TL;DR Redshift ETL tools extract analytical data from Amazon Redshift and load it into the warehouses, databases and applications that need it. The most prominent options include:
Airbyte Fivetran Stitch Matillion These ETL and ELT tools extract data from Redshift and other sources, transform it, and load it into a database, warehouse or lake. Airbyte distinguishes itself by offering self-hosted open source, managed Cloud, and Enterprise Flex hybrid deployment where the data plane stays inside your own environment.
What is Redshift & How Does it Tie With ETL? Amazon Redshift is a cloud data warehousing service from AWS that lets you analyse very large datasets using standard SQL. It uses columnar storage and is built for high performance and scalability on analytical workloads. Data teams extract data from Redshift using SQL queries, JDBC and ODBC connections, or dedicated ETL tools that move processed analytical data into other systems. That matters because Redshift is rarely the last stop: teams need to combine warehouse data with operational systems, feed BI tools that cannot query it directly, automate reporting, and distribute insights across the organisation while keeping Redshift as the central analytical store.
What is ETL? ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT? More recently, ETL has been replaced by ELT (Extract, Load, Transform) . ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
Faster processing times and loading speed Better scalability at a lower cost Support of more data sources (including Cloud apps), and of unstructured data Ability to have no-code data pipelines More flexibility and autonomy for data analysts with lower maintenance Better data integrity and reliability, easier identification of data inconsistencies Support of many more automations, including automatic schema change migration For simplicity, this article uses Redshift ETL as shorthand for all data integration tools that move data out of Redshift, ETL and ELT alike.
Why Move Data Out of Redshift? Companies might do Redshift ETL for several reasons:
Business intelligence: Redshift data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operationsCompliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.Overall, ETL from Redshift allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.
Tool Type Connectors Extensible Deployment Pricing Model Airbyte Open-source ELT 700+ Yes, CDK and no-code builder Cloud, hybrid (Flex), self-managed Free OSS; capacity-based paid tiers Fivetran Managed ELT 700+ Limited, via Functions Cloud Monthly active rows Stitch Data Managed ELT 140+ Via Singer taps Cloud Row volume tiers Matillion Self-hosted ELT 100+ Limited Self-hosted, cloud Credit-based Apache Airflow Orchestration Operators, not connectors Yes, you build it Self-managed Free, infrastructure cost only Talend Enterprise ETL 1,000+ Via Open Studio On-premises, cloud Subscription, custom Pentaho Enterprise ETL Broad Open-source roots On-premises, cloud Enterprise licence Informatica PowerCenter Enterprise ETL Extensive Limited On-premises Enterprise licence SSIS Traditional ETL Microsoft ecosystem Custom components On-premises, Azure Included with SQL Server licence Singer Open-source framework Community taps Yes, fully Self-managed Free, open source Rivery Managed ELT 150+ Limited Cloud Usage-based credits Hevo Data Managed ELT 150+ Limited Cloud Event-based tiers Meltano Open-source ELT Singer taps and targets Yes, via SDK Self-managed Free, open source
Which Are the Best Redshift ETL Tools? Here are the thirteen Redshift ETL tools worth comparing, based on adoption and the criteria set out below:
1. Airbyte Airbyte is the leading open data movement platform, created in July 2020. It offers one of the largest catalogues of data connectors available, with 700+ connectors covering databases, APIs, files and SaaS applications. Major users include Siemens, Calendly and AngelList. Airbyte integrates with dbt for transformation and with Airflow, Prefect or Dagster for orchestration, and offers an API and Terraform Provider alongside its interface.
What's unique about Airbyte? Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
Beyond those two, Enterprise Flex offers a hybrid model where Airbyte runs the control plane for orchestration and monitoring while the data plane runs inside your own AWS account, VPC or data centre. Records, credentials and encryption keys never leave your boundary, connections are outbound only, and you can bring your own KMS. Region pinning ties a data plane to a specific region, and air-gapped deployment is supported.
Pros Cons 700+ connectors, the widest coverage in this comparison Transformation relies on dbt rather than being built in Connectors are open source and editable Self-managed deployment needs Kubernetes or Docker skills Stream-level control lets you relaunch a single failed stream Not an orchestrator, so pair it with Airflow or Dagster Enterprise Flex keeps data and keys inside your AWS account Flex pricing is custom and needs a sales conversation Capacity-based pricing keeps costs flat as volume grows Advanced governance sits on the paid tiers
2. Fivetran Fivetran is a closed-source managed ELT service created in 2012, with 700+ data connectors and a large customer base.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran? Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
Pros Cons Large connector catalogue with reliable upkeep Monthly active row pricing is hard to forecast Proven and widely adopted since 2012 Closed source, so connectors cannot be freely edited Automated schema handling reduces breakage Data processed on vendor infrastructure by default Very low ongoing maintenance Among the more expensive options at volume
3. Stitch Data Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch? Stitch competes primarily on price rather than breadth, which suits teams with a small number of common sources and modest volumes. The trade-off is a narrower connector catalogue and less depth in transformation than the larger platforms offer.
Here are more insights on the differences between Airbyte and Stitch .
Pros Cons Among the lowest-cost managed options Narrower connector catalogue than the leaders Built on the open Singer specification Only the most used Singer taps are actively maintained Simple setup with a short time to first sync Limited transformation capability Transparent row-volume pricing Ownership has changed hands several times
4. Matillion Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion? Being self-hosted means Matillion keeps your data inside your own infrastructure. The trade-offs are that multi-cloud setups may require several paid instances, and that Matillion has verticalised its offering to cover the whole ELT process, so integration with tools like dbt and Airflow is more limited than with composable platforms.
Here are more insights on the differentiations between Airbyte and Matillion .
Pros Cons Self-hosted, so data stays in your infrastructure Around 100 connectors, fewer than the leaders Covers extract, load and transform in one tool Multi-cloud setups may need several paid instances Strong visual transformation designer Limited integration with dbt and Airflow Warehouse-native processing keeps performance high Credit-based pricing needs active monitoring
5. Airflow Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow? Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow .
Pros Cons Free, open source and very widely adopted Not an ETL tool, so it ships no Redshift connector Pipelines defined in Python and version controlled You build and maintain extraction logic yourself Large operator library across AWS services Production operation takes real engineering effort Pairs well with a connector-based tool for movement Steep learning curve for teams new to DAGs
6. Talend Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend? What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.
Pros Cons Integration, quality and governance in one platform Implementation is involved and rarely self-serve Open Studio allows customisation and extension Enterprise pricing puts it beyond smaller teams Very broad connector catalogue Steep learning curve and long onboarding Strong data quality and governance features Roadmap now tied to Qlik after acquisition
7. Pentaho Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho? What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
Pros Cons Combines ETL with analytics, reporting and data mining ETL only, so it misses the benefits of the ELT model Open-source roots allow customisation Enterprise product with no self-serve entry point Machine learning and predictive analytics built in Interface feels dated against modern platforms Broad source and destination coverage Heavier than most Redshift pipelines require
8. Informatica PowerCenter Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
Pros Cons Data profiling alongside cleansing and transformation Enterprise product with no self-serve entry point Proven at very large enterprise scale Deployed in your infrastructure, so ops overhead is yours Strong governance, lineage and metadata support High licence cost and long implementation Handles complex legacy sources well Overpowered for a single Redshift pipeline
9. Microsoft SQL Server Integration Services (SSIS) SQL Server Integration Services is the Microsoft option, strongest when your stack already runs on Microsoft infrastructure. It follows the traditional ETL model, transforming data before loading rather than after, which suits heavy pre-load logic but misses the benefits of ELT.
Pros Cons Included with a SQL Server licence, so no extra tool cost Strongest only inside the Microsoft ecosystem Heavy transformation before data reaches the target ETL only, so it misses the benefits of the ELT model Large existing base of expertise and documentation Few prebuilt connectors for modern SaaS sources Mature and stable for on-premises workloads Package development requires Visual Studio tooling
10. Singer Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
Pros Cons Open JSON-based specification anyone can implement Maintenance largely stopped after the Talend acquisition Free, with no licence or usage cost Many taps and targets are increasingly outdated Taps and targets are fully editable No interface, scheduling or monitoring of its own Useful as a building block inside other tools No support or SLA
11. Rivery Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
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 Less flexible than composable open-source stacks
12. HevoData HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
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
13. Meltano Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
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 Building connectors takes more effort than Airbyte's CDK
All those ETL tools are not specific to Redshift, you might also find some other specific data loader for Redshift data. But you will most likely not want to be loading data from only Redshift in your data stores.
How Do You Choose the Right Redshift ETL Tool? As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your Redshift ETL solution.
Here is our recommendation for the criteria to consider:
Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?Ability to build new connectors: all data integration solutions support a limited number of data sources.Support of change data capture: this is especially important for your databases.Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling featureEfficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbtLevel of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.Redshift holds the analytical data your business runs on, and an ETL tool can extract any of it. In practice that means:
1. Structured tables: the core relational data in your Redshift schemas, organised into columns with defined types.
2. Semi-structured data: JSON and SUPER column data, which Redshift can store and query natively.
3. Time-series data: event, transaction and sensor records organised by timestamp.
4. Historical and archived data: long-retention records kept in Redshift for compliance or trend analysis.
5. Spectrum external tables: data held in S3 and queried through Redshift Spectrum without loading it into the cluster.
6. Materialized views: precomputed result sets you may want to replicate rather than recalculate downstream.
7. Aggregated and derived tables: the modelled outputs of your transformations, ready to be pushed to BI tools or operational systems.
Redshift also exposes cluster metadata, query logs and performance metrics through its management API, but those are operational monitoring data rather than the analytical data most ETL pipelines are built to move.
How Do You Start Pulling Data From Redshift? If you decide to test Airbyte, you can start analyzing your Redshift data within minutes in three easy steps:
Step 1: Set up Redshift as a source connector 1. Open the Airbyte UI and navigate to the "Sources" tab.
2. Click on the "Create a new connection" button and select "Redshift" as the source.
3. Enter a name for the connection and click "Next".
4. Enter the necessary credentials for your Redshift database, including the host, port, database name, username, and password.
5. Test the connection to ensure that the credentials are correct and the connection is successful.
6. Select the tables or views that you want to replicate from Redshift to Airbyte.
7. Choose the replication method, either full or incremental, and set any necessary parameters.
8. Click "Create connection" to save the configuration and start the replication process.
9. Monitor the replication progress and troubleshoot any errors that may occur. 10. Once the replication is complete, you can use the data in Airbyte for further analysis or integration with other tools.
Step 2: Set up a destination for your extracted Redshift data Choose the destination you want your Redshift data to land in. This can be a cloud data warehouse, data lake, database, cloud storage service or any other supported Airbyte destination.
Step 3: Configure the Redshift data pipeline in Airbyte Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
The process is the same whether you deploy Airbyte Open Source in a few minutes or start with Airbyte Cloud .
Which Redshift ETL Tool Should You Choose? The thirteen tools above cover the realistic options for moving data out of Redshift. If connector breadth and deployment flexibility matter most, Airbyte fits. If you want pipelines someone else maintains, Fivetran, Stitch or Hevo. If transformation is the bulk of the work, Matillion, Talend or Pentaho. If you are working under enterprise governance with legacy sources, Informatica or SSIS. And if you already run an orchestrator, Airflow or Meltano keep everything defined in code.
Suggested Reads AWS ETL Tools
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Data Integration Tools
Data Synchronization Tools
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