TL;DR Short answer: most Redshift ETL tools only load into Redshift, so the first filter is whether a tool can read from it at all. Fivetran and Stitch, the names on most lists, cannot. Among those that can, the method matters: pulling rows through JDBC routes everything through the leader node and competes with your queries, while UNLOAD writes in parallel from the compute nodes straight to S3. Nine tools that genuinely extract from Redshift:
Airbyte AWS Glue Matillion Hevo Data Boomi Data Integration Talend Informatica PowerCenter Meltano Apache Airflow One Redshift constraint: Redshift exposes no change log that external tools can read, so there is no log-based CDC out of it. Incremental extraction depends on an updated-at column, and deleted rows only disappear downstream if you use soft-delete flags or run periodic full refreshes.Redshift ETL tools move data out of Amazon Redshift into another warehouse, a lake, or the operational systems that need it. Moving data out is a different job from loading data in, and most products are built for the second, which is why this list is shorter than most.
Why move data out of Redshift? Amazon Redshift is AWS's columnar cloud data warehouse, built for large analytical scans with standard SQL. Data usually flows into it, but there are four common reasons to move it out: migrating to another warehouse such as Snowflake or BigQuery, sharing modelled data with a team or partner on a different platform, feeding operational tools and BI systems that cannot query Redshift directly, and archiving cold data to cheaper storage in S3.
How do the Redshift ETL tools compare? Tool How it reads Redshift Incremental Hosting Pricing basis Airbyte JDBC queries Cursor column Cloud or self-hosted Free self-hosted; Cloud volume-based; Pro and Flex capacity-based AWS Glue UNLOAD to an S3 temporary directory Job bookmarks AWS serverless Per DPU-hour Matillion Runs inside Redshift; S3 Unload component Your job logic Your AWS account Credits Hevo Data JDBC queries Incremental column Cloud Events Boomi Data Integration Redshift source connector Incremental column Cloud Credits Talend Redshift input and unload components Your job design Cloud or on-premises Quoted subscription Informatica PowerCenter PowerExchange, reading via UNLOAD Your mapping design On-premises Quoted licence Meltano Community Singer tap over JDBC Replication key Self-hosted Free, open source Apache Airflow RedshiftToS3Operator runs UNLOAD Whatever you write Self-hosted or MWAA Free, you run it
Which Redshift ETL tools should you consider? Every tool below can read from Redshift, not just write to it. Fivetran and Stitch are left out for that reason: both treat Redshift as a destination only, and Fivetran's own guidance for Redshift as a source is to build a custom connector with its Connector SDK.
1. Airbyte Airbyte is an open-source data integration platform with 700+ connectors and a community of 25,000+ data engineers. Its Redshift source reads tables over JDBC with full refresh or cursor-based incremental sync, and loads them into any Airbyte destination, including Snowflake, BigQuery, Databricks and S3, which makes it a straightforward route for a Redshift migration or an ongoing copy.
What's unique about Airbyte? Connectors are open source and editable, and the Connector Development Kit and no-code Connector Builder cover sources without a pre-built connector. If one stream fails you can resync it alone. Airbyte runs as self-hosted open source, on Airbyte Cloud, or as Enterprise Flex, where the data plane stays inside your own AWS account and records, credentials and keys never leave it.
Pros Cons Reads Redshift and loads into 700+ connectors' destinations Reads over JDBC rather than UNLOAD, so very large tables load the leader node Connectors are open source and editable Self-managed deployment needs Kubernetes or Docker skills Stream-level control lets you relaunch a single failed stream Transformation relies on dbt rather than being built in Enterprise Flex keeps data and keys inside your AWS account Flex pricing is custom and needs a sales conversation
2. AWS Glue AWS Glue is Amazon's serverless ETL service. Its Redshift connection reads by running UNLOAD to a temporary S3 directory, so large extractions run in parallel from the compute nodes rather than through a single JDBC stream. Job bookmarks track what has already been processed, and jobs are billed per DPU-hour. It is the natural choice when the destination is also inside AWS, such as S3, Athena or another Redshift cluster.
Pros Cons Reads via UNLOAD, the efficient path out of Redshift Few connectors for SaaS and non-AWS destinations Serverless, with no infrastructure to manage Spark-based jobs have a learning curve Job bookmarks handle incremental runs Debugging failed jobs can be slow Integrates with the Glue Data Catalog and IAM Ties the pipeline to AWS
3. Matillion Matillion's Data Productivity Cloud was built around Redshift and runs its transformations inside the warehouse by generating SQL. For moving data out, its S3 Unload component wraps Redshift's UNLOAD, so exports run in parallel to S3. It has 100+ connectors, dbt support since v1.70, and bills in credits tied to job runtime. It makes most sense if Matillion already runs your Redshift transformations.
Here is a closer look at Airbyte vs Matillion .
Pros Cons Built around Redshift, with pushdown transformation Around 100 connectors, fewer than the leaders S3 Unload component uses Redshift's parallel export Credit pricing tracks runtime and needs monitoring Strong visual transformation designer Multi-cloud estates may need more than one setup dbt support since v1.70 Most valuable only if you transform in Redshift already
4. Hevo Data Hevo Data is a no-code cloud ELT platform with 150+ connectors, including Redshift as a source. It loads incrementally on a timestamp or ID column and applies transformation, mapping and enrichment before data reaches the destination. It is cloud-only and billed on events, which suits teams without engineering capacity who do not need self-hosting.
Pros Cons Built-in transformation, mapping and enrichment before load Around 150 integrations, well short of the leaders Data activation syncs results back to source APIs Event-based pricing is hard to forecast at scale Short setup time with a no-code interface Cloud only, so no self-hosted or hybrid option Automatic schema mapping on incoming data Connector gaps cannot be filled by your own team
5. Boomi Data Integration (formerly Rivery) Boomi acquired Rivery in December 2024 and now sells it as Boomi Data Integration. It supports Redshift as a source as well as a destination, and bundles transformation, orchestration and reverse ETL in one cloud platform with 150+ connectors. Pricing is in credits that vary by connector, which can make cost hard to model.
Pros Cons Bundles ingestion, transformation and orchestration 150+ connectors, fewer than the leaders Data activation pushes results back to business tools Credit pricing varies by connector, making cost hard to model One platform rather than several to integrate Cloud only, with no self-hosted option Reasonable learning curve for analytics teams Roadmap now tied to Boomi's wider suite
6. Talend Talend pairs integration with data quality and governance, and has dedicated Redshift components for reading tables and for running UNLOAD to S3. Two things to check before shortlisting it: Qlik acquired Talend in 2023 and now sells it as Qlik Talend Cloud, and Talend Open Studio, the free open-source edition, was retired on 31 January 2024, so there is no free tier or self-serve route in.
Pros Cons Integration, quality and governance in one platform Implementation is involved and rarely self-serve Dedicated Redshift input and unload components No free tier since Open Studio was retired in 2024 Very broad connector catalogue Steep learning curve and long onboarding Strong data quality and governance features Roadmap now tied to Qlik after acquisition
7. Informatica PowerCenter Informatica PowerCenter is long-established enterprise ETL with profiling, cleansing and governance, and it reads Redshift through PowerExchange for Amazon Redshift, which uses UNLOAD for extraction. Two things to weigh: Salesforce completed its acquisition of Informatica in November 2025, and PowerCenter 10.5 left standard support in March 2026, so new investment is going into Informatica's cloud platform rather than PowerCenter.
Pros Cons Data profiling alongside cleansing and transformation Enterprise product with no self-serve entry point Proven at very large enterprise scale PowerCenter 10.5 out of standard support since March 2026 Strong governance, lineage and metadata support High licence cost and long implementation Handles complex legacy sources well Overpowered for a single Redshift pipeline
8. Meltano Meltano is an open-source, CLI-first platform spun out of GitLab that manages Singer taps and targets as a version-controlled project. It reaches Redshift through a community Singer tap over JDBC, with incremental sync on a replication key. Connector maintenance depends on the Singer community, which is now largely inactive, so expect to patch the tap yourself.
Pros Cons Free, open source and Git-native CLI-first, so no interface for non-technical users Strong DataOps and CI/CD orientation Connector maintenance is left to the Singer community SDK available for building connectors No support package with an SLA Works well alongside dbt and Airflow Reads over JDBC rather than UNLOAD
9. Apache Airflow Apache Airflow is an open-source workflow orchestrator rather than an ETL tool, but its Amazon provider includes a RedshiftToS3Operator that runs UNLOAD for you, which covers many export jobs on its own. You still write the incremental logic and the load into the destination, and you run Airflow yourself or on Amazon MWAA. It also pairs well with a connector-based tool such as Airbyte for everything else.
Here is a closer look at Airbyte vs Airflow .
Pros Cons Free, open source and very widely adopted Not an ETL tool, so incremental logic and loading are yours to build RedshiftToS3Operator runs UNLOAD with no custom code Production operation takes real engineering effort Pipelines defined in Python and version controlled Steep learning curve for teams new to DAGs Available managed as Amazon MWAA No built-in connectors for SaaS destinations
What is the most efficient way to get data out of Redshift? There are three routes, and which one fits depends on where the data is going:
UNLOAD to S3: runs a query and writes the results from every compute node in parallel to S3, as Parquet or delimited text, using an IAM role. It is the fastest and cheapest way to export large tables, and Snowflake, BigQuery and Databricks can all load from the files it produces.Data sharing: gives another Redshift cluster or Serverless workgroup live, read-only access to your data without copying it. It is the right answer when the consumer is also on Redshift, and no help otherwise.JDBC or ODBC queries: what most ETL tools do. Results return through the leader node and take a slot in your workload management queues, so a large extraction can slow reporting queries. Run it off-peak or in a dedicated queue, and keep it for incremental syncs rather than full table copies.For a one-off migration, UNLOAD followed by a bulk load at the destination is usually quickest. For an ongoing sync, an ETL tool on incremental mode keeps each run small.
How do you start syncing Redshift data with Airbyte? Step 1: Set up Redshift as a source Create a Redshift user with SELECT on the schemas you want, and make sure Airbyte can reach the cluster endpoint through your security group or an SSH tunnel. In Airbyte, open Sources, choose New source and select Redshift, then enter the cluster endpoint, port (5439 by default), database, schemas and credentials, and run the connection test.
Step 2: Set up a destination Choose where the data lands: another warehouse such as Snowflake or BigQuery, a data lake, a database or cloud storage.
Step 3: Configure the connection Select the tables to replicate and use incremental sync with an updated-at cursor column where one exists, falling back to full refresh for small tables or ones where deletes must be reflected. Schedule large initial syncs outside peak reporting hours.
The process is the same in Airbyte Open Source, which you can deploy yourself , and in Airbyte Cloud, which you can try free for 14 days .
Which Redshift ETL tool should you choose? For a one-off migration out of Redshift, UNLOAD to S3 and a bulk load at the destination is hard to beat, scheduled with Airflow or Glue if it repeats. For an ongoing copy into another warehouse alongside your other sources, Airbyte does it with incremental sync and keeps the pipeline in one place. Choose Glue if everything stays in AWS, Matillion if it already runs your Redshift transformations, and Hevo or Boomi Data Integration if you want a no-code managed service. If the consumer is another Redshift cluster, use data sharing and skip ETL entirely.
For tools that cover more than Redshift, see our guide to the top data integration tools .
Frequently Asked Questions (FAQs) 1. Can you use change data capture to replicate Redshift? Not log-based CDC. Redshift does not expose a change log that external tools can read, so incremental extraction relies on a cursor column such as an updated-at timestamp. Deletes only reach the destination if you use soft-delete flags or run periodic full refreshes.
2. What is the fastest way to export data from Redshift? UNLOAD to S3. It writes query results from every compute node in parallel, as Parquet or delimited files, rather than returning rows through the leader node. Most other warehouses can then bulk load from those files.
3. How do you move data from Redshift to Snowflake or BigQuery? For a one-off migration, UNLOAD to S3 and load from there: Snowflake reads S3 through an external stage, and BigQuery Data Transfer Service has a Redshift migration option that uses the same approach. For an ongoing sync, use an ETL tool with a Redshift source and incremental replication.
4. What is Redshift data sharing? A Redshift feature that gives another cluster or Serverless workgroup live, read-only access to your data without copying it. It removes the need for ETL when both sides use Redshift, but it does not help when the consumer is on another platform.
Large SELECTs over JDBC return every row through the leader node and occupy a workload management slot, competing with reporting queries. Use UNLOAD for large exports, run extractions off-peak or in a dedicated queue, and switch ongoing syncs to incremental mode.
Neither offers Redshift as a pre-built source; both are built to load into it. Fivetran's suggested route for Redshift as a source is a custom connector built with its Connector SDK.
Suggested reads AWS ETL Tools
Data Ingestion Tools
Data Synchronization Tools
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