AWS Glue vs IDMC

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Our analysts compared AWS Glue vs IDMC based on data from our 400+ point analysis of ETL Tools, user reviews and our own crowdsourced data from our free software selection platform.

Product Basics

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
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Informatica IDMC is a cloud-based ETL (Extract, Transform, Load) tool designed to simplify data integration for businesses of all sizes. It excels in user-friendliness, offering a drag-and-drop interface and visual mapping tools, making it accessible to users with limited coding experience. This ease of use positions it well for mid-sized and larger companies looking for a scalable solution to manage complex data pipelines. IDMC boasts robust data integration capabilities, seamlessly connecting to diverse sources like databases, cloud applications, and file formats. Its built-in data cleansing and validation features ensure data accuracy and reliability, crucial for data-driven decision-making. Users also appreciate its collaboration tools, facilitating teamwork and knowledge sharing among data professionals. However, IDMC's strengths come with caveats. Its cloud-native nature, while convenient, can lock users into the Informatica ecosystem, potentially hindering migration to other cloud providers. Additionally, its pricing follows a consumption-based model, making it difficult to pinpoint a fixed cost. While potentially cost-effective for high-volume users, smaller organizations might find the upfront IPU purchase and ongoing consumption fees prohibitive. Ultimately, IDMC's user-friendliness, comprehensive data integration, and collaboration tools make it a compelling choice for mid-sized and larger businesses seeking a cloud-based ETL solution. However, its pricing model and potential vendor lock-in require careful consideration before adoption.

Pros
  • User-friendly interface
  • Handles complex ETL tasks
  • Robust data integration
  • Scalable for large datasets
  • Strong support community
Cons
  • Can be expensive
  • Steep learning curve
  • Limited customization
  • Occasional performance issues
  • Vendor lock-in concerns
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Product Insights

  • 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.
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  • Unified Data Platform: Consolidate data from diverse sources into a single, accessible repository.
  • Streamlined Workflows: Automate data extraction, transformation, and loading processes for improved efficiency.
  • Enhanced Data Quality: Ensure data accuracy and consistency through validation and cleansing routines.
  • Flexible Data Manipulation: Apply a wide range of transformations to prepare data for analysis.
  • Visual Mapping Tools: Design and implement data flows with intuitive drag-and-drop interface.
  • Scalable Processing Power: Handle large datasets efficiently with parallel processing capabilities.
  • Empowered Business Users: Provide self-service data access and analysis tools for informed decision-making.
  • Advanced Reporting & Visualization: Generate insightful reports and dashboards to communicate data effectively.
  • Improved Collaboration: Foster data-driven collaboration across departments and teams.
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  • 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. 
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  • Visual Data Mapping: Drag-and-drop interface for intuitive design of data flows.
  • Codeless Transformations: Apply data transformations without extensive coding knowledge.
  • Cloud-Native Architecture: Scalable and flexible deployment on major cloud platforms.
  • AI-Powered Data Integration: Automated pattern discovery and data lineage tracking.
  • Hybrid Data Integration: Connect to both on-premises and cloud-based data sources.
  • Data Quality Management: Built-in tools for data profiling, cleansing, and validation.
  • Metadata Management: Comprehensive catalog of data assets for better visibility and governance.
  • Data Security & Compliance: Role-based access control, encryption, and audit trails.
  • Real-Time Data Integration: Capture and process streaming data for instant insights.
  • Self-Service Data Access: Empower business users with data exploration and analysis tools.
  • Collaboration & Integration: Seamless integration with other Informatica products and third-party tools.
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Product Ranking

#9

among all
ETL Tools

#37

among all
ETL Tools

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Analyst Rating Summary

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Platform Capabilities

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

AWS Glue
IDMC
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Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 100 92 62 90 96 100 100 100 100 98 96 100 100 96 0 25 50 75 100
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Analyst Ratings for Technical Requirements Customize This Data Customize This Data

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User Sentiment Summary

Great User Sentiment 165 reviews
Great User Sentiment 596 reviews
85%
of users recommend this product

AWS Glue has a 'great' User Satisfaction Rating of 85% when considering 165 user reviews from 3 recognized software review sites.

84%
of users recommend this product

IDMC has a 'great' User Satisfaction Rating of 84% when considering 596 user reviews from 2 recognized software review sites.

4.0 (46)
4.2 (282)
4.4 (109)
4.2 (314)
3.9 (10)
n/a

Awards

SelectHub research analysts have evaluated AWS Glue and concluded it earns best-in-class honors for Workflow Management.

Workflow Management Award

SelectHub research analysts have evaluated IDMC and concluded it deserves the award for the Best Overall ETL Tools available today and earns best-in-class honors for Data Sources and Targets Connectivity.

Analysts' Pick Award
Data Sources and Targets Connectivity Award

Synopsis of User Ratings and Reviews

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
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User-friendly interface: Intuitive drag-and-drop interface simplifies development and reduces reliance on coding expertise.
Cloud-native architecture: Streamlines deployment, eliminates infrastructure management, and offers automatic updates.
Comprehensive integration capabilities: Connects to a vast array of sources and targets, encompassing databases, cloud applications, and file formats.
Scalable performance: Handles large data volumes efficiently, ensuring timely data delivery for critical business processes.
Robust data quality features: Cleanses and validates data for accuracy, fostering reliable insights and decision-making.
Collaboration features: Enables teamwork and knowledge sharing among developers, enhancing productivity and efficiency.
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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.
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Learning curve: While user-friendly, mastering its full capabilities can require a learning investment, especially for those new to ETL tools.
Cost: Can be relatively expensive compared to some alternatives, potentially limiting adoption for smaller organizations or those with tight budgets.
Limited customization: Certain features and integrations might offer less flexibility for tailored solutions compared to more open-source alternatives.
Technical support: Users have occasionally reported mixed experiences with the quality and responsiveness of technical support.
Cloud vendor lock-in: Being a cloud-native platform, migrating to a different cloud provider can involve challenges and potential data transfer costs.
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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.

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User reviews of Informatica IDMC in the past year paint a picture of a powerful ETL tool with strengths in user-friendliness, cloud-native architecture, and comprehensive data integration. Its intuitive interface and drag-and-drop features make it accessible even for those with limited coding experience, a major advantage over more complex competitors like Talend Open Studio. However, its cloud-based nature can be a double-edged sword: while it offers scalability and automatic updates, it also locks users into Informatica's infrastructure, potentially hindering migration to other cloud providers. The robust data quality features of IDMC are another highlight, cited as crucial for ensuring reliable data-driven decisions. Compared to competitors like Pentaho Data Integration, IDMC's built-in cleansing and validation capabilities are seen as a significant advantage, saving time and effort. However, some users have expressed concerns about the learning curve associated with mastering IDMC's full potential, especially compared to more lightweight alternatives like Fivetran. While cost is a common concern with any enterprise software, IDMC's price tag can be particularly prohibitive for smaller companies or those on tight budgets. Users often compare it to open-source options like Apache Airflow, which offer similar functionality at a fraction of the cost. However, the lack of built-in features and steeper learning curve of open-source options can negate the cost savings for some organizations. Ultimately, user reviews suggest that IDMC is a valuable ETL tool for organizations seeking a user-friendly, cloud-based solution with robust data integration and quality features. However, its pricing and potential vendor lock-in are important considerations. Weighing these strengths and weaknesses against the needs and budget of your organization will determine whether IDMC is the right ETL fit for you.

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