This guide breaks down the top 7 AWS ETL tools on the market, explores their primary use cases, and compares key criteria to help you find the right one for your data pipeline.

What are AWS ETL tools?

AWS ETL tools are a broad category of services that manage data movement and transformation within the Amazon Web Services ecosystem.

This category includes AWS-native services, integrated with its infrastructure, and third-party platforms, offering broader connectivity beyond AWS.

While these tools vary in function, they all serve the central goal of data integration: reliably moving data from various operational systems to a centralized destination to enable analytics, business intelligence, and AI workloads.

ETL vs. ELT

The rise of cloud data warehouses like Amazon Redshift, which can scale to handle massive data volumes, prompted a fundamental shift in data architecture.

Data teams now often replace the traditional ETL (Extract, Transform, Load) model with the modern ELT (Extract, Load, Transform) approach. In the ELT model, teams load raw data directly into the data warehouse, which then handles the transformation work. This method decouples ingestion from transformation, allowing raw data to be preserved and modeled for many different use cases.

It takes full advantage of the warehouse’s extensive processing capabilities and gives analysts direct access to source data.

Key AWS services for data pipelines

Most AWS ETL workflows rely on a few foundational services that work together to store, process, and analyze data:

• Amazon S3 (Simple Storage Service): An object storage service designed for massive scale. It acts as a central repository, or data lake, for vast quantities of structured, semi-structured, and unstructured data and is the most common initial staging area for raw data from different data sources.
• Amazon Redshift: A petabyte-scale cloud data warehouse built for high-performance BI reporting and analytics. It uses columnar storage and parallel processing to execute complex queries on large datasets. In an ELT workflow, AWS Redshift serves as both the destination for raw data and the engine for its transformation.
• Amazon EC2 (Elastic Compute Cloud): This service provides the foundational, on-demand compute capacity that many ETL tools use to run their applications and data processing jobs. It delivers this capacity in the form of virtual servers.

Key criteria for evaluating AWS ETL tools

Selecting the right AWS ETL tool requires assessing five criteria that directly impact pipeline reliability, data availability, and total cost. A mistake at this stage commits engineering resources to a solution that may fail to meet long-term business needs.

Connector support

Assess the quality of a tool's pre-built connector library by examining both the quantity of available data sources and the integrity of each individual integration.

High-quality connectors do more than just move data; they can also:

• automatically manage schema drift
• adapt to source API changes
• handle incremental updates without manual intervention

A sparse connector library forces engineers to build and maintain custom pipelines, which introduces latency and consumes valuable development resources.

Confirm that the tool supports both your sources, from SaaS applications to relational databases, and your target destination, whether it is a data warehouse like Amazon Redshift or a cloud storage platform like Amazon S3.

Transformation approach

Tools approach data transformation in two primary ways: visual, low-code interfaces for analysts or code-first development (e.g., SQL, Python, etc.)

• Visual tools are great for self-service analytics and straightforward business logic.
• Code-first tools offer granular control for complex data science feature engineering or custom business rules.

Your team’s technical skill set and the complexity of your requirements determine the correct approach.

Architecture and performance

ETL tools are either serverless or cluster-based.

A serverless tool like AWS Glue simplifies intermittent or unpredictable workloads. A managed-cluster tool like Amazon EMR provides more configuration options for performance tuning and consistent performance for long-running jobs.

This control allows for fine-grained cost optimization through instance selection and can provide significant cost savings if actively managed.

Overhead

Prioritize the total cost of ownership over initial price. Less expensive tools usually require extensive technical resources to fully utilize the various functions.

Fully managed solutions allow engineers to focus on data modeling and analytics.

Pricing model

ETL tool pricing typically falls into two categories:

1. Consumption-based (data volume/rows)
2. Resource-based (hourly compute)

Forecast costs by modeling data volumes and job frequency. Clarify how failed jobs and data resyncs impact costs, as these can be significant.

The top 7 AWS ETL tools

Fivetran: Best for automated, reliable data movement

Fivetran is an automated data movement platform that centralizes data from hundreds of sources into a cloud data warehouse. It operates on an ELT model, automating the extraction and loading stages with 99.9% uptime. Its pre-built, zero-maintenance connectors handle the entire ingestion process, which frees data teams to focus on analysis rather than data transport.

Features

• Automated schema management: Fivetran automatically propagates source schema changes to the destination. It manages new columns or altered data types in the data warehouse without manual intervention, which prevents pipeline failures.
• Pre-built, zero-maintenance connectors: The platform offers more than 700 pre-built connectors for databases and SaaS applications. Fivetran engineers maintain these connectors and proactively manage all source API changes to ensure continuous data flow.
• High-volume Change Data Capture (CDC): For database replication, Fivetran uses log-based CDC to achieve near-real-time data replication with minimal impact on the source. The platform replicates more than 500 GB of data per hour for large-scale enterprise workloads.

Use cases

• Centralizing diverse data sources: Teams use Fivetran to consolidate data from sources like Salesforce and Google Analytics into a centralized repository in Amazon Redshift or Snowflake.
• High-volume database replication: Organizations replicate large transactional databases to the cloud for analytics, such as creating a real-time replica of a production Postgres to Snowflake for queries that do not affect operational performance.
• Building the data foundation for BI: Fivetran supplies the continuous and reliable flow of fresh, analytics-ready data required for business intelligence, reporting, and data science modeling.
• Reverse ETL: Fivetran’s reverse ETL (rETL) can sync from your data warehouse or lake back into your team’s everyday business applications.

Limitations

• Limited transformation capabilities: Fivetran does not perform data cleaning or transformation during ingestion. It preserves raw source fidelity and leaves all transformation to downstream tools like dbt.

Pricing

Fivetran uses a consumption-based model that bills on Monthly Active Rows (MAR). Fivetran considers a row active once a month when it adds or updates that row in the destination warehouse.

AWS Glue: Best for serverless, Spark-based data integration

AWS Glue is a serverless data integration service that discovers, prepares, and integrates data for analytics and machine learning. AWS Glue uses the Apache Spark framework and is built to work natively within the AWS ecosystem. The service contains three components: the AWS Glue Data Catalog for metadata, a managed Spark engine for ETL jobs, and AWS Glue Studio for visual workflow authoring.

Features

• Serverless Spark environment. Glue automatically provisions and scales the resources for data transformation jobs. This architecture eliminates cluster management, simplifies operations, and means users pay only for the compute time they use.
• Integrated Data Catalog. The Glue Data Catalog acts as a persistent metadata store. Glue crawlers scan sources like Amazon S3 and Amazon RDS, infer schemas, and automatically populate the catalog, making the data discoverable by other AWS services.
• Visual and code-based job authoring. Users can visually build ETL jobs in Glue Studio with a drag-and-drop interface. For complex logic, developers can write custom Python or Scala scripts.

Use cases

• Data preparation for a data lake. Glue processes and transforms raw data in Amazon S3. It converts data into optimized formats like Apache Parquet and partitions it to improve query performance.
• Periodic, large-scale ETL jobs. For scheduled batch workloads, Glue’s serverless model is highly cost-effective because it avoids the expense of an idle, persistent cluster.
• Transforming data within the AWS ecosystem. The service excels at moving data between different AWS services, such as processing Amazon Kinesis streams from S3 and loading the results into Redshift.

Limitations

• Focus on the AWS ecosystem. AWS primarily optimizes Glue's connectors for its own services. Connecting to many external SaaS applications often requires custom development.
• Cold start latency. As a serverless service, Glue jobs can experience multi-minute delays during resource provisioning, making the tool a poor fit for real-time data pipelines.
• Complex jobs require Spark expertise. While Glue Studio simplifies basic tasks, building sophisticated pipelines requires a strong understanding of Apache Spark development.

Pricing

AWS Glue uses a resource-based model, charging a set hourly rate for its data processing units (DPUs). However, billing is metered by the second, so you only pay for the precise compute time a job requires. Each DPU provides 4 vCPUs of compute and 16 GB of memory. The AWS Glue Data Catalog incurs separate fees for object storage and API requests.

AWS Data Pipeline: Best for scheduled data workflows between AWS services

AWS Data Pipeline is a web service for orchestrating and automating data movement and transformation workflows. Users design pipelines to schedule data processing tasks between different AWS compute and storage services, as well as some on-premises sources. The service manages resource provisioning, execution logic, and dependency tracking. It is a legacy service for simple, time-based batch processing jobs.

Features

• Workflow orchestration. Data Pipeline provides a scheduling and tracking engine that manages dependencies between tasks, handles transient failures with automatic retries, and sends notifications for success or failure events.
• Template-based deployment. The service offers pre-built templates for common tasks, such as archiving data from Amazon DynamoDB to Amazon S3 or performing regular backups of Amazon RDS instances.
• Managed resource provisioning. The pipeline automatically provisions and terminates the necessary AWS resources, such as Amazon EC2 instances or Amazon EMR clusters, to perform its scheduled tasks.

Use cases

• Scheduled data movement. A primary use case is moving daily logs from S3 into an EMR cluster for processing, then loading the aggregated results into Amazon Redshift.
• Automated backups and archiving. Teams use Data Pipeline to create recurring jobs that back up production databases like RDS to S3 for disaster recovery.
• Simple batch processing. It orchestrates basic, periodic data validation or transformation tasks on a fixed schedule.

Limitations

• Legacy interface and functionality. Data Pipeline is not a modern ETL tool. Its interface is dated, and it lacks the transformation capabilities and broad connector support of newer services.
• Limited source support. The service is designed for workflows within the AWS ecosystem and does not connect to the wide range of external SaaS applications modern businesses use.
• Inefficient for simple tasks. For event-driven tasks, other services like AWS Lambda and AWS Step Functions are more direct and cost-effective.

Pricing

AWS Data Pipeline charges a monthly fee based on the frequency of a pipeline’s activities. Users also pay for the underlying AWS resources, such as EC2 instances and S3 storage, that the pipeline consumes.

Stitch Data: Best for rapid ingestion from SaaS sources

Stitch is a cloud-based ELT platform that specializes in moving data from SaaS applications and databases to a data warehouse. Now part of Talend, Stitch provides a simple, developer-friendly way to centralize data. It offers a large library of pre-built integrations that require minimal configuration, allowing teams to begin moving data in minutes.

Features

• Broad source connectivity. Stitch provides pre-built connectors for more than 130 databases and SaaS applications, with a strong focus on popular business tools.
• Extensible Singer-based framework. Stitch uses Singer, an open-source standard for data extraction scripts. This allows developers to build custom data sources or contribute to the community of existing taps.
• Automated data loading. The platform automates data extraction, schema replication, and loading into the destination. It manages scheduling and incremental updates without user intervention.

Use cases

• Centralizing marketing and sales data. Marketing teams use Stitch to pull data from platforms like Salesforce and Google Ads into a central warehouse for attribution analysis.
• Quickly setting up new data pipelines. The user-friendly interface allows teams to establish new ELT pipelines without a lengthy engineering project.
• Consolidating product analytics. Engineering teams connect to product databases and event-tracking services to build a unified view of user behavior.

Limitations

• Limited transformation capabilities. Stitch is an ELT tool with limited transformation features. Any significant data transformation must occur post-load in the warehouse.
• Less enterprise database-focused. While it supports common databases, its high-volume CDC capabilities are less mature than those of platforms focused on large-scale database replication.
• Fewer enterprise security features. Advanced security features, such as granular role-based access control and column-level masking, are less comprehensive than in other enterprise-focused tools.

Pricing

Stitch uses a consumption-based model that bills on the number of rows replicated per month. Pricing tiers are based on data volume and offer different levels of support and features.

Talend: Best for enterprise hybrid-cloud integration

Talend, now owned by Qlik, is a data integration platform offering tools for ETL, data quality, and governance.

Its comprehensive platform, Talend Data Fabric, helps build, manage, and monitor complex data pipelines using traditional ETL or modern ELT patterns in a visual, code-optional environment. It’s built for large enterprises with hybrid-cloud data environments.

Features

• Visual job design. A graphical interface allows developers to build data pipelines with minimal coding. The studio generates Java or Apache Spark code that developers can customize or extend.
• Broad connectivity. Talend offers an extensive library of connectors and components that support a wide range of databases, enterprise applications, and cloud services.
• Unified data platform. Talend Data Fabric combines data integration, application integration, data quality, and metadata management into a single product to support large-scale data projects.

Use cases

• Complex ETL for data warehousing. Talend is used for traditional data warehouse projects that require significant in-flight data cleansing, validation, and enrichment before loading.
• Hybrid cloud integration. The platform integrates data sources from both on-premises systems and cloud environments, making it a common choice for enterprises undergoing cloud migrations.
• Big data processing. Talend generates native Spark code, allowing data engineers to build and run large-scale data processing jobs on clusters like Amazon EMR.

Limitations

• Steep learning curve: Talend Studio's complexity and vast library often require extensive training.
• Resource-intensive. Due to its resource-heavy components, the execution environment needs to be carefully managed.
• Talend Open Studio, previously open-source, was discontinued in January 2024; new users must now use the commercial version.

Pricing

Talend Data Fabric's subscription model is based on user count and required features.

Amazon EMR: Best for large-scale big data processing

Amazon EMR (Elastic MapReduce) is a managed cluster platform that simplifies running big data frameworks, such as Apache Spark and Hadoop, to process vast amounts of data. EMR is not a traditional ETL tool with a graphical interface. Instead, EMR functions as an infrastructure service that gives data engineers a flexible environment to run custom, code-heavy data processing and transformation jobs.

Features

• Managed big data frameworks. EMR automates the provisioning and configuration of clusters, simplifying the deployment of open-source tools like Spark, Hive, Presto, and Flink on AWS.
• Granular cluster control. Users have complete control over their cluster's configuration, including the specific EC2 instance types, storage options, and software versions for deep performance tuning.
• Decoupled storage and compute. EMR clusters use the EMR File System (EMRFS) to directly access data in Amazon S3. This allows users to terminate clusters when idle to save costs while their data persists in S3.

Use cases

• Petabyte-scale data transformation. EMR processes massive datasets that are too large for traditional database systems. Common workloads include log analysis, scientific simulation, and financial modeling.
• Machine learning data preparation. Data science teams use EMR to run large-scale data cleansing, feature engineering, and model training jobs on distributed datasets.
• Custom ETL pipelines. Organizations with deep data engineering resources use EMR to run highly customized ETL jobs that require specific libraries or performance characteristics unavailable in serverless tools.

Limitations

• High overhead. EMR is not a fully managed service. Users are responsible for configuring, monitoring, and optimizing their clusters, which requires significant data engineering expertise.
• Requires coding expertise. EMR has no low-code or visual interface. Building pipelines requires proficiency in Python or Scala and a deep understanding of frameworks like Spark.
• Inefficient for small jobs. The cluster-based model is not cost-effective for small or intermittent workloads. Serverless services like AWS Glue are a better choice for those tasks.

Pricing

Amazon EMR bills per second for the type and number of EC2 instances in a cluster. AWS adds an EMR platform fee for every instance hour to the standard EC2 price.

Informatica: Best for enterprise-grade, hybrid cloud data management

Informatica is an enterprise data integration platform with a comprehensive suite of tools for both on-premises and cloud environments. Its cloud offering, Informatica Intelligent Data Management Cloud (IDMC), provides services for data ingestion, ETL, application integration, data quality, and data governance. The platform serves large organizations with complex security, governance, and hybrid infrastructure requirements.

Features

• Comprehensive data management. IDMC is a unified platform that includes tools for data profiling, data quality management, metadata management, and data privacy.
• AI-powered automation. The platform's CLAIRE AI engine provides recommendations to automate data integration tasks, such as data discovery, schema mapping, and pipeline creation.
• Hybrid and multi-cloud support. Informatica provides broad connectivity to hundreds of on-premises systems, enterprise applications, and cloud data sources.

Use cases

• Enterprise data warehousing. Large corporations use Informatica to populate their enterprise data warehouses from a wide array of legacy systems and modern sources while enforcing strict data quality and governance rules.
• Hybrid data integration. A primary use case is integrating data between on-premises databases like Oracle Data Integrate or DB2 and cloud destinations like Amazon Redshift or Snowflake.
• Master Data Management (MDM). Organizations use the platform to create and manage a single, authoritative view of critical business data, such as customer or product information.

Limitations

• High total cost of ownership. Informatica is positioned as a premium enterprise solution, and its pricing reflects that. Beyond licensing, the platform's complexity creates a high total cost of ownership, as it requires specialized developers and significant hands-on management.
• Heavyweight architecture. The platform's architecture is heavyweight compared to cloud-native, serverless alternatives and is often overkill for smaller data teams.
• Complex pricing model. The consumption-based pricing, which uses Informatica Processing Units (IPUs), can be difficult to forecast and manage.

Pricing

Informatica IDMC uses a consumption-based pricing model that bills on IPUs. Data integration jobs consume IPUs, and the specific cost also depends on the number of connectors, users, and advanced features in the subscription.

The role of automated data movement

Native AWS tools excel at processing data already within the cloud. An automated data movement platform solves the distinct challenge of ingesting data from hundreds of external SaaS applications and databases.

These platforms automate the EL (Extract and Load) process by providing pre-built, zero-maintenance connectors that adapt to source API changes and schema drift. This eliminates the engineering overhead of building and maintaining custom pipelines, ensuring a consistent and reliable flow of data into a destination like Amazon Redshift or Amazon S3.

The architecture separates the EL from the T. The automated platform handles data ingestion, delivering centralized data to the warehouse. Teams then use native AWS services for all downstream transformation. Fivetran, for example, provides more than 700 pre-built connectors that automate this process with 99.9% uptime.

Build your AWS ETL foundation with Fivetran

Choosing the right AWS ETL tool is a strategic architectural decision. A team that runs custom Spark jobs on petabyte-scale datasets in Amazon EMR has fundamentally different requirements than a marketing team that centralizes SaaS application data with a tool like Stitch or Fivetran. The best solution directly maps to your specific data sources, transformation complexity, and the technical skill set of your team.

Ultimately, a reliable ingestion layer is the most critical component of a modern data stack. Prioritizing automated, zero-maintenance data movement ensures your teams have uninterrupted access to fresh, centralized data, giving your organization the flexibility to adapt and innovate.

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