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Apache Hive VS AWS Glue

Compare Apache Hive VS AWS Glue and see what are their differences

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Apache Hive logo Apache Hive

Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

AWS Glue logo AWS Glue

Fully managed extract, transform, and load (ETL) service
  • Apache Hive Landing page
    Landing page //
    2023-01-13
  • AWS Glue Landing page
    Landing page //
    2022-01-29

Apache Hive features and specs

  • Scalability
    Apache Hive is built on top of Hadoop, allowing it to efficiently handle large datasets by distributing the load across a cluster of machines.
  • SQL-like Interface
    Hive provides a familiar SQL-like querying language, HiveQL, which makes it easier for users with SQL knowledge to perform data analysis on large datasets without needing to learn a new syntax.
  • Integration with Hadoop Ecosystem
    Hive integrates seamlessly with other components of the Hadoop ecosystem such as HDFS for storage and MapReduce for processing, making it a versatile tool for big data processing.
  • Schema on Read
    Hive uses a schema-on-read model which allows it to work with flexible data schemas and handle unstructured or semi-structured data efficiently.
  • Extensibility
    Users can extend Hive's capabilities by writing custom UDFs (User Defined Functions), UDAFs (User Defined Aggregate Functions), and SerDes (Serializers/ Deserializers).

Possible disadvantages of Apache Hive

  • Latency in Query Processing
    Queries in Hive often take longer to execute compared to traditional databases, as they are converted to MapReduce jobs which can introduce significant latency.
  • Limited Real-time Processing
    Hive is designed for batch processing and is not suitable for real-time analytics due to its reliance on MapReduce, which is not optimized for low-latency operations.
  • Complex Configuration
    Setting up Hive and configuring it to work optimally within a Hadoop cluster can be complex and require a significant amount of effort and expertise.
  • Lack of Support for Transactions
    Hive does not natively support full ACID transactions, which can be a limitation for applications that require consistent transaction management across large datasets.
  • Dependency on Hadoop
    Hive's reliance on the Hadoop ecosystem means it inherits some of Hadoop's limitations, such as a steep learning curve and the need for substantial resources to manage a cluster.

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.

Apache Hive videos

+ Add

Hive vs Impala - Comparing Apache Hive vs Apache Impala

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

Category Popularity

0-100% (relative to Apache Hive and AWS Glue)

Apache Hive

AWS Glue

Databases
100 100%
Databases
0% 0
ETL
0 0%
ETL
100% 100
Big Data
100 100%
Big Data
0% 0
Data Integration
0 0%
Data Integration
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 Apache Hive and AWS Glue

Apache Hive Reviews

We have no reviews of Apache Hive yet.
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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

Social recommendations and mentions

Based on our record, AWS Glue should be more popular than Apache Hive. It has been mentiond 14 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.

Apache Hive mentions (8)

View more

AWS Glue mentions (14)

  • 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 / 15 days 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 / about 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
  • How to check for quality? Evaluate data with AWS Glue Data Quality
    It is serverless data integration service to allow you to easily scale your workloads in preparing data and moving transformed data into a target location. - Source: dev.to / almost 2 years ago
  • Deploying a Data Warehouse with Pulumi and Amazon Redshift
    So in the next post, we'll do that: We'll take what we've done here, add a few more components with Pulumi and AWS Glue, and wire it all up with a few magical lines of Python scripting. - Source: dev.to / over 2 years ago
View more

What are some alternatives?

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

Apache Doris - Apache Doris is an open-source real-time data warehouse for big 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.

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

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.

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

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

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