

Find out in this report how the two Cloud Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
We also avoid hiring a dedicated data engineer for pipeline maintenance, which has saved us a significant salary.
I have observed a return on investment with 30 to 70 percent of costs saved.
The main benefit was reducing engineering time spent maintaining custom ingestion pipelines and lowering operational overhead around data syncs, which indirectly contributes to efficiency.
I advocate using Glue in such cases.
Airbyte Cloud's customer support is professional and quite responsive.
Upgrades occur every four months, and new developments coincide with version updates.
For complex Glue-related problems such as job failures or permission issues, their documentation is good, but having direct access to support helps cut down troubleshooting time significantly.
We can run multiple syncs in parallel at the same time.
Airbyte Cloud is highly scalable, and we can scale it up whenever required on demand.
Airbyte Cloud scales well as our data needs grow to a scale of ten.
It is beneficial to upgrade jobs, and we conduct extensive testing in development before migrating to production.
It can easily handle data from one terabyte to 100 terabytes or more, scaling nicely with larger datasets.
The incremental sync feature is particularly very accurate as it only moves new or changed records, which keeps our warehouse clean and our data cost-controlled.
Airbyte Cloud has handled our workloads well for scheduled syncs between Postgres and a few SaaS sources and Snowflake.
AWS Glue is highly stable, and I would rate its stability as nine.
A more user-friendly error explanation would be beneficial.
Comprehensive video tutorials, demonstrations, or proper documentation would be beneficial.
Error debugging depth in the UI, more granular visibility into why a sync failed, and better handling or guidance around schema changes when they happen frequently in source systems.
Migrating jobs from version 3.0 to 4.0 can present compatibility issues.
With AWS, I gather data from multiple sources, clean it up, normalize it, de-duplicate it, and make it presentable.
A more user-friendly and simpler process would help speed up the deployment process.
Its price is 30 to 70 percent lower compared to competitor tools in the market.
I think the overall cost was relatively low, so I don't think we had any issues with billing or costs.
Costing depends on resource usage, and cost optimization may involve redesigning jobs for flexibility.
AWS charges based on runtime, which can be quite pricey.
The smallest cost for a project is around €700, while the largest can reach up to €7,000 based on the scale of the usage.
Definitely the pre-built connectors have been the most valuable feature for my team, and it has made my workflow easier.
The best features I found most useful were the large number of pre-built connectors, the managed scheduling for syncs, and the ability to monitor sync status and failures through the UI without needing to maintain infrastructure.
The best feature that I have liked about it is the scheduling and automation features that help reduce manual effort significantly in moving data between systems.
For ETL, I feel the performance is excellent. If I create jobs in a standard way, the performance is great, and maintenance is also seamless.
AWS Glue's most valuable features include its transformation capabilities, which provide data quality and shape for processing in ML or AI models.
AWS Glue has reduced efforts by 60%, which is the main benefit.
| Product | Mindshare (%) |
|---|---|
| AWS Glue | 7.8% |
| Airbyte Cloud | 0.8% |
| Other | 91.4% |

| Company Size | Count |
|---|---|
| Small Business | 10 |
| Large Enterprise | 4 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 6 |
| Large Enterprise | 34 |
Airbyte Cloud is a modern data integration platform that facilitates seamless data movement across applications and warehouses with user-friendly features and robust connectors.
Airbyte Cloud offers an adaptable approach to data integration, designed to handle large-scale data synchronization efficiently. It supports various environments, providing reliable and fast data transfer. Users benefit from its open-source foundation, offering flexibility and innovation. Its architecture allows developers to create custom connectors, making it highly customizable to meet specific data movement needs.
What are the crucial features of Airbyte Cloud?Airbyte Cloud is utilized in sectors such as e-commerce, where quick access to real-time data is essential for inventory management, and in financial services, enabling seamless transactions and accurate data analytics. Its flexibility supports environments demanding high agility, driving transformation with minimal disruptions.
AWS Glue is a serverless data integration service offering seamless integration with AWS services like S3, Redshift, and Athena. Known for its flexibility with data formats and automation of ETL tasks, AWS Glue enhances data management and transformation.
AWS Glue facilitates seamless data extraction, transformation, and loading for businesses, integrating with key AWS services, allowing efficient data pipeline automation. It's valued for a user-friendly GUI, scalability, and cost-effectiveness, supporting PySpark for complex datasets and includes a robust data catalog, real-time backup capabilities, and code generation. Despite its strengths, improvements are needed in documentation, training, and broader programming language support. Users face challenges with its complex interface and integration with non-AWS products, driving demand for enhancements in its usability and performance.
What are AWS Glue's most important features?Businesses leverage AWS Glue in industries for ETL processes, data integration, and transformation. It is used to optimize data lakes or warehouses integration, enhancing data cataloging and real-time integration. Its serverless feature enables efficient data processing in sectors like finance and healthcare, where handling complex data-intensive tasks is crucial.
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