AWS Glue vs Skyvia

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Our analysts compared AWS Glue vs Skyvia 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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Skyvia is a cloud-based data integration solution from Devart. Hosted on Azure, it has query, connect and backup capabilities. Automated workflows and a visual query wizard simplify ETL and data pipelines.

Users can set it up to accept data when a user clicks on a website or makes a selection in an application. It’s called event-based data ingestion. The vendor offers monthly subscriptions based on the volume of processed data.

  • Pros
  • Easy to use
  • Visual data pipelines
  • User-friendly interface
  • Balance of features & ease
  • Cons
  • Limited coding options
  • Fewer integrations compared to some
  • May not be ideal for complex needs
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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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  • Stay Competitive: Get excellent performance with high fault tolerance, thanks to the Azure cloud. It can scale by adding resources and can manage workloads efficiently.
  • Improve Decision-Making: Make informed decisions with accurate data, thanks to automatic updates, scheduled refreshes and incremental loading. Export reports to CSV files and FTP/SFTP servers.
  • Gain Timely Insights: Get accurate data from a single source of truth — a unified data store. Trust automated workflows to deliver data on time. Uncover new information with filters, joins, and string and date-time functions.
  • Act: Hit the ground running with pipeline templates or build new ones with the data flow designer. A visual query builder makes data access easy, while a code editor serves developers who want more control.
  • Secure Data: Encrypt data at rest and in motion and prepare record-wise logs for easy troubleshooting. GDPR, HIPAA and SOC compliance and access controls (MFA, SSO, RBAC) ensure that only authorized users see the necessary information.
  • Stay Connected to Data: Access from anywhere with an internet connection. It’s software-as-a-service, so it avoids the expense of on-premise infrastructure and software installation.
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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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  • Event-Based Data Ingestion: Pull data from several sources using Skyvia Automation. Set up ingestion tasks to trigger per a schedule or after an event happens.
  • Bulk Loading: Loads large volumes or full data dumps from a source into a database. The vendor provides a Use Bulk Import checkbox with every connection popup.
  • Bidirectional Updates: Tracks the latest modifications with change data capture (CDC). It keeps data updated in both directions—from the source to the target and from the target to the source.
  • Functions: Convert data to a usable format with string and mathematical functions. The vendor provides date-time, lookup, join and filter functions. Map a single table to several related tables and create new values as desired.
  • Audit Trails: Stores logs of successfully loaded records for specific integrations and connections. It also retains records of failed integration attempts.
  • Data Encryption: Use AES 256-bit encryption to hide sensitive data at rest and when it's on the move. Additionally, the TLS protocol secures the data end-to-end.
  • Role-Based Access Control: Set up role-based users — admin, developer, member and supporter — at the workspace level. Administrators can create custom roles if they want.
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Product Ranking

#9

among all
ETL Tools

#33

among all
ETL Tools

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Analyst Rating Summary

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Data Delivery
Performance and Scalability
Platform Capabilities
Platform Security
Workflow Management
Platform Security

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

AWS Glue
Skyvia
+ Add Product + Add Product
Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 100 92 62 90 96 100 100 84 27 65 68 16 79 71 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
Excellent User Sentiment 430 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.

96%
of users recommend this product

Skyvia has a 'excellent' User Satisfaction Rating of 96% when considering 430 user reviews from 5 recognized software review sites.

n/a
4.8 (14)
4.0 (46)
4.8 (220)
n/a
4.8 (67)
4.4 (109)
4.8 (104)
3.9 (10)
4.8 (25)

Awards

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

Workflow Management Award

Skyvia stands above the rest by achieving an ‘Excellent’ rating as a User Favorite.

User Favorite 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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Easy to Use: Many users praised its functionality for moving data from source to target systems and building pipelines independently.
Improved Data Integration: Users said readymade connections make it easy to pull data from cloud applications and databases.
Automated Data Workflows: Users appreciated the freedom to set up pipelines to run later. It frees them up to focus on high-level tasks.
Enhanced Data Accessibility: Users who reviewed data access said common storage gives everyone the same data to work with and avoids misdirected decisions.
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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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Limited Coding Options for Complex Transformations: While Skyvia boasts a friendly interface, complex data manipulations require coding knowledge. If teams don't know how to code, it can block them from performing deep analysis or building pipelines.
Fewer Integrations Compared to Some Competitors: It offers over 180 connectors, but if users need to connect to other sources, they might need workarounds.
May Not Be Ideal for Highly Scalable or Complex Needs: It works best with moderate data volumes, but users might need something more for large datasets and complex needs.
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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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Skyvia stands out for its user-friendly interface. With drag-and-drop features, anyone can connect to data sources and build pipelines, which is a big plus compared to some trickier tools that require coding.But there's a catch — the vendor keeps things simple, so it might not be the best for super complex data transformations. It has connectors for many popular apps, but some users felt the vendor should offer more options.It could be a problem for businesses that need to connect to specific data sources or want advanced functionality. Reviews also said it works great for medium data volumes.Its automatic workflows streamline data transfer and analysis while freeing workers for high-level tasks.Overall, Skyvia is a good fit for businesses that want an easy-to-use tool to integrate their data, automate tasks and gain insights without needing IT help.

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