Fivetran vs AWS Glue

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Our analysts compared Fivetran vs AWS Glue 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

Fivetran is a cloud-native data extraction tool that simplifies and streamlines the data analysis process with a zero-maintenance pipeline that ensures expedient, transparent delivery of data from source to warehouse. Built to empower analysts, it allows users to accelerate analytics and achieve faster time-to-insights without the need for complex engineering, promoting more efficient data-driven decision-making for users of all technical skill levels.

Suitable for small to large companies, it is a public-cloud hosted SaaS available through annual subscriptions, paid via invoice. Pricing is volume based with three tiers of plans, in ascending order of offerings and cost: Starter, Standard and Enterprise.

Pros
  • Easy to use interface
  • Connects to many data sources
  • Fast and reliable data pipelines
  • Centralized data management
  • Scalable for large data volumes
Cons
  • Limited customizability
  • Can be expensive for complex needs
  • Not ideal for real-time data
  • Limited support for data transformations
  • Learning curve for advanced features
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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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Product Insights

  • Five-Minute Setup: The platform boasts a zero-maintenance configuration process that can have users up and running in 10 clicks.
  • Data Simplified: Users can simplify their data stack with streamlined data extraction and preparation processes, allowing for more accurate and efficient access to analytics.
  • Optimized Loading: Automatic updates reduce data entry tasks and ensure real-time accuracy across storage facilities. The platform also reduces workloads on cloud data warehouses by putting the brunt of demand on their servers instead of the warehouses’. 
  • Centralize Data: The solution helps users to extract data from disparate sources and load it into their data warehouse to create a single, cohesive source of truth in the cloud.
  • Flexibility: Standing apart from many traditional data tools, Fivetran utilizes ELT (extract, load, transform) over ETL (extract, transform and load). By flipping the last two steps of the process and postponing data transformation, it ensures that stored data won’t be compromised by transformations, allowing users to perform analysis more deliberately and securely.
  • Smooth Integrations: Integration with other systems is intuitive and smooth with this platform. Its seamless data sync allows users to draw data from their CRM, ERP or other business solutions as well as a range of cloud data sources, allowing for real-time monitoring.
  • Data Security: The system has advanced security features and is compliant with PCI, HIPAA, GDPR and SOC 2 standards. It also encrypts data in motion and at rest, purging data after every sync. 
  • Built to Scale: The platform helps users navigate a world of exponential data growth, with instantly scalable cloud resources, optimized processes and pricing plans that keep up with companies as they grow. 
  • Free Trial: Interested buyers can receive a free 14-day trial of the platform to test connectors and features by signing up on the vendor’s website. Users can add up to two sources and set up an unlimited number of connectors for those sources, pointing to one destination. Users interested in adding more sources or destinations can contact the Fivetran sales team for more information.
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  • 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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  • Data Connectivity: The solution has data connectors for 100 sources, with the option for users to create their own data connector to APIs not yet natively supported. It directly pulls information from cloud applications. Users can also upload or email files into a cloud storage service and have that data loaded into their warehouse. The system reflects changes made to live files such as Google Sheets.
  • Extract Data: The solution connects natively to over 100 SaaS sources, automatically extracting information from those sources after an admin has granted the tool access through OAuth. The system normalizes, cleanses and standardizes data before loading.
  • Data Sync: Upon connection, the system performs a historical sync. From there, instead of arduously reloading full data dumps from APIs and databases, the solution optimizes loadings by incrementally updating data sources in batches, with data load time configurable as frequently as five minutes and as infrequent as every 24 hours.
  • Load into Cloud Data Warehouses: Fivetran supports modem cloud warehouses like BigQuery, Snowflake, Azure and Redshift.
  • Transform Data: The platform preps data, normalizing schemas from APIs, so that it can be analyzed instantly. Transformations always happen in the warehouse so that the raw data is always available alongside the transformed data; data will never be lost and the transformations can be edited and run again on the raw data.
  • Alerts: The system notifies users if there are delays or issues in any step of the process.
  • Dashboards: Users interact with the platform via dashboards that display information about ELT processes in a visual, easy-to-digest format.
  • System Logs: The solution maintains transparency with granular system logs of every sync that users can cross-reference in their own logging system.
  • Metadata Management: A suite of policies and procedures allows users to manage the data which describes other data such as file size, date of data creation, tags, titles, authors, etc.
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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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Product Ranking

#7

among all
ETL Tools

#9

among all
ETL Tools

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Analyst Rating Summary

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Platform Capabilities
Platform Security
Metadata Management
Data Transformation
Data Sources and Targets Connectivity
Data Delivery
Performance and Scalability
Platform Capabilities
Platform Security
Workflow Management

Analyst Ratings for Functional Requirements Customize This Data Customize This Data

Fivetran
AWS Glue
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Data Delivery Data Quality Data Sources And Targets Connectivity Data Transformation Metadata Management Platform Capabilities Workflow Management 86 75 93 94 96 100 81 100 92 62 90 96 100 100 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

Excellent User Sentiment 28 reviews
Great User Sentiment 165 reviews
92%
of users recommend this product

Fivetran has a 'excellent' User Satisfaction Rating of 92% when considering 28 user reviews from 2 recognized software review sites.

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.

n/a
4.0 (46)
4.64 (14)
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4.6 (14)
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4.4 (109)
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3.9 (10)

Awards

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

User Favorite Award

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

Workflow Management Award

Synopsis of User Ratings and Reviews

Effortless Data Integration: Connects to hundreds of data sources with pre-built connectors and minimal setup.
Automated Data Pipelines: Schedules and runs data transfers reliably, freeing up time for analysis.
Centralized Data Management: Provides a single source of truth for all your data, simplifying reporting and decision-making.
Scalable for Growth: Handles large data volumes with ease, adapting to your evolving needs.
Improved Data Visibility: Makes data readily available for everyone in your organization, fostering data-driven decision-making.
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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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Limited Customizability: Relies on pre-built connectors, making complex data pipelines or transformations challenging.
Costly for Advanced Needs: Pricing scales with data volume and complexity, becoming expensive for intricate ETL processes.
Batch-Oriented Transfers: Focuses on scheduled data refreshes, not ideal for real-time needs or low-latency pipelines.
Basic Data Transformations: Offers limited built-in transformations, requiring additional tools for complex data manipulation.
Advanced Feature Learning Curve: Mastering custom connectors, scripting, or other advanced features requires technical expertise.
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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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Users praise Fivetran for its ease of use and effortless data integration. "Setting up connectors is straightforward," one reviewer comments, "like plugging in appliances." This plug-and-play simplicity sets it apart from competitors like Stitch, often lauded for its flexibility but criticized for a steeper learning curve. However, Fivetran's strength in pre-built connectors comes at a cost: limited customizability. While users love its "seamless data movement," another user points out it's "not ideal for complex transformations," requiring additional tools that negate its initial ease. This lack of advanced ETL capabilities puts it behind platforms like Informatica PowerCenter, but at a fraction of the cost. Ultimately, Fivetran shines for its user-friendly approach and reliable data pipelines, perfect for businesses prioritizing simplicity and scalability. But for complex data manipulation or real-time needs, users might find themselves yearning for the power and flexibility of other ETL solutions.

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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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Screenshots

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