Best AWS DMS Alternatives to Consider in 2025

If you’re already using AWS DMS or exploring it as an option, then you’re in the right place. Keep reading!

AWS DMS is a popular, cloud-based solution for moving data between databases. It is simple to set up and works well for basic migration tasks.

But as your data pipelines grow in size and complexity, can AWS DMS keep up?

Many companies find that while DMS handles easy migrations, it falls short when it comes to complex transformations, managing schema changes, troubleshooting issues, and ensuring smooth performance at scale.

In this article, we will help you choose the right tool that is fast, efficient, and easy to use for your data stack. We will evaluate top AWS DMS alternatives across key features, pricing, and pros & cons so you know which is best for you.

AWS DMS Overview

AWS DMS is a managed service that helps organizations migrate databases to AWS quickly and securely, with minimal downtime and zero data loss. AWS DMS architecture supports both simple and complex migrations by enabling an initial full load of your existing data, known as AWS DMS full load.

The AWS DMS CDC for Oracle enables continuous data replication, helping businesses keep datasets synchronized across on-premise, cloud, or hybrid environments. The tool is valuable for businesses that need to move or synchronize data across different environments, including on-premise, cloud, and hybrid setups.

Why Are People Moving Away From AWS DMS?

The major reasons many people are switching from AWS DMS are:

1. Complex user interface

Organizations are increasingly exploring alternatives to AWS DMS due to challenges like a complicated setup, repeated operational problems, and limitations in scaling continuous CDC workloads. AWS DMS CDC often leads to sync lag, high maintenance overhead, and unreliable replication, especially at scale.

A user says on G2:

CDC is not easily implemented using DMS and needs detailed study to implement,we need to add details in JSON sometimes which gets a little messy.

Sonali A.

Senior Data Engineer

2. Expensive

AWS DMS pricing is often higher than expected, with complex, pay-as-you-go models and additional costs for replication instances, storage, and data transfer.

A G2 review says:

Found the pricing bit on the higher side compared to other suppliers in the market

Ashish R.

Zoho CRM

3. Lack of automation

Many users are moving away from AWS DMS CDC SQL Server migration because it requires manual effort for tasks like managing schema changes, replaying historical data, and resolving failures, lacking the automation modern teams expect.

A G2 review says:

No automation or trigger need yo manual trigger to use the application

Richard Alexander A.

Data Engineer

Top 10 AWS DMS Alternatives to Consider

Here’s a quick comparison table of the best alternatives to AWS DMS:

1. Hevo

Hevo is a leading no-code data integration platform designed to expedite the process of integrating, transforming, and loading data from a wide variety of sources into data warehouses or databases. It is hosted on Amazon’s AWS and features a user-friendly interface, enabling both technical and non-technical users to manage data pipelines.

Hevo automates the entire data ingestion process by ingesting data from multiple data sources (SaaS, database, file, or custom API) and loading it into your preferred destination. 

Change Data Capture (CDC) ensures that only updated records are ingested. The platform is an ideal choice for data analysts and organizations with siloed data.

The platform’s uniqueness lies in the table-level migration control, custom SQL for partial data loads, and the ability to pause specific tables. Additionally, it offers a graphical interface for configuring pre-load transformations, and schema mapping is handled automatically. Features like automated error handling and transformation validations help maintain data quality throughout the migration process. Its real-time transformation capabilities and low latency features make it an excellent choice.

Key features:

• Connectivity: Provides over 150 battle-tested connectors, including JDBC databases, SaaS platforms, cloud storage, marketing systems, SDKs (iOS, Android), and both structured and unstructured data sources.
• Data transformation: Hevo provides a drag-and-drop interface and a Python scripting environment for complex data transformation. Users can split nested NoSQL data, normalize structures, and apply custom logic during data movement.
• Audit logs: The tool provides live monitoring dashboards, detailed audit logs at the user level, and real-time notifications via Slack, email, and other channels.

Customer review:

I love the simplicity and ease free nature of setting up pipelines. As some members in our team who come from non-tech background having knowledge in data, this tools helps them get the work done faster without having to worry about the programming and infrastructure side of it. It easily integrates in our platform. The customer support is excellent as well.

Nikhil S.

Data Science Engineer

Pros:

• Provides near real-time data replication.
• The multi-tenant structure handles billions of records.
• Offers “within-the-product” customer support through live chats.
• API integration allows users to trigger and manage pipelines programmatically.

Cons:

• Might experience high  lowlatency with large-scale datasets.
• Best suited for cloud-based integrations and not on-premise.

Why choose Hevo over AWS DMS?

• Hevo supports secure SSH connections to enhance security for on-premises or cloud databases.
• Supports real-time and incremental data replication using both SQL queries and binary logs.
• Supports data modeling, joins, and aggregations to provide a comprehensive view of the destination warehouse.

Pricing:

• Free plan with limited connectors up to 1 million events.
• Starter: $239/month up to 5 50 million events
• Professional: $679/month up to 20100 million events
• Business: Custom pricing

Case study:

Company: Meru streamlined and digitized the supply chain of the fragmented automotive aftermarket industry in Latin America.

Challenge: Meru struggled to aggregate market data into their Redshift-powered data warehouse.

Hevo’s solution: Meru deployed Hevo to integrate with diverse data sources and automate pipelines. The tool allowed the addition of new data sources without any extra costs and enabled data teams to focus on actionable insights.

Result: 50-70% reduction in data pipeline costs and increased insight frequency with a 10% cost increase.

2. Fivetran

Fivetran is a cloud-based data integration platform that extracts raw data from sources and sends it to the preferred destination using a fixed set of IP addresses. The tool features a self-deployment model and a high-volume replicator (HVR) to support enterprise-grade file management systems and database replication.

The built-in HVR solution works with multiple operating systems and supports distributed architectures, ensuring minimal impact on existing workflows while delivering low latency and managing vast data volumes. Fivetran is particularly valuable for businesses seeking to streamline analytics, power AI workflows, enable real-time reporting, and modernize their data infrastructure.

The pre-built connectors are fully managed, meaning they work, load, and update automatically. Moreover, it automates the detection of schema drifts without any manual intervention, which means new or renamed columns flow straight into the warehouse. This makes it an ideal choice for organizations aiming to scale with evolving datasets.

Key features:

• REST API: The REST API allows users to perform management actions at several endpoints to trigger immediate syncs outside scheduled times for event-driven data pulls.
• Column hashing: This feature anonymizes sensitive data within the destination while keeping its analytical value intact. Column hashing prevents the exposure of PII, making it compliant with privacy regulations.
• Custom data: Fivetran replicates custom data whenever it’s accessible. This includes custom objects, tables, and fields that store unique data of your organization.

Customer review:

Fivetran makes syncing data from multiple SaaS tools to data warehouses like BigQuery fast and effortless. With plug-and-play connectors, automatic schema management, and strong alerts, it saves our small team hours of manual work.

Dennis C.

Head of Business Operations

Pros:

• Offer flexible deployment, which includes SaaS, hybrid, and self-hosted.
• Provides a vast connector library with over 700 connectors.
• Works well with large data volumes at scale.

Cons:

• The pricing model might feel expensive to users.
• Limited customization for complex data integration needs.
• No built-in visualization and reporting capabilities.

Why choose Fivetran over AWS DMS?

• It is easy to set up and doesn’t require technical expertise.
• Certified for major security and compliance standards (SOC 2, GDPR, HIPAA, ISO 27001, PCI DSS Level 1), which is critical for handling sensitive business data.
• Fivetran enables real-time or near-real-time analytics, supports AI/ML workflows, and promotes data-driven decisions.

Pricing:

Follows a MAR model and offers four plans:

• Free
• Standard
• Enterprise
• Business critical

The price for each plan depends on the number of rows you use in a month.

3. Stitch

Stitch (owned by Qlik) is a cloud-first ETL service that operates on a Singer-based replication engine. The engine pulls data from databases on a scheduled basis and can be used as a temporary buffer for large data extractions. The tool encrypts datasets and stores them using Apache Kafka and Amazon S3, spanning various data centers.

The ETL process starts with a structure sync to detect new columns and tables in the source data. Stitch lightly transforms raw datasets to ensure compatibility with your preferred destination, such as Redshift and BigQuery. It uses SSL/TLS-encrypted connections to load data into the destination, making it ideal for data engineers looking to automate and scale data ingestion from disparate sources.

Stitch never deletes data from destinations, even if removed at the source, to preserve historical data integrity. It works on a connector-first philosophy where data exchanges happen via a standard JSON-based protocol, enabling interchangeability and modular pipeline architecture.

Key features:

• Open-source framework: The Singer framework extends extensibility and transparency. Users can create custom connectors for unique or proprietary data sources, leveraging a large community of pre-built connectors.
• Reverse ETL: Stitch supports reverse ETL, enabling organizations to move data from data warehouses back into operational tools. For example, pushing analytics data into CRM or marketing platforms.
• Data handling: Stitch encrypts data at rest and in transit to comply with major security standards, such as SOC 2, GDPR, and HIPAA.

Customer review:

Stitch integrates with most large companies such as Google Ads, Microsoft Ads, etc. One of the best things is that it sets up cost allocation in a very easy straightforward manner.

Megan S.

Digital Marketing Director

Pros:

• Fast onboarding and configuration process.
• Automatically adapts schema changes in source systems.
• Keeps the destination database up to date with accurate data replication.

Cons:

• Only supports batch data loading.
• Users complain about the inactive customer support.
• Offers basic data manipulation capabilities.

Why choose Stitch over AWS DMS?

• Stitch API access allows teams to quickly create and configure integrations to connect Stitch with an external scheduler.
• Using the cron impression, users can specify granular start times for data extraction.
• Ensures compliance with industry-standard privacy regulations, SOC 2, GDPR, and HIPAA.

Pricing:

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

4. Integrate.io

Integrate.io is a cloud-based ETL/ELT platform that provides a jargon-free environment to help companies connect with a variety of data stores and a broad set of out-of-the-box data transformation components. The tool has a data processing package that creates clusters, runs jobs, terminates jobs, and monitors pipeline performance.

Integrate.io’s ETL Dataflow defines data sources, transformation filters, and output destinations. The package detects dependencies while executing a SQL query or running a dataflow package. Furthermore, you can create a free sandbox cluster for testing ETL jobs and managing the package development cycle. Integrate.io is an optimal choice for engineering teams who need to build production-grade pipelines.

Integrate.io’s drag-and-drop workflow builder and transformation tools allow non-engineers to create and manage complex data pipelines without writing code. The platform enables users to connect to virtually any REST API, making it highly flexible for integrating with custom or less common data sources.

Key features:

• Dynamic connections: This feature allows teams to replace database connections in source and destination components and execute SQL workflows at package runtime.
• Encryption: Using the Key Management Service (KMS), you can manage encryption keys and use them to generate the encrypted message. Each dataset is encrypted using a unique data key.
• Cron expressions: Integrate.io supports job execution at irregular intervals. Packages are scheduled periodically to fit the scheduled cluster size using the existing cluster.

Customer review:

Doing a simple data transfer is exactly that -- extremely simple. With only a ten minute overview, we had our first transfer up and working in under two hours. We can create a new one now in minutes. But there is power there when we need it--for transformations, for controlling and monitoring the jobs, for taking a different path due to success, error, or any other scenario we can test for. It\'s the best of both worlds.

Arlene S.

Salesforce Technical Architect

Pros:

• Includes robust monitoring tools, detailed logging, and automated error notifications.
• Offers strong customer support.
• Creates new clusters to meet growing data needs.

Cons:

• Limited interface support for complex data pipelines.
• Has a steep learning curve.
• Lacks straightforwardness while dealing with specialized transformations.

Why choose Integrate.io over AWS DMS?

• Facilitates integration with over 150 pre-built connectors for batch processing.
• Supports aggregate transformations to group the input dataset by one or multiple fields.
• The transparent pricing model enables businesses to predict costs and set budgets.

Pricing:

The platform follows a fixed-fee pricing model. The standard pack is available at $1,999 per month with a 60-second pipeline frequency and full platform access.

5. Keboola

Keboola is a data integration platform with a multi-project architecture that supports its Data Mesh strategy. This strategy promotes customization for unique business needs and use cases. The tool features a command-line interface (CLI) that provides a set of commands for operating cloud data pipelines, which can be installed on Windows, macOS, and Linux environments.

Keboola’s Flow Builder builds a custom automated process to extract or write the code required for performing transformations. Transformations are performed in isolation, referred to as mapping. The mapping process replicates datasets from storage into a staging area to run the transformation scripts. It helps enterprises and SMBs to unify, automate, and govern data across departments.

You can build and deploy data applications in minutes, leverage a marketplace of third-party components, and extend functionality with custom code. With features like AI Flow Builder, error explanations, and component suggestions, Keboola helps accelerate pipeline development and troubleshooting out-of-the-box intelligence.

Key features:

• Multi-tenant architecture: Keboola is designed to support multiple projects and tenants within a single platform, making it suitable for organizations that require granular access control, cost attribution, and resource management across teams or departments.
• Network management: Keboola manages outbound IP addresses for secure access to external systems, provides reverse DNS records for identification, and publishes its IP ranges in the whitelist.
• Interface: Offers a self-service platform with a modular design, enabling users to build, deploy, and manage data pipelines without deep technical expertise or reliance on IT teams.

Customer review:

I have to say that coming across Keboola at the beginning of my career as a Data Analyst was the best experience I could have had. The integration with Snowflake works flawlessly, making the workspaces intuitive. Keboola works even better when a company has strong Data Governance. The Keboola Academy helped me the most at the start, but I still return to it—and as I gain more experience, I continue to get even deeper context from it. I use Keboola almost on a daily basis. I haven’t needed to contact customer support, as my supervisor is able to answer all of my questions.

Zbyněk V.

Data Quality Specialist

Pros:

• Enables rapid development of MVPs and quick setup of data pipelines.
• Offers multi-cloud deployment options.
• AI-powered capabilities accelerate data operations.

Cons:

• Users face a steep learning curve while migrating from another ETL tool.
• The free plan gives access to minimal features.
• Not ideal for niche use-cases.

Why choose Keboola over AWS DMS?

• Keboola offers responsive customer support.
• The platform tracks operational metadata, data lineage, and user activity.
• Keboola’s modular environment provides support for custom connectors and applications.

Pricing:

Starts with a free tier plan that offers 250 GB of data storage, Snowflake backend, SQL, R, and Python transformations. For the enterprise plan, Keboola’s team provides a customized quote based on business needs.

6. Azure Data Factory (ADF)

ADF is Azure’s cloud ETL service for serverless data integration and data transformation. The tool features an intuitive, no-code UI to streamline authorization and a single-pane-of-glass monitoring mechanism for management. ADF is well-known for shifting existing SSIS packages to the native database and running them with full compatibility.

ADF bridges cloud and on-premises environments securely, enabling hybrid data flows without manual networking setup. This flexible deployment manages pipelines that automate data movement and transformation across sources and destinations, including databases, data lakes, SaaS apps, and APIs. It is an optimal choice for data teams looking to build scalable pipelines without managing infrastructure.

ADF’s uniqueness lies in its integration runtime that enables secure, high-performance data movement between on-premises, cloud, and multi-cloud sources. The support for hybrid architectures runs existing SQL Server Integration Services (SSIS) packages in the cloud. This offers a direct migration path for enterprises with legacy ETL investments.

Key features:

• Visual pipeline design: ADF provides a robust, drag-and-drop visual interface for building, configuring, and managing data pipelines. This enables both technical and non-technical users to create complex ETL/ELT workflows without writing code.
• Built-in connectors: Offers over 90 pre-built connectors for a wide range of data sources, making it ideal for hybrid and multi-cloud environments.
• Data transformation: ADF supports code-free data transformations via Mapping Data Flows, as well as advanced, customizable transformations using Azure Databricks, HDInsight, and custom activities.

Customer review:

What I like best about Azure Data Factory is its robust and versatile data integration capabilities. It offers a wide range of connectors and tools to efficiently manage and transform data from various sources. Its user-friendly interface, combined with the flexibility to handle complex workflows, makes it an excellent choice for orchestrating data pipelines. The seamless integration with other Azure services also enhances its functionality, making it a powerful tool for data engineering tasks.

Sowjanya G.

Digital Education Student Ambassador

Pros: 

• Smoothly integrates with the Azure ecosystem.
• Implements data encryption at rest and in transit.
• Provides built-in logging and monitoring tools.

Cons:

• Limited support for complex transformation logic.
• Lacks advanced debugging features.
• Not ideal for integration with non-Azure or third-party tools.

Why choose ADF over AWS DMS?

• ADF supports batch processing, real-time streaming, and hybrid source integration natively.
• Supports schedule-based, event-based triggers, and file arrival triggers without external services.
• Provides an interactive graphical interface with responsive customer support.

Pricing:

It has a pay-as-you-go pricing model where pricing is prorated by the minute for integration runtime usage and is billed per activity and DIU consumed.

7. Apache NiFi

Apache NiFi is a dataflow system that operates on a flow-based programming model. The tool supports comprehensive and scalable directed graphs of system mediation logic, data routing, and transformation. It is highly configurable and provides fine-grained data provenance throughout the ETL pipeline.

NiFi automates the data flow between systems, handling ingestion, transformation, routing, and delivery of data in real-time or batch modes. During the data flow, it allows users to trace and audit every data movement and transformation step. It scales horizontally and is often opted for by organizations working in a multi-cloud environment.

Unlike traditional code-centric ETL tools, NiFi’s drag-and-drop interface allows rapid pipeline design, modification, and monitoring without deep programming expertise. The effortless flow management supports runtime flow modifications, back pressure, prioritization, and error handling, enabling resilient and adaptive pipelines.

Key features:

• Data ingestion: NiFi can ingest data from numerous sources (databases, IoT devices, APIs, files, cloud services), transform it (cleanse, enrich, normalize), and route it to various destinations. This includes real-time and batch processing.
• Flow-based programming: NiFi uses a modular, flow-based programming paradigm, allowing users to construct and modify data workflows by connecting pre-built processors.
• Flow control: Incorporates mechanisms to control data flow rates, apply backpressure, and prioritize queues. This ensures system stability and prevents overload during high-throughput operations.

Customer review:

The best thing about Nifi is that the tools bar is located at convenient place for the user to acces the tools. The drag and drop feature comes handy. The grid offers a perfect measure of components. DAG is represented properly by connecting arrows.

Subham G.

Full Stack Engineer

Pros:

• Developers can extend NiFi by creating their own processors.
• Supports parallel processing for large data volumes.
• Integrates with enterprise security systems like LDAP.

Cons:

• The UI is not ideal for managing large and complex data flows.
• Faces scalability issues with large data flows.
• Lacks a centralized manager to monitor Nifi nodes.

Why choose Apache NiFi over AWS DMS?

• NiFi connects to a wide variety of sources using protocols such as HTTP, FTP, Kafka, MQTT, and SFTP to process diverse data formats.
• Offers robust security through  SSL/TLS encryption, role-based access control, and integration with enterprise security systems.
• Provides a defined toolbar to execute ETL jobs.

Pricing:

Free to use.

8. Matillion

Matillion ETL/ELT tool with a push-down ELT technology that processes complex joins over millions of rows in seconds. The platform is specifically designed for cloud database environments, like Delta Lake, Amazon Redshift, and Google BigQuery.

Matillion delivers push-down ELT via a modern browser UI, using Python and SQL to connect sources and load data into cloud platforms. It offloads transformations into your data warehouse for BI and data science workloads. By streamlining data movement, Matillion enables data engineers to manage pipelines more efficiently and effectively.

Matillion is built for cloud environments, like Google Cloud and Azure, enabling teams to design, test, and deploy ETL jobs seamlessly. The browser-based environment provides built-in features, such as version control, collaboration, and transformation components to facilitate easy execution.

Key features:

• Visual pipeline design: Matillion enables users to build complex data pipelines using a drag-and-drop, browser-based interface. This allows non-technical users and data engineers to design, manage, and monitor data workflows.
• Real-time validation: Matillion offers live data previews, built-in validation, and job monitoring, allowing users to catch errors early and maintain high data quality throughout the pipeline.
• Connectors: It offers over 80 out-of-the-box connectors for SaaS applications, databases, and cloud services, enabling rapid integration with a wide variety of data sources.

Customer review:

What I like best about Matillion is its seamless integration with major cloud platforms like AWS, GCP and Azure. This is very user friendly platform for ETL. It\'s visual interface makes complex workflows look easier. It offers great scalability, making it suitable for big and small scale users. It helps to reduce the complexity of ETL Process with its no code working ability.

Nikhil L.

Data Engineer

Pros:

• Offers over 150 pre-built data integration connectors.
• Provides in-client support with detailed log files.
• Built-in security and management mechanisms.

Cons:

• Not ideal for managing complex data transformations.
• Limited support for CI/CD compatibility.
• Pre-built templates are not entirely user-friendly.

Why choose Matillion over AWS DMS?

• Organizations can easily migrate to the cloud using Matillion.
• Has a very responsive customer support team.
• Built for large-scale batch processing, ideal for handling heavy batch workloads.

Pricing:

• Developer: Free
• Basic: $1,000 /month for 500 prepaid credits
• Advanced: $2,000/month for 750 prepaid credits
• Enterprise: Custom pricing

9. Talend Data Fabric

Talend Data Fabric (owned by Qlik) combines Talend products into a single solution and is considered a complete data integration platform. The data integration solution enables users to access, transform, move, and synchronize bid data by leveraging the Apache Hadoop Big Data Platform.

Talend Data Fabric operates in distinct functional blocks that address key stages of the ETL process. The Talend Studio designs big data jobs through a Hadoop cluster to deploy and execute jobs. Using Talend JobServer, the system generates reports for analysis and shares actionable insights with business users seeking self-service data preparation.

Supports on-premises, cloud, and hybrid deployments, with the ability to include native Spark support for big data workloads. Native support for modern data architecture paradigms facilitates decentralized data ownership and sharing, which makes it a considerable alternative to AWS DMS.

Key features:

• Unified data integration: Talend Data Fabric integrates, cleans, enhances, governs, and delivers data across the entire lifecycle. It handles ETL, ELT, batch, streaming, and API-based integration patterns within a single platform.
• Pipeline development: With a no/low-code interface, Talend enables rapid development of complex data workflows, making it accessible to non-developers and accelerating project delivery.
• Metadata management: Talend includes a data catalog for discovering, cataloging, and managing data assets, as well as integration catalogs for workflows and APIs.

Customer review:

The excellent feature of Talend Data Fabric is to manage every type of data with ease. Its analytics and reporting features are also excellent. Its interface is easy-to-use and its support is great.

Akshay S.

Software Developer Information Technology and Services

Pros:

• Offers flexible deployment options.
• Processes unstructured data by converting it into JSON formats.
• Features a wide range of data connectors.

Cons:

• You might face a steep learning curve.
• Exception handling is not customizable within the platform.
• The enterprise version carries a hefty price tag.

Why choose Talend Data Fabric over AWS DMS?

• Built-in profiling, cleansing, standardization, and enrichment to ensure data is accurate and reliable.
• Provides enterprise-grade security, encryption, and detailed audit logs for sensitive data handling.
• Seamless integration with multiple data sources.

Pricing:

Talend Data Fabric follows a capacity-based pricing model and offers four plans:

• Starter
• Standard
• Premium
• Enterprise

Talend’s team provides a customized quote for each plan.

10. Google Cloud Dataflow

Google Cloud Dataflow is a fully managed service for processing both batch and real-time (streaming) data on Google Cloud. It is built on Apache Beam, providing a unified programming model that allows the same codebase to handle both types of data workloads without separate development tracks.

Google Cloud Dataflow enables users to build, deploy, and manage data processing pipelines that can ingest, transform, and analyze data from a wide range of sources. The process includes visual pipeline monitoring to identify bottlenecks and automate recommendations for helping data engineers simplify ETL workflows.

Dataflow allows you to write a single pipeline for both batch and streaming data, increasing code reusability and reducing maintenance. The single pipeline framework introduces advanced autoscaling that supports both batch and streaming data, adapting to evolving workload needs.

Key features:

• ML integration: Dataflow offers turnkey capabilities, including built-in support for running inference, pre-processing for model training, and dynamic GPU allocation for ML workloads. This supports Gen AI models for real-time predictions and enrichment.
• Multimodal data processing: Dataflow can ingest and transform multimodal data (images, text, audio) in parallel ingestion, extract features for each modality, and fuse them for use in generative AI models.
• Monitoring: Offers rich monitoring and diagnostics, including straggler detection, data sampling at each stage, Dataflow Insights, and detailed job graphs and execution metrics.

Customer review:

Google Cloud Dataflow is extremely easy to use for processing stream of events. Building complex streaming pipelines is simple and effiicent with Dataflow. Offers real time monitoring of the streaming pipeline with important metrics such as Throughput, CPU and memory utilisation.

Sanyam G

Software Engineer

Pros:

• Provides enterprise-grade security and compliance.
• Workloads are portable across clouds and on-premises.
• Support advanced streaming use cases at scale.

Cons:

• Debugging capabilities aren’t robust.
• Deployment of VMs is glitchy.
• Requires technical expertise to operate.

Why choose Google Cloud Dataflow over AWS DMS?

• Dataflow is purpose-built for high-throughput, low-latency data processing and analytics.
• Offers data encryption, IAM, and audit logging specifically for data processing.
• Backed by a large community for instant support and contribution.

Pricing:

Google Cloud Dataflow pricing is usage-based: you pay per vCPU-hour, and for data shuffled or streamed.

Factors to Consider When Choosing an AWS DMS Alternative

Here’s what to consider while choosing an AWS DMS alternative:

1. Fully-managed

Managed tools like Hevo, Matillion, or Fivetran remove the need for maintenance, upgrades, and scaling, which can save engineering time. When deciding, evaluate control, flexibility, ease of use, and operational simplicity. Managed platforms often offer SLAs and support plans, which are critical for production pipelines.

2. Pipeline orchestration

Choose alternatives that provide a graphical interface, dynamic DAG generation, or flow-based visual programming to manage complex workflows at scale.

3. Integration capabilities

Examine whether the tool supports different data sources (like APIs, databases, file systems, and cloud storage), as well as destinations (like data warehouses, lakes, or BI platforms). 

Also, check for real-time and batch processing, the ability to connect to on-premise systems, and compatibility with SaaS tools.

4. Observability

Choose a platform that offers detailed operational logs, real-time metrics, custom alerts, and ideally a UI for flow inspection and debugging. 

Platforms like Hevo include real-time monitoring dashboards and custom alerting features to trace job execution, monitor retries, and debug failures more effectively.

5. Scalability

Assess whether the tool supports horizontal scaling, parallel job execution, and resource auto-allocation. 

For batch-heavy workloads, ensure that the system handles large data volumes efficiently without bottlenecks. For streaming or real-time data, verify latency metrics and throughput limits.

Streamline Your Data Pipelines With Hevo

Finalizing a particular tool among the leading choices is challenging. From ensuring broad connectivity, faster deployment, to detailed monitoring, you must choose a tool that offers robust solutions and scales with evolving business needs.

If you require a no-code ETL platform, consider Hevo a compelling alternative. With native support for 150+ data sources, real-time replication, and built-in transformation capabilities, Hevo streamlines every step of your pipeline. 

Its intuitive interface, automatic schema mapping, and proactive error handling ensure that your data flows are not only fast but also reliable.

Sign up for Hevo’s 14-day free trial and experience flawless data integration at scale.

Frequently Asked Questions About AWS DMS Alternatives