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Hadoop VS AWS Database Migration Service

Compare Hadoop VS AWS Database Migration Service and see what are their differences

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Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing

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.
  • Hadoop Landing page
    Landing page //
    2021-09-17
  • AWS Database Migration Service Landing page
    Landing page //
    2022-01-30

Hadoop features and specs

  • Scalability
    Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
  • Cost-Effective
    It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
  • Fault Tolerance
    Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
  • Flexibility
    It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
  • Parallel Processing
    Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
  • Community Support
    As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

  • Complexity
    Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
  • Overhead
    The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
  • Security
    While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
  • Hardware Requirements
    Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
  • Lack of Real-Time Processing
    Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
  • Data Integrity
    Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

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.

Analysis of Hadoop

Overall verdict

  • Hadoop is a robust and powerful data processing platform that is well-suited for organizations that need to manage and analyze large-scale data. Its resilience, scalability, and open-source nature make it a popular choice for big data solutions. However, it may not be the best fit for all use cases, especially those requiring real-time processing or where ease of use is a priority.

Why this product is good

  • Hadoop is renowned for its ability to store and process large datasets using a distributed computing model. It is scalable, cost-effective, and efficient in handling massive volumes of data across clusters of computers. Its ecosystem includes a wide range of tools and technologies like HDFS, MapReduce, YARN, and Hive that enhance data processing and analysis capabilities.

Recommended for

  • Organizations dealing with vast amounts of data needing efficient batch processing.
  • Businesses that require scalable storage solutions to manage their data growth.
  • Companies interested in leveraging a diverse ecosystem of data processing tools and technologies.
  • Technical teams that have the expertise to manage and optimize complex distributed systems.

Analysis of AWS Database Migration Service

Overall verdict

  • Overall, AWS Database Migration Service is a reliable and efficient tool for migrating databases to the cloud, especially within the AWS ecosystem. Its flexibility, along with support for various database scenarios, makes it a worthwhile option for organizations looking to modernize their data infrastructure.

Why this product is good

  • AWS Database Migration Service (AWS DMS) is considered a good option for database migrations due to its ease of use, cost-effectiveness, and reliability. It supports a wide range of database engines and allows for seamless data migration with minimal downtime. The service enables continuous data replication, making it suitable for live migrations. Additionally, AWS DMS is fully managed, which means users don't need to worry about the underlying infrastructure, and it offers robust security features.

Recommended for

  • Organizations seeking to migrate their on-premises databases to AWS with minimal downtime.
  • Businesses looking to replicate their data across different regions or availability zones.
  • Users who require a scalable and managed database migration solution.
  • Enterprises wanting to transform their database infrastructure into a cloud-native architecture.
  • Teams that need to conduct continuous data replication from source to target databases.

Hadoop videos

+ Add

What is Big Data and Hadoop?

More videos:

  • Review - Product Ratings on Customer Reviews Using HADOOP.
  • Tutorial - Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank Kane

AWS Database Migration Service videos

+ Add

AWS Database Migration Service (DMS)

Category Popularity

0-100% (relative to Hadoop and AWS Database Migration Service)

Hadoop

AWS Database Migration Service

Databases
100 100%
Databases
0% 0
Data Integration
0 0%
Data Integration
100% 100
Big Data
100 100%
Big Data
0% 0
ETL
0 0%
ETL
100% 100

User comments

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Reviews

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

Hadoop Reviews

A List of The 16 Best ETL Tools And Why To Choose Them
Companies considering Hadoop should be aware of its costs. A significant portion of the cost of implementing Hadoop comes from the computing power required for processing and the expertise needed to maintain Hadoop ETL, rather than the tools or storage themselves.
Source: www.datacamp.com
16 Top Big Data Analytics Tools You Should Know About
Hadoop is an Apache open-source framework. Written in Java, Hadoop is an ecosystem of components that are primarily used to store, process, and analyze big data. The USP of Hadoop is it enables multiple types of analytic workloads to run on the same data, at the same time, and on a massive scale on industry-standard hardware.
Source: www.analytixlabs.co.in
5 Best-Performing Tools that Build Real-Time Data Pipeline
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than relying on hardware to deliver high-availability, the library itself is...
Source: www.analyticsinsight.net

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

Social recommendations and mentions

AWS Database Migration Service might be a bit more popular than Hadoop. We know about 31 links to it since March 2021 and only 26 links to Hadoop. 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.

Hadoop mentions (26)

  • JuiceFS 1.3 Beta 2 Integrates Apache Ranger for Fine-Grained Access Control
    To simplify ​​fine-grained permission management​​ and enable centralized ​​web-based administration​​, JuiceFS now supports ​​Apache Ranger​​, a widely adopted security framework in the Hadoop ecosystem. - Source: dev.to / 4 months ago
  • Apache Hadoop: Open Source Business Model, Funding, and Community
    This post provides an in‐depth look at Apache Hadoop, a transformative distributed computing framework built on an open source business model. We explore its history, innovative open funding strategies, the influence of the Apache License 2.0, and the vibrant community that drives its continuous evolution. Additionally, we examine practical use cases, upcoming challenges in scaling big data processing, and future... - Source: dev.to / 5 months ago
  • What is Apache Kafka? The Open Source Business Model, Funding, and Community
    Modular Integration: Thanks to its modular approach, Kafka integrates seamlessly with other systems including container orchestration platforms like Kubernetes and third-party tools such as Apache Hadoop. - Source: dev.to / 5 months ago
  • India Open Source Development: Harnessing Collaborative Innovation for Global Impact
    Over the years, Indian developers have played increasingly vital roles in many international projects. From contributions to frameworks such as Kubernetes and Apache Hadoop to the emergence of homegrown platforms like OpenStack India, India has steadily carved out a global reputation as a powerhouse of open source talent. - Source: dev.to / 5 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 7 months ago
View more

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 / 10 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 / over 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 / over 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: about 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: over 2 years ago
View more

What are some alternatives?

When comparing Hadoop and AWS Database Migration Service, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

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.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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

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