Around 95 percent of organizations say their inability to manage and comprehend data holds them back. 

It's no wonder, then, that so many of these companies are loading their data into a single location like Amazon Redshift

Redshift uses SQL to analyze data sets so users can solve organizational problems and make more profitable business decisions. Keeping all your data in this data warehouse makes it easier to manage and generate business intelligence (BI) about everyday operations such as sales, marketing, and customer service. 

While Redshift is an ever-popular warehousing choice for successful organizations — Amazon claims it's the most widely-used cloud data warehouse in the world — moving data to the platform can be a challenge. Below, learn the best methods for loading data to Redshift and why remains the most effective option of all. 

Table of Contents is the ETL solution for moving data to Redshift with no code. Start your seven-day free trial today.

Read more: Amazon Redshift: Comprehensive Guide

Why Do You Need to Load Data to Redshift?

Taming big data is one of the biggest challenges for organizations in almost every sector. In 2020, the average person created 1.7 megabytes of data every second. So moving data from its various sources to a single location like Redshift makes sense. 

Amazon says Redshift processes data operations 10 times faster than other enterprise data warehouses. That's because it uses something called Advanced Query Accelerator (AQUAD), which allows users to uncover data insights and use those insights to improve all kinds of business-related tasks. Redshift, part of Amazon Web Services, contains Redshift clusters that comprise nodes and disk storage. 

Redshift generates real-time insights by running data sets through third-party BI tools like Zoho and Looker. Decision-makers can then analyze data patterns with Amazon Redshift data. Use cases for Redshift include identifying high-value customers and learning about market trends.

But how do you move data to Redshift in the first place? Here are five options:

1. Manually Load Data to Redshift 

Amazon's best practices for pushing data to Redshift suggest uploading data sources to an Amazon S3 bucket and then loading that data into tables using the Copy command. Unfortunately, this process is far more difficult than it sounds. It involves splitting data, verifying files, compressing files, using time-series tables, compute nodes, permissions, staging tables, and managing data types such as JSON and CSV. 

Despite Amazon's recommendations, most companies will find uploading data to S3 buckets an extremely challenging prospect, especially if they lack a large data engineering team. Pushing data to Redshift in this way will also consume computing resources and maybe even impact query performance or schemas. 

Read more: AWS Redshift vs Other Data Warehouses

2. Manually Build ETL Pipelines

Extract, Transform, Load (ETL) is a much easier way to load data to Redshift than the method above. It involves the creation of big data pipelines that extract data from sources, transform that data into the correct format and load it to the Redshift data warehouse. Once you load data into Redshift, you can perform analytics with various BI tools.

The problem with this method is similar to the Amazon S3 bucket one. It requires advanced coding and data engineering experience that many organizations lack. Plus, creating pipelines from scratch is a laborious process that can take weeks or even months. That's because you need to consider factors such as job scheduling, IAM roles, data files, metadata, concurrency, queues, massively parallel processing, and connecting databases. Get manual ETL wrong and you could also affect query performance. 

3. ETL Tools

ETL tools automate much of the work associated with manual pipeline-building. These programs extract, transform and load data to Redshift automatically in the background. ETL tools require little human intervention, making them worthwhile for companies without data engineering teams. 

There are lots of ETL tools on the market. Some are open-source, allowing you to modify the software to suit your data requirements. However, this approach requires lots of coding and programming. Also, developers of these programs don't always update them, which can cause security issues. Proprietary ETL tools, on the other hand, belong to the companies that create them. These platforms use a subscription-based or pay-as-you-go pricing model, but users typically get improved security, customer service, and more features.

Read more: Offload ETL from Redshift to

4. AWS Glue

Glue is Amazon's ETL tool. It aggregates data, transforms it, prepares it for analytics, and loads it to Redshift. Glue has a free tier with limited features, so you'll likely need to upgrade to the pay-monthly model if you want to load large data loads to Amazon's data warehouse. 

Even at the paid-for tier, many users will struggle with Glue. Surprisingly, the tool doesn't natively interact with Redshift at all. It only connects to it, similar to other databases, which might cause problems when scaling and processing database queries. Glue also runs on Scala and Python — two languages that demand advanced coding skills. 

Even Amazon recommends third-party ETL tools — called 'AWS partners' — when loading data to Redshift. These tools include Fivetran, Informatica, and Stitch.

5. AWS Partners

The third-party ETL tools recommended by Amazon for loading data to its Redshift database vary in price, features, and capabilities. 

Fivetran, for example, moves data to Amazon's warehouse via a native Redshift integrator that requires no code, making it far easier to use than Glue. However, Fivetran charges users for the data they use during the ETL process, which could be expensive for organizations with large data loads.

Informatica, on the other hand, requires an advanced programming skillset that, like Glue, makes moving data to Redshift difficult. Stitch also poses problems for organizations with large volumes of data because its developers have designed the program for light- and medium-duty ETL.

How Helps

Using is the easiest way to load data to Redshift. This ETL tool, like Fivetran, comes with a native Redshift connector that extracts, transforms, and loads large data sets without any code. However, unlike Fivetran, only charges users for each connector and not the amount of data they use, potentially saving organizations thousands of dollars. has a drag-and-drop interface that makes it simple to optimize data and generate BI. You can also use its no-code/low-code connectors for other data warehousing solutions such as Microsoft Azure, Snowflake, Oracle, and PostgreSQL, as well as databases, data lakes, customer relationship management (CRM) systems, SaaS applications, and more. The platform's Salesforce-to-Salesforce connector, for example, extracts Salesforce data, transforms it into the correct format, and then returns it to Salesforce.

Other features include tutorials, a powerful REST API, extensive customer support (email, telephone, live chat, etc.), and compliance with data security standards and governance frameworks such as GDPR extracts, transforms, and loads data into Amazon's cloud data warehouse via its native Redshift connector. Start your seven-day free trial.