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.
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.
AWS Glue is highly stable, and I would rate its stability as nine.
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.
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.
Costing depends on resource usage, and cost optimization may involve redesigning jobs for flexibility.
AWS Glue's most valuable features include its transformation capabilities, which provide data quality and shape for processing in ML or AI models.
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.
I think if I'm working with big data, common languages like Python work quite nicely, which is advantageous.
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.
WhereScape is data warehouse software that automates the Data Warehouse lifecycle. From implementation to maintenance, WhereScape will ensure your data warehouse projects are completed up to 5x faster than manual coding.
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