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AWS Glue VS Hadoop

Compare AWS Glue VS Hadoop and see what are their differences

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AWS Glue logo AWS Glue

Fully managed extract, transform, and load (ETL) service

Hadoop logo Hadoop

Open-source software for reliable, scalable, distributed computing
  • AWS Glue Landing page
    Landing page //
    2022-01-29
  • Hadoop Landing page
    Landing page //
    2021-09-17

AWS Glue features and specs

  • Fully Managed
    AWS Glue is a fully managed ETL (Extract, Transform, Load) service, which means you don't need to manage any underlying infrastructure. This reduces the operational overhead and allows you to focus on the data processing tasks.
  • Scalability
    AWS Glue can automatically scale resources up or down based on the demand and workload, ensuring optimal performance without manual intervention.
  • Serverless
    Being serverless, there are no servers to manage or maintain. You only pay for the resources that you consume, which can result in significant cost savings.
  • Integrated Data Catalog
    AWS Glue comes with a built-in data catalog that helps you organize and discover your data. It automatically indexes and maintains metadata about your data, making it easier to manage.
  • Support for Multiple Data Sources
    AWS Glue supports a variety of data sources including Amazon S3, RDS, Redshift, and many external databases, providing flexibility in your ETL processes.
  • Developer Tools
    AWS Glue provides developer endpoints for custom ETL logic, and integrates with AWS SDKs, Boto3, and the AWS CLI, allowing for a flexible development experience.

Possible disadvantages of AWS Glue

  • Complex Pricing
    The pricing model for AWS Glue can be complicated, involving multiple components such as Data Processing Units (DPUs), data catalog storage, and crawler costs, which may make it hard to estimate costs.
  • Learning Curve
    There is a significant learning curve for developers who are new to AWS Glue, especially when it comes to understanding its various components and configurations.
  • Performance for Small Datasets
    AWS Glue is optimized for large-scale data processing, which may result in suboptimal performance and higher costs for smaller datasets.
  • Vendor Lock-in
    Using AWS Glue ties you to the AWS ecosystem, making it harder to switch to another cloud provider without significant rework of your ETL pipelines and data catalog.
  • Limited Debugging Tools
    The debugging and troubleshooting tools for AWS Glue are somewhat limited compared to other mature ETL tools, which may complicate the development and maintenance of ETL jobs.
  • Job Run Delays
    There can be delays in job startup times, which can be problematic for certain time-sensitive applications requiring near real-time data processing.

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.

Analysis of AWS Glue

Overall verdict

  • AWS Glue is generally considered a good option for organizations looking for a powerful, scalable, and cost-effective ETL solution within the AWS ecosystem. Its ease of integration with AWS services, managed nature, and capability to handle large volumes of data make it a strong choice, particularly for teams that are already using AWS services.

Why this product is good

  • AWS Glue is a fully managed ETL (Extract, Transform, Load) service that makes it easy to prepare and transform data for analytics, machine learning, and application development. It is particularly beneficial for its serverless architecture, which allows users to run data processing jobs without the need to manage any infrastructure. The service integrates seamlessly with other AWS services like S3, RDS, and Redshift, providing a robust ecosystem for data processing. It also supports a wide range of data sources and formats, and offers a graphical interface for easy job creation and monitoring.

Recommended for

  • Organizations already using AWS services and looking to streamline their ETL processes.
  • Data engineers and developers who need a scalable solution to handle large datasets without managing infrastructure.
  • Companies that require seamless integration with a wide array of data storage options and formats.

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.

AWS Glue videos

+ Add

Build ETL Processes for Data Lakes with AWS Glue - AWS Online Tech Talks

More videos:

  • Review - AWS re:Invent BDT 201: AWS Data Pipeline: A guided tour
  • Review - Getting Started with AWS Glue Data Catalog
  • Review - Bajaj Housing Finance Limited: Serverless Data Pipelines with AWS Glue and Amazon Aurora PGSQL

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

Category Popularity

0-100% (relative to AWS Glue and Hadoop)

AWS Glue

Hadoop

ETL
100 100%
ETL
0% 0
Databases
0 0%
Databases
100% 100
Data Integration
100 100%
Data Integration
0% 0
Big Data
0 0%
Big Data
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 AWS Glue and Hadoop

AWS Glue Reviews

Best ETL Tools: A Curated List
AWS Glue is a fully managed serverless ETL service from Amazon Web Services (AWS) designed to automate and simplify the data preparation process for analytics. Its serverless architecture eliminates the need to manage infrastructure. As part of the AWS ecosystem, it is integrated with other AWS services, making it a go-to choice for cloud-based data integration for...
Source: estuary.dev
10 Best ETL Tools (October 2023)
AWS Glue is an end-to-end ETL offering intended to make ETL workloads easier and more integratable with the larger AWS ecosystem. One of the more unique aspects of the tool is that it is serverless, meaning Amazon automatically provisions a server and shuts it down following the completion of the workload.
Source: www.unite.ai
15+ Best Cloud ETL Tools
AWS Glue is a serverless data integration service designed to streamline analytics, machine learning, and app development tasks. It discovers, prepares, and moves data from a myriad of sources and offers a seamless integration experience. AWS Glue's inclusive toolset and automatic scaling let you focus on gaining insights from data instead of managing infrastructure.
Source: estuary.dev
Top 14 ETL Tools for 2023
Notably, AWS Glue is serverless, which means that Amazon automatically provisions a server for users and shuts it down when the workload is complete. AWS Glue also includes features such as job scheduling and “developer endpoints” for testing AWS Glue scripts, improving the tool’s ease of use.
Source: www.integrate.io
A List of The 16 Best ETL Tools And Why To Choose Them
Better yet, when interacting with AWS Glue, practitioners can choose between a drag-and-down GUI, a Jupyter notebook, or Python/Scala code. AWS Glue also offers support for various data processing and workloads that meet different business needs, including ETL, ELT, batch, and streaming.
Source: www.datacamp.com

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

Social recommendations and mentions

Based on our record, Hadoop should be more popular than AWS Glue. It has been mentiond 26 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 Glue mentions (16)

  • Optimizing AWS Costs for AI Development in 2025
    Managed Services: This includes the per-token costs of using services like Amazon Bedrock, the hosting fees for SageMaker endpoints, and the costs associated with data pipelines using services like Glue or Lambda. - Source: dev.to / about 2 months ago
  • Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed
    However, using any Iceberg engine traditionally requires a first, crucial step: setting up and configuring an Iceberg catalog. This catalog is responsible for managing the table metadata. While flexible, this often means provisioning and managing a separate service like AWS Glue, a dedicated PostgreSQL database for the JDBC catalog, or a REST service. This adds an extra layer of configuration and operational... - Source: dev.to / 3 months ago
  • Vector: A lightweight tool for collecting EKS application logs with long-term storage capabilities
    In this article, we present an architecture that demonstrates how to collect application logs from Amazon Elastic Kubernetes Service (Amazon EKS) via Vector, store them in Amazon Simple Storage Service (Amazon S3) for long-term retention, and finally query these logs using AWS Glue and Amazon Athena. - Source: dev.to / 5 months ago
  • Build Your Movie Recommendation System Using Amazon Personalize, MongoDB Atlas, and AWS Glue
    AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analysis. It helps bridge the gap between our MongoDB Atlas data and the services we'll use for recommendation. - Source: dev.to / over 1 year ago
  • Using Snowflake data hosted in GCP with AWS Glue
    AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services (AWS). It is designed to make it easy for users to prepare and load their data for analysis. AWS Glue simplifies the process of building and managing ETL workflows by providing a serverless environment for running ETL jobs. - Source: dev.to / over 1 year ago
View more

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

What are some alternatives?

When comparing AWS Glue and Hadoop, you can also consider the following products

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

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.

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

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

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

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