Informatica Intelligent Data Management Cloud (IDMC) and AWS Glue compete in the data management and integration category. Informatica IDMC appears to have an edge in versatility due to its multiple interconnected modules and flexibility offered by Informatica Processing Units (IPUs).
Features: Informatica IDMC offers features like data cleansing, data masking, and API management, enhancing its integration capabilities under one umbrella. Users appreciate the flexibility of using IPUs across services. AWS Glue provides robust integration with AWS services, supporting serverless architecture, real-time data processing, and cost-effectiveness in data cataloging. Its easy integration with other AWS services and ability to handle large data sets are highly valued.
Room for Improvement: Informatica IDMC could improve its internal tech support, UI, and scalability, with users seeking more automation and easier configuration similar to on-premise solutions. AWS Glue faces limitations due to its AWS-only ecosystem, language support constraints, and pricing concerns. Enhancing user accessibility and support efficiency is crucial for both, with AWS Glue notably challenged by its AWS-exclusive constraints.
Ease of Deployment and Customer Service: Informatica IDMC is flexible in deploying across on-premises, private, public, and hybrid clouds, appealing to diverse setups. However, customer service is mixed, with varying responsiveness. AWS Glue is adaptable in public and hybrid clouds, praised for prompt user support. Both solutions need improvements in deployment guides and consistency in service.
Pricing and ROI: Informatica IDMC is perceived as high-cost, with scalable pricing based on data volume, accepted by users for its comprehensive features. AWS Glue's pay-as-you-go model can lead to unpredictable expenses but is seen as cost-effective for less intensive workloads. Informatica demands higher licensing and operational costs, while AWS Glue's flexibility may appeal more but can result in high cumulative costs for intensive use.
I advocate using Glue in such cases.
We see return on investment from this solution in terms of time; time reduction or cost benefits is what we are getting very good results from.
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.
Due to the tool's maturity limitations, solutions are not always simple and often require workarounds.
The response time is pretty good because we have someone in-house, who is an expert from Informatica, in our team who can help us with any sort of queries usually.
If they are unsure how to resolve an issue, they keep customers informed, providing updates about progress and ensuring communication with the product team to deliver accurate responses.
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.
As a SaaS platform, IDMC is quite scalable and provides complete flexibility.
I find Informatica Intelligent Data Management Cloud (IDMC) to be a sustainable and scalable solution.
AWS Glue is highly stable, and I would rate its stability as nine.
Stability is crucial because IDMC holds business-critical data, and it needs to be available all the time for business users.
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.
The tool needs to mature in terms of category-specific attributes or dynamic attributes.
I also want to see integration with other Informatica products, such as IICS, to leverage the metadata from EDC.
The licenses are too expensive compared to before, which is why customers are now preferring other data metadata management tools like OneTrust, Collibra, and Azure Purview.
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.
It ranges from a quarter million to a couple of million a year.
The licenses are too expensive compared to before, which is why customers are now preferring other data metadata management tools like OneTrust, Collibra, and Azure Purview.
I think the costs are reasonable for the kinds of features that Informatica Intelligent Data Management Cloud (IDMC) has.
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.
The platform's ability to pull in data from other platforms without the need for an additional integration tool enhances its appeal.
Informatica Intelligent Data Management Cloud (IDMC) can connect to pretty much any application, including Oracle Analytics and Power BI, and it works quite seamlessly.
In on-premise, we call it EDC for metadata management, while in cloud-based technologies, it is known as the Metadata Command Center, which serves the same purpose as EDC concerning CDGC.
| Product | Market Share (%) |
|---|---|
| AWS Glue | 15.3% |
| Informatica Intelligent Data Management Cloud (IDMC) | 5.6% |
| Other | 79.1% |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 6 |
| Large Enterprise | 32 |
| Company Size | Count |
|---|---|
| Small Business | 42 |
| Midsize Enterprise | 24 |
| Large Enterprise | 134 |
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.
Informatica Intelligent Data Management Cloud (IDMC) integrates data quality, governance, and integration with flexible architecture. It supports multiple domains and a data models repository, delivering AI-enhanced data management across cloud-native platforms.
IDMC provides seamless integration and governance capabilities that support diverse data environments. Its comprehensive suite includes customizable workflows, data profiling, and metadata management. AI features, a data marketplace, and performance scalability enhance data management. While its interface poses challenges, its robust matching and cloud-native integration facilities are essential for complex data ecosystems. Users employ IDMC for connecting systems, ensuring data quality, and supporting data compliance but seek better pre-built rules, services, and improved connectivity, especially with platforms like Salesforce. Licensing, cost, and added AI functionalities are areas for potential refinement.
What are the key features of IDMC?IDMC is implemented across industries for data integration, metadata management, and governance. Organizations use it to connect systems, migrate data to cloud environments, and maintain data quality. They manage master data and automate business processes, facilitating data lineage and ensuring compliance with privacy regulations.
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