:

  • Integrations: Hevo’s fault-tolerant Data Pipeline offers you a secure option to unify data from 100+ sources (including 40+ free sources) and store it in Redshift or any other Data Warehouse of your choice. This way you can focus more on your key business activities and let Hevo take full charge of the Data Transfer process.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to your Redshift schema.
  • Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

With continuous Real-Time data movement, Hevo allows you to assemble data from multiple data sources and seamlessly load it to Redshift with a no-code, easy-to-setup interface. Try our 14-day full-feature access free trial!

Get Started with Hevo for Free

Method 3: Loading Data to Redshift using Insert Into Command

Redshift’s INSERT INTO command is implemented based on the PostgreSQL. The simplest example of the INSERT INTO command for inserting four values into a table named employee_records is as follows. 

INSERT INTO employee_records(emp_id,department,designation,category) 

It can perform insertions based on the following input records.

  • The above code snippet is an example of inserting single row input records with column names specified with the command. This means the column values have to be in the same order as the provided column names. 
  • An alternative to this command is the single row input record without specifying column names. In this case, the column values are always inserted into the first n columns.  
  • INSERT INTO command also supports multi-row inserts. The column values are provided with a list of records. 
  • This command can also be used to insert rows based on a query. In that case, the query should return the values to be inserted into the exact columns in the same order specified in the command. 

Even though the INSERT INTO command is very flexible, it can lead to surprising errors because of the implicit data type conversions. This command is also not suitable for the bulk insert of data.

Method 4: Loading Data to Redshift using AWS Services

AWS provides a set of utilities for loading data To Redshift from different sources. AWS Glue and AWS Data pipeline are two of the easiest to use services for loading data from AWS table.

AWS Data Pipeline

Loading Data to Redshift: AWS Data Pipeline| Hevo Data
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AWS data pipeline is a web service that offers extraction, transformation, and loading of data as a service. The power of the AWS data pipeline comes from Amazon’s elastic map-reduce platform. This relieves the users of the headache to implement a complex ETL framework and helps them focus on the actual business logic. To have a comprehensive knowledge of AWS Data Pipeline, you can also visit here.

AWS Data pipeline offers a template activity called RedshiftCopyActivity that can be used to copy data from different kinds of sources to Redshift. RedshiftCopyActivity helps to copy data from the following sources.

RedshiftCopyActivity has different insert modes – KEEP EXISTING, OVERWRITE EXISTING, TRUNCATE, APPEND. 

KEEP EXISTING and OVERWRITE EXISTING considers the primary key and sort keys of Redshift and allows users to control whether to overwrite or keep the current rows if rows with the same primary keys are detected. 

AWS Glue

Loading Data to Redshift: AWS Glue| | Hevo Data
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AWS Glue is an ETL tool offered as a service by Amazon that uses an elastic spark backend to execute the jobs. Glue has the ability to discover new data whenever they come to the AWS ecosystem and store the metadata in catalogue tables. You can explore in detail the importance of AWS Glue from here.

Internally Glue uses the COPY and UNLOAD command to accomplish copying data to Redshift. For executing a copying operation, users need to write a glue script in its own domain-specific language. 

Glue works based on dynamic frames. Before executing the copy activity, users need to create a dynamic frame from the data source. Assuming data is present in S3, this is done as follows.

connection_options = {"paths": [ "s3://product_data/products_1", "s3://product_data/products_2"]}

df = glueContext.create_dynamic_frame_from_options("s3_source", connection-options)

The above command creates a dynamic frame from two S3 locations. This dynamic frame can then be used to execute a copy operation as follows.

connection_options = {

    "dbtable": "redshift-target-table",

    "database": "redshift-target-database",

    "aws_iam_role": "arn:aws:iam::account-id:role/role-name"



    frame = s3_source, 

    catalog_connection = "redshift-connection-name", 

    connection_options = connection-options, 

    redshift_tmp_dir = args["TempDir"])

The above method of writing custom scripts may seem a bit overwhelming at first. Glue can also auto-generate these scripts based on a web UI if the above configurations are known.

Benefits of Loading Data to Redshift

Some of the benefits of loading data to Redshift are as follows:

1) It offers significant Query Speed Upgrades

Amazon’s Massively Parallel Processing allows BI tools that use the Redshift connector to process multiple queries across multiple nodes at the same time, reducing workloads.

2) It focuses on Ease of use and Accessibility

MySQL (and other SQL-based systems) continue to be one of the most popular and user-friendly database management interfaces. Its simple query-based system facilitates platform adoption and acclimation. Instead of creating a completely new interface that would require significant resources and time to learn, Amazon chose to create a platform that works similarly to MySQL, and it has worked extremely well.

3) It provides fast Scaling with few Complications

Redshift is a cloud-based application that is hosted directly on Amazon Web Services, the company’s existing cloud infrastructure. One of the most significant advantages this provides Redshift is a scalable architecture that can scale in seconds to meet changing storage requirements.

4) It keeps Costs relatively Low

Amazon Web Services bills itself as a low-cost solution for businesses of all sizes. In line with the company’s positioning, Redshift offers a similar pricing model that provides greater flexibility while enabling businesses to keep a closer eye on their data warehousing costs. This pricing capability stems from the company’s cloud infrastructure and its ability to keep workloads to a minimum on the majority of nodes.

5) It gives you Robust Security Tools

Massive data sets frequently contain sensitive data, and even if they do not, they contain critical information about their organisations. Redshift provides a variety of encryption and security tools to make warehouse security even easier.

These all features make Redshift one of the best Data Warehouses to securely and efficiently load data in. A No-Code Data Pipeline such as Hevo Data provides you with a smooth and hassle-free process for loading data to Redshift.


The above sections detail different ways of copying data to Redshift. The first two methods of COPY and INSERT INTO command use Redshift’s native ability, while the last two methods build abstraction layers over the native methods. Other than this, it is also possible to build custom ETL tools based on the Redshift native functionality. AWS’s own services have some limitations when it comes to data sources outside the AWS ecosystem. All of this comes at the cost of time and precious engineering resources.

Hevo Data, a No-code Data Pipeline provides you with a consistent and reliable solution to manage data transfer between a variety of sources and a wide variety of Desired Destinations with a few clicks.

Visit our Website to Explore Hevo

Hevo Data with its strong integration with 100+ Data Sources (including 40+ Free Sources) allows you to not only export data from your desired data sources & load it to the destination of your choice such as Amazon Redshift but also transform & enrich your data to make it analysis-ready. Hevo also allows integrating data from non-native sources using Hevo’s in-built REST API & Webhooks Connector. You can then focus on your key business needs and perform insightful analysis using BI tools. 

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You may also have a look at the amazing price, which will assist you in selecting the best plan for your requirements.

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