Data is only as useful as the way you collect, move, and transform it. If your ETL processes cannot keep up, then even your best data is of no use. DynamoDB users need ETL tools that can manage high-volume, real-time data without breaking the pipelines.
Choosing the right DynamoDB ETL tool makes all the difference, but most of them look similar on paper. This blog rounds up the 9 best DynamoDB ETL tools for 2025 with clear feature comparison, pricing, and pros and cons. Read on to quickly find the solution that fits your workflow and keeps your data flowing smoothly.
Table of Contents
List Of Best DynamoDB ETL Tools
 |  |  |  |  |  |  |  |  | |
| Pros | No-code, real-time, 150+ connectors, 24x7 support  | AWS-native, prebuilt workflows | Enterprise-grade, high performance | 100+ connectors, MDM | Cloud-native, strong transformations | Serverless, event-driven | Simple DynamoDB → Redshift | Built-in ELT | Open-source, robust integrations |
| Cons | None major | Limited to AWS sources | Expensive, limited storage  | Complex for DynamoDB | Limited DynamoDB support | RCU limits | Hard to change configs | Limited if using other BI tools | Overkill for simple ETL |
| Best For | Easy, reliable ETL | AWS-only infrastructures | Large enterprises | Row-based ETL workflows | Cloud warehouse ETL | ETL within AWS ecosystem | Redshift-centric analytics | Panoply BI users | Complex multi-stage pipelines |
| Pricing | Free trial / Transparent | $0.6–$2.5/month | $2,000+/month | $1.71/hr+ | $1.37–$5.48/hr | Pay-as-you-go | $150–$500/month | $200/month | Free |
Choosing the perfect DynamoDB ETL tool that perfectly fits your business needs can be a daunting task, especially when a large number of tools are available on the market. To make your search easier, here is a complete list of the 9 best DynamoDB ETL tools for you to choose from and easily start setting up your ETL pipeline.
1) Hevo Data
Hevo Data is a no-code ETL platform designed to simplify data integration from Amazon DynamoDB to various data warehouses like Redshift, BigQuery, and Snowflake.
With support for DynamoDB Streams, Hevo enables real-time data replication, schema mapping, and automated transformations, making it ideal for teams seeking efficient and scalable data workflows.
Hevo’s platform is designed for reliability and speed, helping businesses make data-driven decisions faster without complex setup or ongoing maintenance.
Key Features
- Effortlessly capture and replicate real-time changes from DynamoDB tables to your data warehouse.
- Handles dynamic schema updates in DynamoDB without breaking pipelines.
- Apply no-code transformations tailored for DynamoDB data, including flattening nested attributes and converting JSON data.
- Guarantees zero data loss even during failures or network interruptions.
- Keep track of every event in your ETL process for compliance and troubleshooting.
Pricing
Hevo offers transparent, usage-based pricing. Plans include a 14-day free trial, followed by tiered packages starting from entry-level for small teams to enterprise-grade options. Pricing depends on events processed, ensuring you only pay for what you use.
Why is Hevo the Best ETL tool for DynamoDB?
Hevo goes beyond standard ETL by addressing DynamoDB’s unique NoSQL challenges. It adapts automatically to schema changes, ensuring your pipelines never break, and supports multi-region replication to consolidate data seamlessly.
With built-in data governance, audit logs, and compliance features, your sensitive DynamoDB data stays secure. Low-code automation lets you trigger workflows and detect anomalies in real time, while team collaboration features, like version control and activity tracking, make pipeline management effortless.
For organizations looking to transform raw DynamoDB data into analytics-ready insights, Hevo combines simplicity, reliability, and advanced functionality in one platform.
With Hevo, simply connect your DynamoDB database to your preferred data warehouse and watch your data load in a matter of minutes. Enjoy a stress-free, low-maintenance data load.
2000+ customers trust Hevo for the following reasons:
- Hevo’s real-time streaming architecture helps you get faster insights.
- It identifies schema changes in your incoming data and automatically replicates those in your destinations.
- Hevo’s fault-tolerant architecture assures that no data is lost even if a pipeline fails.
Try Hevo today to experience seamless data transformation and migration.
Get Started with Hevo for Free2) AWS Data Pipeline
AWS Data Pipeline is a web service offered by Amazon that provides an easy management system for data-driven workflows. There are pre-configured workflows to bring data from other AWS offerings. You can also build various patterns or templates to deal with similar tasks in the future to avoid repeating the same pipelines.
For DynamoDB, it automates the movement of data between tables and other AWS services such as S3 or Redshift, enabling efficient ETL processes while maintaining data consistency across AWS infrastructure.
Data pipeline allows the user to schedule and orchestrate workflows of existing ETL code or applications, rather than forcing you to conform to the bounds and rules of the chosen DynamoDB ETL application.
Key Features:
- Supports custom pipeline templates to avoid repetitive configurations.
- Pre-configured workflows optimized for AWS and DynamoDB.
- Schedule and automate DynamoDB ETL jobs.
- Integrates directly with AWS data services like S3, Redshift, and EMR.
Pros
- It resides on the same infrastructure as DynamoDB, hence it’s faster and integrates seamlessly.
Cons
- Data pipeline doesn’t support any other SaaS data sources.
Use Case
Best suited for AWS-only data infrastructures or when a fully managed ETL solution is needed. Data pipelines efficiently create data lakes and warehouses. For instance, an e-commerce platform records user actions in RDS, transforms the data into analytics, and stores it. AWS Data Pipeline creates EMR clusters to build analytics documents nightly. It mashes up historical data with current activities for on-demand reports, while costs are minimized by provisioning infrastructure only during jobs.
Pricing
High-frequency activities running on AWS cost $1 per month, whereas low-frequency activities cost $0.6 per month. Running on-premise, the high and low-frequency activities, cost $2.5 and $1.5 per month respectively.
3) Informatica
Informatica’s native connector for DynamoDB ETL provides native, high-volume connectivity and abstracts several hierarchies of key-value pairs.
Informatica can build custom transformations using a proprietary transformation language. It has pre-built data connectors for most AWS offerings, like DynamoDB/EMR/RDS/Redshift/S3, it is probably the only vendor to provide a complete data integration solution for AWS.
Amazon DynamoDB connector by Informatica uses AWS SDk authentication that supports environment variables, credential profiles, and more for flexible access. It handles many DynamoDB data types, including Binary, Boolean, List, Map, Number, and String and maps them accurately to transformation types like Integer, Double, String, Array, and Struct. This ensures reliable, high-volume ETL operations while maintaining data integrity across your DynamoDB workflows.
It adheres to many compliances, governance, and security certifications like HIPAA/SOC 2/SOC 3/Privacy Shield.
Key Features:
- Supports multiple AWS data services for end-to-end integration.
- High-volume DynamoDB connectivity with hierarchical data handling.
- Custom transformations via proprietary transformation language.
- Enterprise-grade security and compliance (HIPAA, SOC 2/3).
Pros
- Delivers high performance and is suited if you have many sources on AWS.
Cons
- The solution is limited to 1TB of DynamoDB storage.
- The only cloud data warehouse destination it supports is Amazon Redshift
- The only data lake destination it supports is Microsoft Azure SQL Data Lake
Use Case
Informatica has primarily been an on-premise solution, focusing on preload transformations, which is key for on-premises data warehouses. Its pricing caters to large enterprises with substantial budgets.
For example, to analyze Facebook user reactions by age and geography, you can use the Amazon DynamoDB Connector to store comments in DynamoDB. A synchronization task can categorize these comments, and once stored, BI tools can generate reports.
Pricing
Informatica’s pricing starts at $2000 per month with additional costs for customization, integration, and migration (based on the number of records).
They offer different pricing based on regions for Australia, Europe, Japan, and the UK.
4) Talend
Talend is a data integration tool (not full BI ) with 100+ connectors for various sources. Continuous integration reduces the overhead of repository management and deployment. Its GUI drove and has Master Data Management (MDM) functionality, which allows organizations to have a single, consistent, and accurate view of key enterprise data.
Talend offers dedicated connectors for DynamoDB, allowing organizations to pull data from tables, apply transformations, and load it into warehouses or analytics platforms. Its support for dynamic schemas makes it ideal for handling the flexible, evolving structures typical of NoSQL datasets. Its drag-and-drop interface simplifies transformations, while custom Java code allows advanced logic for high-volume data.
With real-time monitoring and scalable workflows, Talend ensures your DynamoDB data flows reliably into analytics platforms or warehouses without manual intervention, making it ideal for both technical and business teams.
Key Features
- Pre-built transformation components for common data operations without custom coding
- Real-time synchronization of DynamoDB tables with minimal latency
- Advanced monitoring and logging to identify and resolve pipeline issues quickly
- Flexible deployment across cloud, on-premise, or hybrid environments
Pros
- Supports dynamic schemas, handling records without predefined columns.
- Processes data row-by-row, ideal for per-record transformations before loading into a warehouse.
Cons
- Limited scheduling and streaming in the open-source edition.
- Better suited for big data than typical DynamoDB ETL.
Use Case
Talend Studio now allows you to manage data with DynamoDB in a Standard Data Integration Job using the following components: tDynamoDBOutput and tDynamoDBInput.
Pricing
Talend offers user-based pricing with a basic plan starting at $1.71 per hour for 2 users, going up to $1170 per user per month for the enterprise plan. The pricing is transparent.
5) Matillion ETL
Another solution specifically built for cloud data warehouses is Matillion.
So, if you want to load DynamoDB data into Amazon Redshift, Google BigQuery, or Snowflake, it could be a good option for you. Matillion ETL allows you to perform powerful transformations and combine transformations to solve complex business logic. You can use scheduling orchestration to run your jobs when resources are available. Matillion ETL integrates with an extensive list of pre-built data source connectors, loads the data into the cloud data warehouse, and then performs the necessary transformations to make data consumable by analytics tools such as Looker, Tableau, and more.
Key Features:
- Integration with BI tools like Tableau and Looker.
- Direct loading of DynamoDB data into Redshift, BigQuery, or Snowflake.
- Powerful cloud data warehouse transformations.
- Scheduling orchestration for efficient resource usage.
Pros
- Efficiently loads DynamoDB data to Redshift using native capabilities.
- Supports complex joins and transformations during data transfer.
Cons
- Potential conflicts when multiple users develop jobs simultaneously.
- No clustering; large datasets may take longer to process.
- DynamoDB connector not supported for Snowflake.
Use Case
If you want to quickly process large amounts of data to meet performance objectives and ensure that data in transit remains secure, Matillion could be an option. It supports 70+ data sources, allows you to think about new analytics and reports instead of your data/programming architecture.
DocuSign selected Matillion ETL for Snowflake to best facilitate DocuSign’s transition to the cloud, aggregate its various data sources, and create the dimensional models needed for downstream consumption.
Pricing
Matillion’s pricing is transparent and the product is offered in multiple pricing plans, starting with Medium for 1.37 per hour going to 5.48 for Xlarge instances.
6) AWS Glue
AWS Glue is a fully managed ETL service that you control from the AWS Management Console. Glue may be a good choice if you’re moving data from an Amazon data source to an Amazon Data Warehouse. For your data sources outside AWS, you can write your code in Scala/Python to import custom libraries and Jar files into Glue ETL jobs. AWS Glue crawls through your data sources, identifies the data formats, and suggests schemas and transformations. AWS takes care of all provisioning, configuration, and scaling of resources on an Apache Spark environment. Glue also allows you to run your DynamoDB ETL jobs when an event occurs.
AWS Glue provides native support for DynamoDB, enabling automated extraction and transformation of high-velocity NoSQL data. By leveraging event-driven ETL, you can continuously synchronize DynamoDB tables with data warehouses or lakes, ensuring analytics-ready datasets without manual intervention, while maintaining low-latency access for operational insights.
Key Features:
- Integration with AWS analytics services like Redshift, S3, and Athena for streamlined reporting and insights.
- Incremental loads to capture only changes from DynamoDB streams, minimizing processing time.
- Secure data handling with encryption at rest and in transit, ensuring compliance with AWS security standards.
- Job monitoring and logging for complete visibility into DynamoDB ETL pipelines.
Pros
- You pay only for the resources used while your jobs are running.
Cons
- Quota limits can restrict ETL speed
- Increasing RCUs may affect production workloads
- ETL jobs compete with live applications for resources
Use Case
One can write a Lambda function to load data from DynamoDB, whenever new data arrives and a threshold is crossed. You can also define an hourly job, which fetches your logs from S3 and does a Map-Reduce analysis using Amazon EMR.
Pricing
Glue’s pricing is pretty transparent, a user is charged by the second for crawlers (finding data) and ETL jobs (processing and loading data), whereas there is a fixed monthly fee for storing and accessing the metadata.
7) Blendo
Blendo is a popular data integration tool. It uses natively built data connection types to make the ETL as well as ELT process a breeze. It automates data management and data transformation to get to BI insights faster. Blendo’s COPY functionality supports DynamoDB as an input source. It is possible to replicate tables from DynamoDB to tables on Amazon Redshift.
Blendo provides in-built automation, role-based access to your AWS, thereby enabling more security, and offers fine-grained control of access to resources and sensitive data. This makes managing your DynamoDB pipelines more secure, reliable, and quick-analytics ready.
Blendo integrates and syncs your data to Amazon Redshift, Google BigQuery, Microsoft SQL Server, Snowflake, PostgreSQL, and Panoply.
Key Features:
- Automated schema mapping and transformations specifically for NoSQL data structures.
- Allows integration with BI tools like Tableau or Looker for near real-time insights.
- Ensures fault-tolerant data handling, preventing data loss in high-volume DynamoDB pipelines.
Pros
- If you intend to use Redshift as your Data Warehouse, Blendo gives you an easy and efficient way to integrate many sources via its COPY and DML methods. Its integration with DynamoDB is seamless and fast.
Cons
- After integrations, it is difficult to change the parameters later.
Pricing
Blendo’s starter pack is priced at $150 per month, and the high-end “Scale” plan is priced at $500 per month.
8) Panoply
Panoply is a BI tool but has 80+ data connectors, and it combines an ETL process with its built-in automated Cloud Data Warehouse, thereby achieving ELT and allowing you to go quickly into analytics. Many of its data transformations are automated, and since it uses cloud data warehouses, you will not need to set up a separate warehouse of your own. Under the hood, Panoply uses ELT, making data ingestion faster as you don’t have to wait for the transformation to complete before loading your data.
Panoply’s ETL/ELT capabilities support DynamoDB as a source, allowing seamless table replication and data ingestion into its cloud data warehouse. This integration automates schema detection and transformations, enabling users to quickly analyze DynamoDB data alongside other sources in tools like Tableau, Looker, or Power BI.
For DynamoDB users, Panoply offers a native connector that lets you whitelist your database, select tables, and even use DynamoDB Streams for incremental sync, ensuring real-time data availability in your analytics pipeline.
Key Features:
- Supports DynamoDB Streams to keep analytics pipelines updated in near real-time.
- Easily merges DynamoDB data with other cloud and on-premise sources for unified reporting.
- Supports multi-cloud destinations, enabling flexible data warehouse integration.
Pros
- If you’re already using Panoply for your BI, you can use its inbuilt DynamoDB ETL connector.
Cons
- If you’re using any other BI tool, then Panoply’s connector should be avoided.
Pricing
$200/month (includes managed Redshift cluster).
Next, we will discuss some open source ETL tools that can be used with DynamoDB.
9) Apache Camel
Apache Camel is an open-source integration framework and message-oriented middleware, which allows you to integrate various systems consuming or producing data. It provides Java-based APIs that can be used to define routes that can integrate with live Amazon DynamoDB data. There are JDBC drives that map and translate complex SQL operations onto DynamoDB, enabling transformations and processing. Camel uses routing rules that can be specified using XML or Java and lend itself well.
For DynamoDB, Camel includes a dedicated component that lets you read, write, and update tables programmatically. This allows developers to integrate DynamoDB into broader pipelines with other AWS services or external applications, ensuring event-driven data flows and real-time synchronization.
Key Features:
- Allows programmatic transformations on DynamoDB data before sending to warehouses or other services.
- Supports real-time triggers for DynamoDB updates, enabling responsive data workflows.
- Define complex routes for DynamoDB data using Java or XML to integrate with multiple systems.
Pros
Camel is robust and extensible and integrates well with other frameworks.
Cons
Camel could be overkill if you don’t need a service-oriented architecture using message-oriented middleware and routing.
Use Case
Camel lends itself well for scenarios where the data pipelines need various tools for processing at multiple stages of DynamoDB ETL processes. E.g., when you need other data sources with DynamoDB and there is a need to transform the data before adding it to a warehouse/data lake.
Pricing
It’s free and open source.
Criteria for Choosing DynamoDB ETL Tools
- Scalability and Performance: Choose a tool that can handle growing datasets without slowing down your workflows. Look for support for real-time replication, incremental updates, and parallel processing to keep up with high-velocity DynamoDB data.
- Integration Capabilities: Ensure seamless connection with AWS services like Redshift, S3, and Lambda, as well as other analytics or BI platforms you use. This reduces manual work and allows smoother end-to-end data pipelines.
- Ease of Use: Tools with a user-friendly interface, drag-and-drop transformations, or prebuilt connectors reduce the learning curve and accelerate setup, so both technical and non-technical teams can manage pipelines efficiently.
- Reliability and Monitoring: Look for automated error handling, alerts, and logging features. These ensure data integrity, prevent pipeline failures, and make it easier to troubleshoot issues.
- Cost-Effectiveness: Balance the features and performance with pricing. Consider pay-as-you-go or transparent pricing models that scale with your usage to avoid overpaying for idle resources.
Conclusion
To conclude, this article tries to discuss some features of currently available ETL tools, both paid and open-source, and situations where they could fit in. So, you can choose any DynamoDB ETL tool depending on your needs, investment, use cases, etc. Hevo stands out with its simplistic design and easy-to-use features. Sign up for Hevo’s 14-day free trial and experience seamless data migration
FAQ on Best DynamoDB ETL Tools
1. What is the ETL tool in AWS?
The primary ETL (Extract, Transform, Load) tool in AWS is AWS Glue. It is a fully managed service that makes it easy to prepare and transform data for analytics, machine learning, and application development.
2. What is DynamoDB used for?
It is used for:
Web and Mobile Applications
Real-Time data processing
IOT Data Management
Serverless architecture
3. What are the 3 basic components of DynamoDB?
Tables: The primary structure in DynamoDB where data is stored. Each table is a collection of items, and every item is a collection of attributes.
Items: The individual records in a DynamoDB table, similar to rows in a relational database. Each item consists of a set of attributes.
Attributes: The fundamental data elements of an item, equivalent to columns in a relational database. Attributes store the actual data values.
4. What is DynamoDB ?
DynamoDB is a fully managed NoSQL database by AWS that supports both key-value and document data structures. It delivers fast, predictable performance with seamless scalability and offers features like data replication across regions and encryption at rest for secure applications.
With optional in-memory caching through DynamoDB Accelerator (DAX) and global tables for multi-region replication, it handles real-time, high-volume workloads efficiently.
Many high-growth businesses like Airbnb, Lyft, Major League Baseball, and enterprises such as Toyota, NTT Docomo, and GE Healthcare rely on DynamoDB to run mission-critical applications worldwide.
5. What are DynamoDB ETL tools and how do they work?
DynamoDB ETL tools help you extract, transform, and load data to and from DynamoDB efficiently. They simplify handling large volumes of data, whether from other databases, APIs, or files, and ensure it’s compatible with DynamoDB’s NoSQL structure.
The typical ETL workflow involves extracting data from sources, transforming it to fit DynamoDB requirements, and loading it either in batches or in real time. The right ETL tool reduces errors, saves time, and ensures your data pipelines are scalable, reliable, and ready for analytics or downstream applications.
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