AWS Glue Reviews & Pricing

by Amazon Web Services (AWS) | Founded 1994, Bellevue, WA

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Overview Pricing Benefits Reviews Features

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What is AWS Glue?

Industry Specialties: Serves all industries

AWS Glue is a fully managed, event-driven serverless computing platform that extracts, cleanses and organizes data for insights. Automatic code generation ensures citizen data scientists and power users can create and schedule integration workflows. An event-driven architecture enables setting triggers to launch data integration processes.

A common data catalog with automatic schema generation ensures data is unique and easily accessible. With streaming data integration, it catalogs assets from datastores like Amazon S3, making it available for querying with Amazon Athena and Redshift Spectrum. Developers can access readymade endpoints to edit and test code.

Pros

Serverless & Scalable
Easy Visual Workflow
Built-in Data Connectors
Pay-per-Use Pricing
AWS Ecosystem Integration

Cons

Complex Transformations
Limited On-Premise Data
Python & Scala Only
Potential Cost Overruns
AWS Lock-in Concerns

PRICE

COMPANY SIZE

DEPLOYMENT

PLATFORM

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User Sentiment

Based on 165 reviews:

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AWS Glue Pricing

Based on our most recent analysis, AWS Glue pricing starts at $0 (Per M-DPU-Hour).

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Starting From: $0.44
Pricing Model: Per M-DPU-Hour
Free Trial: No

Training Resources

AWS Glue is supported with the following types of training:

Documentation
In Person
Live Online
Videos
Webinars

Support

The following support services are available for AWS Glue:

Email
Phone
Chat
FAQ
Forum
Help Desk
Knowledge Base
Tickets
Training
24/7 Live Support

AWS Glue Benefits and Insights

Why use AWS Glue?

Key differentiators & advantages of AWS Glue

Effortless Data Integration: Streamline data movement across diverse sources like databases, applications, and cloud storage with pre-built connectors and automated schema discovery.
Simplified Data Preparation: Clean, transform, and enrich data with a visual drag-and-drop interface and built-in transformations, eliminating the need for complex coding.
Serverless Scalability: Forget infrastructure management! Glue seamlessly scales to handle massive data volumes without upfront provisioning or ongoing maintenance.
Cost-Effective Flexibility: Pay-per-use pricing based on actual resource consumption makes Glue ideal for both small and large data pipelines, optimizing your costs.
Seamless AWS Integration: Leverage the power of the AWS ecosystem! Glue effortlessly integrates with S3, Redshift, and other AWS services, creating a unified data pipeline within your existing infrastructure.
Improved Data Accessibility: Deliver prepared data to data lakes, data warehouses, and analytics platforms, democratizing access for data scientists, analysts, and business users.
Enhanced Collaboration: Share data pipelines and workflows with other users and teams, fostering collaboration and streamlining data-driven workflows.
Centralized Data Catalog: Maintain a single source of truth for your data assets with Glue Data Catalog, ensuring data consistency and discoverability.
Continuous Monitoring and Optimization: Track job performance, identify bottlenecks, and optimize your pipelines for efficiency with built-in monitoring and logging tools.
Future-Proof Data Infrastructure: Stay ahead of the curve with Glue's serverless architecture and cloud-native approach, adapting to your evolving data needs with ease.

Industry Expertise

AWS Glue provides data integration to multiple clients in enterprises globally. Some of these are healthcare, travel and hospitality, catering, agriculture and life sciences.

AWS Glue Reviews

Average customer reviews & user sentiment summary for AWS Glue:

165 reviews

85%

of users would recommend this product

Synopsis of User Ratings and Reviews

Based on an aggregate of AWS Glue reviews taken from the sources above, the following pros & cons have been curated by a SelectHub Market Analyst.

Pros

Cost-Effective & Serverless: Pay only for resources used, eliminates server provisioning and maintenance
Simplified ETL workflows: Drag-and-drop UI & auto-generated code for easy job creation, even for non-programmers
Data Catalog: Unified metadata repository for seamless discovery & access across various data sources
Flexible Data Integration: Connects to diverse data sources & destinations (S3, Redshift, RDS, etc.)
Built-in Data Transformations: Apply pre-built & custom transformations within workflows for efficient data cleaning & shaping
Visual Data Cleaning (Glue DataBrew): Code-free data cleansing & normalization for analysts & data scientists
Scalability & Performance: Auto-scaling resources based on job needs, efficient Apache Spark engine for fast data processing
Community & Support: Active user community & helpful AWS support resources for problem-solving & best practices

Cons

Limited Customization & Control: Visual interface and pre-built transformations may not be flexible enough for complex ETL needs, requiring manual coding or custom Spark jobs.
Debugging Challenges: Troubleshooting Glue jobs can be complex due to limited visibility into underlying Spark code and distributed execution, making error resolution time-consuming.
Performance Limitations for Certain Workloads: Serverless architecture may not be optimal for latency-sensitive workloads or large-scale data processing, potentially leading to bottlenecks.
Vendor Lock-in & Portability: Migrating ETL workflows from Glue to other platforms can be challenging due to its proprietary nature and lack of open-source compatibility.
Pricing Concerns for Certain Use Cases: Pay-per-use model can be expensive for long-running ETL jobs or processing massive datasets, potentially exceeding budget constraints.

Researcher's Summary:

User reviews of AWS Glue paint a picture of a powerful and user-friendly ETL tool for the cloud, but one with limitations. Praise often centers around its intuitive visual interface, making complex data pipelines accessible even to non-programmers. Pre-built connectors and automated schema discovery further simplify setup, saving users time and effort. Glue's serverless nature and tight integration with the broader AWS ecosystem are also major draws, offering seamless scalability and data flow within a familiar environment. However, some users find Glue's strength in simplicity a double-edged sword. For complex transformations beyond basic filtering and aggregation, custom scripting in Python or Scala is required, limiting flexibility for those unfamiliar with these languages. On-premise data integration is another pain point, with Glue primarily catering to cloud-based sources. This leaves users seeking hybrid deployments or integration with legacy systems feeling somewhat stranded. Cost also arises as a concern. Glue's pay-per-use model can lead to unexpected bills for large data volumes or intricate pipelines, unlike some competitors offering fixed monthly subscriptions. Additionally, Glue's deep integration with AWS can create lock-in anxieties for users worried about switching cloud providers in the future. Overall, user reviews suggest Glue shines in cloud-based ETL for users comfortable with its visual interface and scripting limitations. Its scalability, ease of use, and AWS integration are undeniable strengths. However, for complex transformations, on-premise data needs, or cost-conscious users, alternative tools may offer a better fit.

Key Features

Console: Discover, transform and make available data assets for querying and analysis. Builds complex data integration pipelines; handles dependencies, filters bad data and retries jobs after failures. Monitor jobs and get task status alerts via Amazon Cloudwatch.
Data Catalog: Gleans and stores metadata in the catalog for workflow authoring, with full version history. Search and discover desired datasets from the data catalog, irrespective of where they are located. Saves time and money – automatically computes statistics and registers partitions with a central metadata repository.
Automatic Schema Discovery: Creates metadata automatically by gleaning schema, quality and data types through built-in datastore crawlers and stores it in the Data Catalog. Ensure up-to-date assets – run crawlers on a schedule, on-demand or based on event triggers. Manage streaming data schemas with the Schema Registry.
Event-driven Architecture: Move data automatically into data lakes and warehouses by setting triggers based on a schedule or event. Extract, transform and load jobs with a Lambda function as soon as new data becomes available.
Visual Data Prep: Prepare assets for analytics and machine learning through Glue DataBrew. Automate anomaly filtering, convert data to standard formats and rectify invalid values with more than 250 pre-designed transformations – no need to write code.
Materialized Views: Create a virtual table from multiple different data sources by using SQL. Copies data from each source data store and creates a replica in the target datastore as a materialized view. Ensures data is always up-to-date by monitoring data in source stores continuously and updating target stores in real time.

Limitations

At the time of this review, these are the limitations according to user feedback:

Isn’t beginner-friendly; requires technical knowledge.
Supports limited data sources.
Doesn’t support conventional RDBMS systems.
Isn’t easy to use with products other than the AWS suite.
Can’t show real-time data for complex operations since it lacks incremental data sync with the source.

Suite Support

Go through technical documentation, the knowledge center, support forums, user guides and tutorials available on the vendor’s website for self-service issue resolution and answers to queries.

Avail Basic support that includes access to the resource center, service health dashboard, product FAQs, discussion forums and support for health checks for no additional cost. Sign up for the Developer Support plan for 24/7 access to customer service, with email support during business hours.

Subscribe to the Business Support plan or above for 24/7 access to support via email, phone and chat, with the Personal Health Dashboard, Trusted Advisor and third-party software support. Get Enterprise Support for access to a dedicated Technical Account Manager (TAM) for onboarding and subject matter experts.

Email: Not specified.

Phone: Not available. Phone callbacks are available to licensed subscribers only.

Training: Register for free, instructor-led courses, either virtually or in-person, on the vendor’s website. Or sign up for self-paced paid online courses from digital training providers.

Tickets: Create a case from the Support Center through a subscriber account.