Top 7 AWS Redshift ETL Tools

Amazon Redshift is a fully managed data warehouse solution that allows you to efficiently analyze all your data using your existing business intelligence tools. While Amazon Redshift is one of the industry's top data storage solutions, many considerations need to be made before using AWS Redshift. One of the primary elements of any cloud-based storage solution is knowing how to transfer and secure data properly. Here, we will break down how to properly move data to and from the AWS Redshift platform.

Why Use AWS With ETLs?

Many businesses are now utilizing ETL operations to migrate their data as a result of cloud technologies. They frequently have RDBMS or old technology data storage, which is inefficient, inflexible, and vulnerable. As a result, organizations migrate to the cloud to gain greater performance, scalability, and fault-tolerant capabilities. ETLs are essential when transferring multiple data sources to a single data warehousing location.

AWS Redshift ETL tools are a vital part of any ETL process. ETL stands for Extract, Transform and Load — an ETL tool helps you extract data from one system, transform the data to meet the needs of your destination system, and load it into that system. There are various use cases for ETLs. Whether it is uploading data to snowflake, AWS, or Microsoft Azure, ETLs are essential. Here are nine of the best AWS Redshift ETL tools to help your business and cloud computing needs.

Best low-code ETL solutions for AWS Redshift data integration

Integrate.io, Talend, and Hevo Data are top low-code ETL solutions for AWS Redshift integration. Integrate.io offers native Redshift connectivity with a drag-and-drop interface, enabling users to ingest, transform, and load data from over 200 sources without writing code. It supports incremental loads, complex transformations, and real-time scheduling, making it ideal for teams that want fast, secure, and scalable Redshift pipelines with minimal engineering effort.

Let's dive deeper into each of the tools.

1. Integrate.io

Integrate.io is a cloud ETL platform that helps you move, transform, and load your data easily. Integrate.io's ETL for Amazon Web Services (AWS) allows users to connect directly to Amazon Redshift without an intermediary ETL server or appliance. This gives you the flexibility to take advantage of both on-premise workloads and public cloud resources using a single user interface. Integrate.io is at the top of the list of the best ETL tools to use with AWS Redshift.

Integrate.io offers customizable integrations into different systems through APIs, including Salesforce, Marketo, Zendesk, Google Analytics Premium/AdExchange, and Omniture SiteCatalyst. This makes it easy for businesses to transfer data from their existing toolsets into AWS Redshift efficiently & effectively. Integrate.io also enables simple-to-use workflows and data pipelines to optimize your entire data ecosystem.

G2 Rating: 4.3/5

Features:

• ETL, ELT, and reverse ETL support

• 200+ pre-built connectors including Salesforce, HubSpot, Google Ads

• Drag-and-drop interface with low-code transformations and API generation

Advantages:

• Easy setup with minimal technical expertise required

• Reliable pipelines with low maintenance overhead

• Excellent customer support

Limitations:

• Limited customization for complex transformations

• Performance bottlenecks on extremely large datasets (tens of millions of rows)

Pricing:

• Fixed fee, unlimited usage based pricing model.

2. AWS Glue

AWS Glue is an ETL tool that provides a unified interface, automation, and monitoring for ETL jobs. AWS Glue makes it easy to extract data from various sources, transform it into your desired format, and load it into your destination system.

AWS Glue, formerly known as AWS Stitch, is a serverless data integration service. It comprises an AWS Glue Data Catalog containing central metadata, an ETL engine that automatically generates Python or Scala code, and a flexible scheduler that handles dependency resolution, job monitoring, and retries. Because it's serverless, there's no need for any infrastructure to be set up or maintained.

G2 Rating: 4.3/5

Features:

• Serverless ETL built on Apache Spark

• AWS Data Catalog for schema discovery

• Built-in job scheduling and automated crawlers

Advantages:

• Fully serverless and scalable, integrated tightly with AWS services

• No infrastructure management required

• Cost-effective for infrequent ETL workloads

Limitations:

• Can become expensive with frequent or large-scale usage

• Steep learning curve, especially for non-AWS users

• Limited third-party connectors and ecosystem

Pricing:

• $0.44 per DPU-hour (compute billed per second)

• Crawlers and development endpoints have separate charges

3. Talend

Talend ETL supports various databases, including Redshift, MySQL, Oracle, Hadoop/Hive, and cloud storage solutions like Amazon SES and Dropbox. Talend ETL also enables users to create integrations with additional tools such as Alfresco ECM Suite.

Talend is an excellent tool for businesses of all sizes due to its various package options. It has plenty of integration options such as data integration, extensive data integration, and data preparation, making it an excellent ETL tool for AWS Redshift or any other cloud-based data storage solution.

G2 Rating: 4/5

Features:

• Drag-and-drop visual pipeline builder

• Data quality, data lineage, and governance modules

• Supports on-premises and cloud deployments

Advantages:

• Rich enterprise features with end-to-end data integration

• Open-source starter edition available

• Suitable for complex transformations with built-in data quality controls

Limitations:

• Advanced capabilities locked behind paid tiers

• Performance issues with large datasets

• UI can feel outdated compared to newer platforms

Pricing:

• Open-source version free

• Paid subscriptions based on number of users and features, quote-based

4. AWS Kinesis

Amazon Kinesis Data Streams enables you to capture and process massive amounts of data in real-time. You may build data-processing applications known as Kinesis Data Streams apps using Amazon Kinesis Data Streams. A standard Kinesis Data Streams application extracts data from a stream of data records.

AWS Kinesis allows you to load streaming data into your Redshift cluster. It works by reading the stream of events from Kinesis, performing any necessary transformation or enrichment on these records, and finally writing them into a destination table in Amazon SCTS.

AWS Kinesis ETL sends transformed event data directly to an Amazon ES domain — no ETL server required. This feature requires AWS Data Pipeline integration with AWS ETL and AWS Lambda functions, enabling managed execution across multiple services.

G2 Rating: 4.3/5

Features:

• Real-time data ingestion and streaming

• Modules include Data Streams, Firehose, Analytics, and Video Streams

• Low-latency stream processing at scale

Advantages:

• Scales horizontally to handle gigabytes per second

• Tight integration with AWS Lambda, S3, Redshift

• Managed service with minimal operational overhead

Limitations:

• Throughput quotas and shard limits

• Requires shard capacity planning for optimal costs

• Basic processing capabilities compared to full ETL platforms

Pricing:

• On-demand and provisioned capacity models

• Costs depend on data volume, PUT payload units, and retention

5. AWS Data Pipeline

AWS Data Pipeline is another great ETL tool for transferring data into your Redshift cluster. The service automates the ETL process by defining all of your ETL tasks, schedules these tasks to run at a specific time and date, and manages their execution across AWS services such as AWS Data Pipeline or Amazon ES.

AWS Data Pipeline enables you to transform streaming data from Kinesis Firehose using Lambda functions that automatically enrich incoming records with additional source metadata.

G2 Rating: 4.1/5

Features:

• Orchestrates batch data movement between AWS services and on-premises

• Supports complex dependency scheduling and retry logic

• Compatible with S3, DynamoDB, RDS, Redshift

Advantages:

• Fully managed, high availability across regions

• Ideal for scheduled batch processing

• Integrated security with AWS IAM

Limitations:

• Outdated interface with limited modern features

• Slower adoption, some AWS services have surpassed it (Glue, Step Functions)

• Limited monitoring and troubleshooting options

Pricing:

• $0.60 per low-frequency activity/month

• $1.00 per high-frequency activity/month

6. Hevo

Hevo is a data transformation ETL tool that lets you transform and load your data into the cloud with a few simple clicks. Hevo has a user-friendly interface and flexible configuration options, and it supports Redshift Spectrum queries and Amazon Athena for easy querying of your transformed SCTS tables.

Hevo is an excellent ETL tool because it takes only minutes to get started loading AWS Redshift from any source system, including Apache Flume, PostgreSQL Database, and Kinesis Firehose. This makes the ETL process faster while saving money on infrastructure costs at the same time.

This ETL tool also includes a one-click publishing feature that allows users to publish their transformed data in real-time directly into Amazon ES without syncing directories.

G2 Rating: 4.6/5

Features:

• Zero-code pipelines with real-time streaming

• 150+ connectors for SaaS, databases, and cloud storage

• Data transformation and activation features included

Advantages:

• Intuitive user interface for rapid deployment

• Real-time data sync with minimal latency

• Transparent pricing with flexible plans

Limitations:

• Limited advanced customization

• Expensive at higher data volumes

• Debugging and monitoring features are basic

Pricing:

• Free tier available

• Paid plans based on event volume, monthly or annual billing

7. Apache Spark

Apache Spark is one of the most popular ETL tools used today. It's a big data processing engine that enables you to ETL your Redshift data in real-time while transforming, enriching, and filtering it along the way.

Apache Spark includes an ETL tool known as Databrick, which is excellent for ETL-ing transformed SCTS into Apache Hive or Amazon EMR. It does this before loading them directly into AWS Redshift using one of its many integration options, including JDBC/ODBC drivers, Kinesis Firehose, and Talend ETL Studio.

Spark also has built-in libraries for ETL purposes such as SQL, data frames, and datasets — making this ETL tool easy to use with Python and Scala programming languages.

One of the most significant advantages to Apache Spark is that it performs in-memory computations based on the Hadoop MapReduce fundamentals. Due to its in-memory processing, it is 100 times faster than Hadoop MapReduce.

G2 Rating: 4.2/5

Features:

• Distributed in-memory data processing

• Supports batch and real-time streaming (Spark Streaming)

• Built-in libraries for SQL, machine learning (MLlib), and graph processing

Advantages:

• Extremely fast for large datasets, especially iterative algorithms

• Versatile for multiple use cases including ETL, ML, and real-time analytics

• Works with multiple languages (Scala, Python, Java, R)

Limitations:

• High memory consumption requires careful tuning

• No built-in data connectors, must be paired with other tools (e.g., Hadoop, Hive)

• Complex debugging and monitoring

Pricing:

• Open-source free

• Cloud-based versions (AWS EMR, Databricks) follow pay-per-use or subscription pricing

Comparison of Top AWS Redshift ETL Tools

How Integrate.io Can Help

Each of these ETL tools offers a unique way to ETL your data into AWS SCTS in either real-time or batch mode. The best part is that they all integrate seamlessly with Amazon ES, Amazon s3, and other Amazon software, making querying and analyzing ETL tables extremely fast and easy.

With Integrate.io leading the way due to its easy-to-use point-and-click interface and robust integrations, Integrate.io is an excellent option for safely transferring data to and from AWS Redshift. Whether looking to move data to a cloud data warehouse, a data lake, or simply load data to the cloud, Integrate.io is is the cost-effective solution for you. 

If you want to get started with Integrate.io, schedule a call with one of their team members today and receive a 7-day trial.

FAQs

Q1: I need recommendations for Redshift ETL tools for healthcare data.

Top tools include Integrate.io, a no-code platform with native Redshift connectors, a visual pipeline builder, encryption, and compliance features. AWS Glue offers serverless, schema-aware ETL with ML-based data cleansing and HIPAA-friendly configurations. Apache Spark on EMR is ideal for high-volume healthcare workloads needing large-scale transformation and parallel processing.

Q2: Efficient ETL platforms for AWS Redshift in the financial sector.

Recommended platforms include AWS Zero-ETL for real-time ingestion from Aurora or Kinesis into Redshift, which is ideal for transaction-heavy financial systems. Integrate.io supports fast, secure pipelines with native Redshift integration and financial data connectors. Integrate.io Data Integration provides cloud-ready, audit-friendly ETL optimized for Redshift with encryption and parallel execution.

Q3: How does Redshift handle transformations during ETL?

Redshift supports SQL-based transformations directly within the warehouse, but for complex logic or pre-processing, it's common to use AWS Glue or external tools to clean and shape the data before loading. ELT (Extract, Load, then Transform in Redshift) is often preferred due to performance and scalability.

Q4: Can I automate and monitor ETL workflows into Redshift?

Yes. You can automate pipelines using AWS Step Functions, Lambda triggers, or built-in schedulers in tools like Airflow and Integrate.io. Redshift also supports CloudWatch monitoring, Workload Management (WLM) queues, and Query Monitoring Rules to track performance and failures.