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AWS Database Migration Service VS Google Cloud Dataflow

Compare AWS Database Migration Service VS Google Cloud Dataflow and see what are their differences

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  2. » Videos
  3. » Reviews
  4. » Alternatives

AWS Database Migration Service logo AWS Database Migration Service

AWS Database Migration Service allows you to migrate to AWS quickly and securely. Learn more about the benefits and the key use cases.

Google Cloud Dataflow logo Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
  • AWS Database Migration Service Landing page
    Landing page //
    2022-01-30
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

AWS Database Migration Service features and specs

  • Minimal Downtime
    AWS Database Migration Service ensures minimal downtime during the database migration process, making it ideal for applications that require continuous availability.
  • Supports Multiple Database Engines
    It supports migration of data between a wide variety of database engines including Oracle, Microsoft SQL Server, MySQL, MariaDB, PostgreSQL, and more.
  • Cost-Effective
    With a pay-as-you-go pricing model, users only pay for the compute resources used during the migration process, making it a cost-effective solution.
  • Managed Service
    As a fully managed service, it reduces the administrative overhead associated with database migrations, including hardware provisioning, software patching, and monitoring.
  • Continuous Data Replication
    It supports continuous data replication with high availability, allowing for nearly real-time data synchronization between the source and target databases.

Possible disadvantages of AWS Database Migration Service

  • Complex Initial Setup
    The initial setup and configuration can be complex, especially for users who are not familiar with AWS services and database migration processes.
  • Limited Customization
    Being a managed service, it offers limited customization options compared to self-managed solutions, which might be a drawback for users with specific requirements.
  • Latency Issues
    For large datasets, there might be latency issues during migration, depending on the network conditions and the geographical locations of the source and target databases.
  • Dependency on AWS Ecosystem
    The service is tightly integrated with AWS, which means it may not be as effective or easy to use with non-AWS environments, creating potential vendor lock-in.
  • Performance Overheads
    There may be performance overheads associated with running the migration tasks, which could impact the performance of the source or target databases during the migration process.

Google Cloud Dataflow features and specs

  • Scalability
    Google Cloud Dataflow can automatically scale up or down depending on your data processing needs, handling massive datasets with ease.
  • Fully Managed
    Dataflow is a fully managed service, which means you don't have to worry about managing the underlying infrastructure.
  • Unified Programming Model
    It provides a single programming model for both batch and streaming data processing using Apache Beam, simplifying the development process.
  • Integration
    Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Bigtable.
  • Real-time Analytics
    Supports real-time data processing, enabling quicker insights and facilitating faster decision-making.
  • Cost Efficiency
    Pay-as-you-go pricing model ensures you only pay for resources you actually use, which can be cost-effective.
  • Global Availability
    Cloud Dataflow is available globally, which allows for regionalized data processing.
  • Fault Tolerance
    Built-in fault tolerance mechanisms help ensure uninterrupted data processing.

Possible disadvantages of Google Cloud Dataflow

  • Steep Learning Curve
    The complexity of using Apache Beam and understanding its model can be challenging for beginners.
  • Debugging Difficulties
    Debugging data processing pipelines can be complex and time-consuming, especially for large-scale data flows.
  • Cost Management
    While it can be cost-efficient, the costs can rise quickly if not monitored properly, particularly with real-time data processing.
  • Vendor Lock-in
    Using Google Cloud Dataflow can lead to vendor lock-in, making it challenging to migrate to another cloud provider.
  • Limited Support for Non-Google Services
    While it integrates well within Google Cloud, support for non-Google services may not be as robust.
  • Latency
    There can be some latency in data processing, especially when dealing with high volumes of data.
  • Complexity in Pipeline Design
    Designing pipelines to be efficient and cost-effective can be complex, requiring significant expertise.

AWS Database Migration Service videos

+ Add

AWS Database Migration Service (DMS)

Google Cloud Dataflow videos

+ Add

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Category Popularity

0-100% (relative to AWS Database Migration Service and Google Cloud Dataflow)

AWS Database Migration Service

Google Cloud Dataflow

Data Integration
100 100%
Data Integration
0% 0
Big Data
0 0%
Big Data
100% 100
ETL
100 100%
ETL
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

Share your experience with using AWS Database Migration Service and Google Cloud Dataflow. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare AWS Database Migration Service and Google Cloud Dataflow

AWS Database Migration Service Reviews

Best ETL Tools: A Curated List
Mostly Batch: Matillion ETL had some real-time CDC based on Amazon DMS that has been deprecated. The Data Loader does have some CDC, but overall, the Data Loader is limited in functionality, and if it’s based on DMS, it will have the limitations of DMS as well.
Source: estuary.dev

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Social recommendations and mentions

Based on our record, AWS Database Migration Service should be more popular than Google Cloud Dataflow. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

AWS Database Migration Service mentions (31)

  • Choosing the right, real-time, Postgres CDC platform
    The major infrastructure providers offer CDC products that work within their ecosystem. Tools like AWS DMS, GCP Datastream, and Azure Data Factory can be configured to stream changes from Postgres to other infrastructure. - Source: dev.to / 6 months ago
  • 3 Proven Patterns for Reporting with Serverless
    The second big drawback is speed. There will be more latency in this scenario. How much latency depends upon the environment. If there is RDBMS in the source, AWS Data Migration Service will at worst take around 60 seconds to replicate. That cost needs to be accounted for. Secondarily, many triggering events are leveraged which happen fairly quickly but they do add up. - Source: dev.to / about 1 year ago
  • RDS Database Migration Series - A horror story of using AWS DMS with a happy ending
    Amazon Database Migration Service might initially seem like a perfect tool for a smooth and straightforward migration to RDS. However, our overall experience using it turned out to be closer to an open beta product rather than a production-ready tool for dealing with a critical asset of any company, which is its data. Nevertheless, with the extra adjustments, we made it work for almost all our needs. - Source: dev.to / about 1 year ago
  • Aurora serverless v1 to v2 upgrade pointers?
    Does AWS DMS make sense here? Doesn't the aforementioned "snapshot+restore to provisioned and upgrade" method suffice? I wanted to get some opinions before deep diving into the docs for yet another AWS service. Source: almost 2 years ago
  • Using Amazon RDS Postgres as a read replica from an external Database
    One easy solution is AWS DMS. I use it for on-going CDC replication with custom transforms, but you can use it for simple replication too. Source: about 2 years ago
View more

Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
View more

What are some alternatives?

When comparing AWS Database Migration Service and Google Cloud Dataflow, you can also consider the following products

AWS Glue - Fully managed extract, transform, and load (ETL) service

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Xplenty - Xplenty is the #1 SecurETL - allowing you to build low-code data pipelines on the most secure and flexible data transformation platform. No longer worry about manual data transformations. Start your free 14-day trial now.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Skyvia - Free cloud data platform for data integration, backup & management

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

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