AWS Glue and webMethods.io compete in the data integration and management space. AWS Glue appears to have an edge for those within AWS ecosystems due to its seamless integration, whereas webMethods.io stands out in enterprise application integration with its broad range of connectors.
Features: AWS Glue offers a robust data catalog, a serverless architecture for cost efficiency, and seamless AWS integration, requiring Python or Scala for customization. webMethods.io provides strong EAI and ESB capabilities, intuitive data mapping and unit testing, and a variety of connectors for easy integration across business processes.
Room for Improvement:AWS Glue users cite slow startup times, limited programming languages, and poor compatibility outside AWS. webMethods.io encounters issues with large file handling, slow integration advances, outdated connectors, and documentation, and needs better cloud adaptation support.
Ease of Deployment and Customer Service: AWS Glue supports cloud deployments, offering flexibility but requires learning, with reliable though sometimes costly and slow support. webMethods.io works primarily in on-premises setups, limiting agility, and its customer service sees mixed feedback, with noted tech advancement gaps.
Pricing and ROI: AWS Glue's pay-as-you-go model is favorable for scalable budgeting but costly for complex workloads, promising ROI in efficiency and speed gains. webMethods.io, while comprehensive in functionality, may deter smaller businesses due to high licensing, with ROI tied to its extensive integration capabilities.
I advocate using Glue in such cases.
AWS's documentation is reliable, and careful reference often resolves missed upgrade details.
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.
Vertically, scalability is fine, however, I have not expanded horizontally with the product yet.
AWS Glue is highly stable, and I would rate its stability as nine.
There are some issues like the tool hanging or the need for additional jars when exposing web services.
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.
Migrating jobs from version 3.0 to 4.0 can present compatibility issues.
A special discount of at least 50% for old customers would allow us to expand our services and request more resources.
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.
AWS charges based on runtime, which can be quite pricey.
Costing depends on resource usage, and cost optimization may involve redesigning jobs for flexibility.
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.
I think if I'm working with big data, common languages like Python work quite nicely, which is advantageous.
It facilitates the exposure of around 235 services through our platform to feed various government entities across the entire country.
AWS Glue is a serverless cloud data integration tool that facilitates the discovery, preparation, movement, and integration of data from multiple sources for machine learning (ML), analytics, and application development. The solution includes additional productivity and data ops tooling for running jobs, implementing business workflows, and authoring.
AWS Glue allows users to connect to more than 70 diverse data sources and manage data in a centralized data catalog. The solution facilitates visual creation, running, and monitoring of extract, transform, and load (ETL) pipelines to load data into users' data lakes. This Amazon product seamlessly integrates with other native applications of the brand and allows users to search and query cataloged data using Amazon EMR, Amazon Athena, and Amazon Redshift Spectrum.
The solution also utilizes application programming interface (API) operations to transform users' data, create runtime logs, store job logic, and create notifications for monitoring job runs. The console of AWS Glue connects all of these services into a managed application, facilitating the monitoring and operational processes. The solution also performs provisioning and management of the resources required to run users' workloads in order to minimize manual work time for organizations.
AWS Glue Features
AWS Glue groups its features into four categories - discover, prepare, integrate, and transform. Within those groups are the following features:
AWS Glue Benefits
AWS Glue offers a wide range of benefits for its users. These benefits include:
Reviews from Real Users
Mustapha A., a cloud data engineer at Jems Groupe, likes AWS Glue because it is a product that is great for serverless data transformations.
Liana I., CEO at Quark Technologies SRL, describes AWS Glue as a highly scalable, reliable, and beneficial pay-as-you-go pricing model.
webMethods.io Integration is a powerful integration platform as a service (iPaaS) that provides a combination of capabilities offered by ESBs, data integration systems, API management tools, and B2B gateways.
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