12 Best Redshift ETL Tools to Consider in 2025

Struggling to compare Redshift ETL options?

Moving data from dozens of apps, databases, and platforms into a single source of truth doesn’t come easy.

Enter Redshift ETL tools! These tools ensure that raw, scattered data is ingested into Amazon Redshift, transformed for analysis, and delivered in a way that enhances modern data infrastructure.

In this article, we’ll compare the top 12 Redshift ETL tools across their key features, pricing models, pros, and cons, so that you can pick the right tool for your operational needs.

What are Redshift ETL Tools?

Redshift ETL tools are platforms designed to extract data from multiple sources, transform it into a usable format, and load it into the Amazon Redshift data warehouse. These tools centralize data to support faster analytics and operational strategies.

The features that accelerate workflows are:

• Multiple source connectivity: Creates a centralized data repository by consolidating data from databases, SaaS platforms, APIs, and files directly into Redshift.
• Data transformation: Clean, normalize, and enrich incoming data before loading to make it ready for analysis. Teams can generate accurate insights without manual preprocessing.
• Sync: Schedule incremental or full data loads to keep Redshift continuously updated.
• Schema handling: Redshift ETL tools automatically handle changes in source data structures, minimizing pipeline failures and operational overhead. Businesses can adapt to evolving data sources without disrupting workflows.
• Monitoring: Comprehensive monitoring tracks pipeline performance and alerts teams about errors. This guarantees reliable, uninterrupted data flows and reduces downtime.
• Scalability: ETL tools handle large data volumes while optimizing Redshift performance to support high-speed reporting at scale.

Top 12 Redshift ETL Tools

Here’s a list of the top 12 Redshift ETL tools:

1. Hevo Data

Hevo Data is a no-code data pipeline platform designed to automate the extraction, transformation, and loading (ETL) of data into Amazon Redshift and other cloud data warehouses.

Hevo extracts data from DBs, SaaS apps, APIs, and files, then transforms it in real-time with built-in cleansing and enrichment. It loads clean, structured data directly into Amazon Redshift, handling schema changes and incremental updates. Ideal for BI teams to centralize analysis-ready data without heavy engineering effort.

Hevo’s strength lies in its simplicity for Redshift pipelines, combining fast setup with seamless scaling. Its end-to-end governance empowers teams to focus on insights rather than infrastructure, making it ideal for organizations prioritizing speed and accuracy.

Key features:

• Plug-and-play connectors: Hevo provides 150+ pre-built, plug-and-play connectors that integrate SaaS apps, databases, cloud storage, and APIs directly with Redshift.
• Zero-code setup: Eliminates coding complexity by offering a no-code setup for Redshift ETL pipelines. With an intuitive interface, teams can move data into Redshift within minutes.
• Visual transformation builder: The transformation builder enables you to cleanse, deduplicate, and enrich data before it reaches Redshift.
• CDC support: Hevo supports change data capture (CDC) for transactional systems, enabling sub-second syncs into Redshift.

Pros:

• Offers a no-code interface.
• Multi-region and multi-workspace support for global teams.
• Strong observability with logs, alerts, and latency tracking.

Cons:

• Limited flexibility for complex integration workflows.
• Cloud-first platform with minimal on-premise support.

Pricing:

• Starts as a free tier with limited connectors up to 1 million events
• Starter: $239/month up to 5 million events
• Professional: $679/month up to 20 million events
• Business: Custom pricing

hevo data provide no code usage for building pipeline. the interface for building pipeline is drag and drop which is easier to implemention. after getting addicted to the tool im frequency use of hevo data is becoming more and more day by day. There are more number of feature such as pipes ,data flows the data base ingration hevo data is very fast.. infact i would say replication data is speed enough

fayaz a.

Data Engineer

2. Fivetran

Fivetran is a fully managed, cloud-native data integration platform designed to automate the movement of data from various sources into destinations like Amazon Redshift. It specializes in ELT/ELT workflows, making it ideal for simplified data operations.

Fivetran continuously extracts data from multiple sources, and its architecture ensures transformations can be applied within Redshift. This workflow automates pipelines while giving analysts access to clean, structured data for immediate insights.

Fivetran’s uniqueness lies in its deep Redshift integration, which optimizes performance for large-scale data loads and complex queries. Its intelligent schema handling keeps pipelines stable even as sources evolve. Combined with real-time incremental syncing, it delivers accurate and analysis-ready data.

Key features:

• Deployment models: Supports Redshift provisioned clusters and Redshift Serverless; lets you choose between connecting via master user or limited user with appropriate permissions.
• Distribution key: Fivetran automatically infers primary keys, foreign keys, sort keys, and distribution (dist) keys when loading data into Redshift.
• Row filtering: With row filtering, users can define specific conditions to control which rows are synced from the source into Redshift.

Pros:

• Instant setup with pre-built connectors.
• Anonymizes sensitive data to comply with GDPR.
• Automated schema changes adaptation.

Cons:

• MAR-based pricing can become unpredictable with large datasets.
• Limited customization for data transformations.
• Customer support lacks responsiveness.

Pricing:

Fivetran’s pricing is based on MAR (Monthly Active Rows), calculated by the number of unique rows inserted, updated, or deleted each month. Explore the platform with a 14-day free trial.

3. Stitch Data

Stitch Data is a cloud-native, open-source ETL platform designed to help developers and data teams replicate data from various sources into Amazon Redshift. It excels in rapid, scalable data integration.

Stitch orchestrates an end-to-end ETL workflow by continuously extracting data from heterogeneous sources, while handling incremental updates to reduce load and latency. It standardizes and structures the data into a schema-compatible format before loading it into Amazon Redshift.

Stitch’s integration with Amazon Redshift enables seamless data replication, ensuring that data is consistently updated and available for analysis. Its user-friendly interface and robust connector library make it an attractive option for teams aiming to streamline their ETL workflows.

Key features:

• Replication engine: Stitch supports 140+ data sources and automatically adapts to schema changes during replication, keeping Redshift pipelines functional even when source structures evolve.
• Field-level sync: Users can select specific fields and tables to replicate and configure sync schedules per source, optimizing Redshift storage.
• Integration support: Beyond built-in connectors, Stitch brings in data via its Import API or Webhooks to push source data (e.g., CSVs, event webhooks) into your Redshift pipelines.

Pros:

• Easy integration with Amazon Redshift.
• Reliable for small to medium data volumes.
• Backed by the Talend ecosystem for credibility.

Cons:

• Limited transformation capabilities.
• Connectors lack real-time sync speed.
• Minimal support for advanced orchestration.

Pricing:

Stitch follows a usage-based pricing model. The core of its pricing is the number of rows of data you process and transfer each month.

• Standard: $100 monthly
• Advanced: $1,250 monthly
• Premium: $2,500 monthly

4. AWS Glue

AWS Glue is a fully managed, serverless ETL service offered by Amazon Web Services. It is best suited for organizations already invested in the AWS ecosystem, looking to build scalable, flexible pipelines that move data into Amazon Redshift.

AWS Glue connects to diverse sources like S3, RDS, or on-prem databases, discovers schemas with crawlers, and transforms data using Spark or Python jobs before loading it into Redshift. With its serverless architecture, teams can automate pipelines that integrate smoothly with Redshift.

AWS Glue stands out for its native integration with Amazon Redshift, eliminating the need for complex configurations or third-party connectors. It leverages Redshift COPY commands and IAM-based security to streamline secure, high-speed data loading.

Key features:

• Spark-based engine: For Redshift ETL, the underlying Spark engine handles complex transformation and scales automatically depending on data size.
• Partition management: When moving data into Redshift, Glue’s crawlers can selectively load only relevant partitions, cutting down unnecessary data ingestion and optimizing query performance.
• Incremental loads (Job bookmarks): Glue tracks previously processed data, so only new or updated records are loaded into Redshift. Critical for reducing load times and keeping Redshift tables fresh without reprocessing.

Pros:

• Auto-generates ETL code using crawlers.
• Pay-as-you-go pricing with scalability.
• Deep integration with the Amazon Redshift ecosystem.

Cons:

• Steeper learning curve for non-technical users.
• Debugging and monitoring can be complex.
• Limited support for non-AWS data sources.

Pricing:

AWS Glue enables serverless ETL operations with a consumption-based pricing model.

5. Airbyte

Airbyte is an open-source data integration platform designed to connect hundreds of source systems (APIs, databases, files, SaaS apps) and sync data into destinations like Amazon Redshift.

Airbyte stages the extracted data in Amazon S3 before loading it into Redshift using the COPY command, ensuring high-performance ingestion. Simultaneously, it supports incremental syncs, schema changes, and optional in-pipeline transformations, so teams operate with accurate data.

Airbyte’s biggest differentiator is its extensible connector framework. Unlike closed platforms, it enables connector modification using the Connector Development Kit (CDK). Particularly useful for Redshift users with niche sources, it ensures precise and customizable data flows.

Key features:

• Normalization: Airbyte integrates directly with dbt, which orchestrates transformations in SQL right inside Redshift.
• Stream-level partitioning: Airbyte can break down data streams and run parallel syncs, improving throughput when moving high-volume datasets into Redshift.
• Deduplication: Provides deduplication and validation mechanisms during syncs, ensuring Redshift receives clean, consistent records. This avoids query inefficiencies and unreliable reporting downstream.

Pros:

• Facilitates real-time data synchronization into Redshift.
• Enables tailored solutions and community-driven development.
• Integrates AI for smarter data handling.

Cons:

• Requires external tools like dbt for complex transformations.
• User experience may vary across connectors.
• Demands ongoing management.

Pricing:

Airbyte provides a free self-hosted option, a 14-day cloud trial, and scalable Team and Enterprise plans designed to fit diverse business requirements.

6. Talend

Talend is an enterprise-grade data integration platform built to handle complex ETL and ELT workflows. It is well-suited to connect diverse data sources, enforce data quality, and move trusted, analysis-ready data seamlessly into Amazon Redshift, including Redshift Serverless.

Talend streamlines the Redshift ETL process by pulling data from DBs and SaaS apps, transforming it with built-in governance rules, and loading it into Redshift. Its workflow automates staging, schema management, and performance tuning for faster query readiness.

Talend embeds validation, cleansing, and governance directly into the Redshift pipeline. This ensures that every dataset entering Redshift is not just fast-loaded, but also accurate, trusted, and compliant, which is critical for enterprises handling sensitive or large-scale data.

Key features:

• Redshift-native support: Talend offers pre-built Redshift connectors and components, allowing direct extraction, loading, and querying within Redshift clusters.
• SQL templates: Users can design SQL-based transformations that run natively in Redshift. These transformations scale with Redshift’s compute nodes, ensuring faster execution on large data volumes.
• Resource management: Talend integrates with Redshift’s WLM (Workload Management) settings to optimize how ETL jobs consume cluster resources.

Pros:

• Scalable for large datasets and complex pipelines.
• Strong data transformation and cleansing capabilities.
• Integration with cloud and on-prem Redshift environments.

Cons:

• Enterprise features require a higher-cost subscription.
• Steeper learning curve for non-technical users.
• Performance can be slower for large Redshift loads.

Pricing:

Has a consumption-based pricing model determined by data volume, job executions, and duration.

7. Integrate.io

Integrate.io is a fully managed, cloud-based data integration platform built to simplify ETL, ELT, and CDC workflows. It stands out for its no-code interface and 140+ prebuilt connectors, making it ideal for moving data into Redshift while balancing both analytical and operational workloads.

Integrate.io extracts data from multiple sources, applies transformations such as filtering, joining, and schema mapping, and loads it into Redshift. Its workflow supports scheduling, automation, and pipeline monitoring, helping BI users access reliable data without heavy engineering effort.

Integrate.io’s standout feature is its flexible workflow orchestration. Users can visually design pipelines, define task dependencies, and schedule automated data flows, ensuring that Redshift always receives up-to-date, error-free data.

Key features:

• COPY-based loading: When writing data into Redshift, Integrate.io uses an intermediary S3 staging step and then leverages Redshift’s COPY command to load data efficiently.
• Schema evolution: The Redshift destination component can automatically create target tables if they don’t exist, and add missing columns when required.
• Destination control: You can configure destination settings like schema, table, default schema, and whether to auto-create tables or fail when missing.

Pros:

• Simplifies pipeline creation with drag-and-drop functionality.
• Meets standards like GDPR, HIPAA, and SOC 2.
• Provides detailed pipeline monitoring with customizable alerts.

Cons:

• Complex transformations may require additional tools.
• Some users report initial setup challenges.
• The pricing structure lacks clarity.

Pricing:

At $1,999 per month, this plan offers full access to the platform, 60-second pipeline updates, and unlimited connectors, with flexible options to customize and add additional features.

8. Matillion

Matillion ETL for Amazon Redshift is a cloud-native, browser-based data integration and transformation platform designed specifically for Amazon Redshift. It excels in extracting, transforming, and loading data to Redshift from various sources.

Matillion simplifies Redshift ETL by orchestrating jobs to extract data from SaaS apps, databases, and files, and apply transformations using Redshift’s compute power. Data engineers can design these workflows visually, schedule incremental or full loads, and monitor pipelines in real-time.

Matillion stands out for its intuitive visual job orchestration, which allows users to design complex ETL workflows through a drag-and-drop interface. By simplifying pipeline creation and monitoring, teams can deploy and scale Redshift workflows faster with fewer errors.

Key features:

• Incremental load wizards: Matillion offers built-in wizards and shared jobs to set up incremental ingestion. You can configure “high water mark” logic so that only changed/new records load after the initial full load.
• Redshift spectrum integration: For hybrid lakehouse setups, Matillion can query external data in S3 via Redshift Spectrum and blend it with warehouse data.
• Granular control: Matillion provides advanced load settings, like compression options and staging file management, that give engineers precise control over Redshift workflows.

Pros:

• Automatically adjusts to handle increasing data volumes.
• Supports SQL and Python scripting for complex data processing.
• Optimized for cloud data warehouses.

Cons:

• Consumption-based pricing becomes expensive at scale.
• Limited support for long-tail connectors.
• Users face API limitations in Matillion.

Pricing:

The platform offers a pay-as-you-go model.

9. Informatica PowerCenter

Informatica PowerCenter is an enterprise-grade ETL platform that connects natively with Redshift architecture through built-in tools. It enables enterprises to extract data from diverse on-premise and cloud systems, transform it, and load it into Redshift at scale.

Informatica PowerCenter connects to Amazon Redshift using its PowerExchange adapter. Data teams can extract data from diverse systems, transform it through reusable mappings, and load it into Redshift using sessions to support pushdown optimization for performance.

The platform stands out for its PowerExchange for Amazon Redshift with pushdown optimization. PowerCenter can push complex transformations directly into Redshift’s compute engine.

Key features:

• Redshift-optimized connector: PowerCenter’s Redshift connector supports bulk loading with Amazon’s native COPY command, parallel file transfer to S3, and optimized write paths, facilitating high-throughput data loads.
• Parallel processing: Data pipelines can be split into partitions that run in parallel for faster processing and efficient utilization of Redshift’s cluster resources.
• Data quality: PowerCenter integrates with Informatica Data Quality tools to profile, standardize, and validate data before Redshift ingestion.

Pros:

• Robust support for complex ETL workflows.
• Extensive connectivity to databases and applications.
• Strong data quality and transformation capabilities.

Cons:

• High licensing and operational costs.
• Limited real-time or sub-second replication support.
• Slower deployment compared to cloud-native tools.

Pricing:

Informatica runs on a consumption-based pricing model, billing you for what you use.

10. IBM Infosphere DataStage

IBM InfoSphere DataStage is an enterprise-grade ETL/ELT platform designed to handle complex, large-scale data integration. With its native Amazon Redshift connector, it enables high-performance data extraction, transformation, and loading while supporting both batch and real-time pipelines.

With DataStage, teams can design ETL jobs to extract data from diverse sources, apply transformations in parallel or push them down to Redshift, and load the results into target tables. Its metadata import ensures schema awareness, while job orchestration, monitoring, and lineage tracking provide end-to-end reliability.

Additionally, it offers deep integration with hybrid and multi-cloud environments, allowing enterprises to move workloads between on-premise systems and Amazon Redshift with minimal friction. Its AI-driven data quality and profiling features help ensure that only clean data resides in Redshift.

Key features:

• Metadata management: The platform integrates with IBM InfoSphere’s metadata repository to provide lineage tracking, quality management, and governance. You can trace data flows across pipelines, monitor schema changes, and ensure consistency.
• Reusable components: Offers a library of prebuilt transformation functions and reusable job templates. Developers can reuse logic across pipelines for consistency and faster development.
• Advanced transformation: The platform supports complex transformations, joins, aggregations, and lookups that can be applied within Redshift to optimize warehouse performance.

Pros:

• Supports on-premise, virtualized, and containerized deployments.
• Optimizes large data volumes with parallel processing.
• Offers comprehensive data lineage and governance.

Cons:

• Limited SaaS connectivity.
• Complex architecture requires significant expertise.
• UI is less intuitive compared to modern ETL platforms.

Pricing:

Offers a pay-as-you-go model.

11. Apache Kafka (Apache ETL)

Apache Kafka is a distributed streaming platform for handling real-time data pipelines and event-driven architectures. In the Redshift ETL context, it’s ideal for businesses that need continuous, high-throughput data streaming into Redshift from diverse systems.

Apache Kafka works by capturing event streams from multiple sources, organizing them into topics, and delivering them in real time to Amazon Redshift via Kafka Connect. Data engineers can load fresh, high-velocity data into Redshift without manual intervention.

Apache Kafka’s uniqueness lies in its ability to decouple data producers and consumers while still feeding Redshift reliably. This means organizations can integrate multiple streaming sources into a single pipeline without putting extra load on Redshift.

Key features:

• Kafka schema registry: Kafka’s schema registry maintains a consistent data format across producers and consumers. It helps maintain clean, analysis-ready data without manual interventions.
• Event-driven architecture: Kafka can trigger downstream processes in response to specific events, making ETL pipelines reactive. You can ingest only the relevant changes instead of full data loads.
• Data transfer: Supports encryption (SSL/TLS) and authentication (SASL), protecting data in transit. Redshift pipelines benefit from secure ingestion, meeting enterprise security and compliance requirements.

Pros:

• High-throughput streaming for real-time data ingestion.
• Supports scalable, distributed architecture for large datasets.
• Durable, fault-tolerant messaging ensures data reliability.

Cons:

• Limited built-in transformations; needs external processing.
• Monitoring and troubleshooting can be complex.
• Requires additional connectors or ETL layers.

Pricing:

Usage-based pricing; it depends on your deployment model.

12. Rivery

Rivery (acquired by Boomi) is a cloud-native, fully managed ELT/ETL platform built to simplify complex data workflows. It excels at helping organizations quickly ingest and transform data from hundreds of sources into Amazon Redshift without heavy engineering effort.

Rivery applies transformations either in Rivery’s cloud engine or pushes them down into Redshift for optimized performance and minimal latency. The platform offers automated pipelines that help BI teams maintain reliable and analysis-ready data without writing complex code.

Rivery excels in combining no-code pipeline building with Redshift-optimized ELT. Users can design and monitor complex workflows visually while leveraging Redshift’s processing power for transformations.

Key features:

• Custom scripts: Users can embed SQL or Python scripts directly into ETL pipelines for complex transformations or business logic.
• Parallel processing: Rivery can execute multiple pipelines and transformations in parallel, leveraging Redshift’s processing power.
• Cross-region support: Rivery can integrate sources across different cloud regions or providers and centralize them into Redshift. Businesses with distributed data environments maintain unified analytics without latency.

Pros:

• Python/SQL transformation support.
• Connect to niche or proprietary systems.
• Features pre-built data workflow templates.

Cons:

• Lacks advanced scheduling and error handling.
• May struggle with complex pipeline management.
• Cannot apply changes in real-time during ingestion.

Pricing:

Rivery employs a usage-based pricing model centered around Rivery Pricing Units (RPUs), where each RPU corresponds to a unit of platform usage. 

What Are the Key Factors in Selecting the Right ETL for Redshift?

Here is a list of factors to consider while selecting the correct ETL tool for your Redshift workflows:

1. Native integration

The ETL tool should have strong support for Amazon Redshift, including native connectors, COPY commands, and pushdown transformations. Native integration ensures faster data loads, reduces errors, and tackles ETL challenges.

2. Source coverage

A good Redshift ETL should support a wide range of data sources, databases, SaaS applications, APIs, and files. The broader the connector library, the easier it is to consolidate data from multiple platforms without building custom connectors.

3. Real-time vs batch processing

Consider whether your business needs real-time data pipelines or scheduled batch loads. Tools that support streaming or near-real-time ingestion empower dashboards to reflect the latest changes.

4. Ease of use

A visual, low-code/no-code interface is easy to operate among teams. A no-code interface reduces onboarding time, while advanced features like scripting, scheduling, and custom transformations offer operational flexibility.

5. Scalability

Your ETL should handle growing data volumes without slowing Redshift or inflating costs. Look for tools that optimize data processing, support parallel loads, and allow incremental updates.

6. Error handling

A robust tool should provide real-time monitoring, automated alerts, retry mechanisms, and comprehensive logs to quickly identify and fix issues.

7. Transparent pricing

The selected tool must clearly outline costs based on factors like data volume, number of pipelines, or connectors. Straightforward pricing avoids hidden fees and scales pipelines without additional costs, making ROI evaluation easier.

8. Security & compliance

ETL tools should support encryption, role-based access, and compliance with standards like GDPR or HIPAA. Security features are critical when transferring sensitive data into Redshift.

Simplify Your Redshift ETL with Hevo

While there are many Redshift ETL tools, Hevo stands out with its fully managed, no-code platform that automates data ingestion, transformation, and loading directly into Redshift. 

With real-time syncing, Hevo keeps your Redshift warehouse clean and up-to-date. Teams can monitor pipelines, handle schema changes, and scale without engineering overhead.

Security and scalability are built in, with SOC 2 and GDPR compliance ensuring your data is protected. The platform effortlessly handles growing volumes without extra engineering overhead.

For SMBs and enterprises, Hevo simplifies complex ETL workflows, accelerates time-to-insight, and makes managing Redshift pipelines reliable and stress-free.

Sign up for Hevo’s 14-day free trial to fine-tune your Redshift workflows today!

Learn More

• 8 Best Redshift SQL Client Tools for 2025
• Redshift Dynamic SQL: Explained Step-by-Step
• Databricks vs Redshift: 6 Critical Differences

FAQs on ETL Tools for Redshift