IBM InfoSphere DataStage and AWS Glue are key players in the data integration and ETL market. While DataStage offers robust data manipulation capabilities, AWS Glue holds the advantage in scalability and seamless AWS integration.
Features: IBM InfoSphere DataStage is notable for its high scalability, efficient parallel processing, and comprehensive metadata management. It also excels in integrating with various tools via APIs and offers robust data quality management. AWS Glue is praised for its seamless integration with AWS services, serverless architecture, and user-friendly features like automatic code generation and a data catalog. It provides significant scalability and is cost-effective due to its pay-as-you-go pricing model.
Room for Improvement: DataStage could improve in user interface design, ease of use, and cloud integration. AWS Glue requires enhancements in its user interface and support for non-AWS platforms. Both solutions can improve their user-friendliness and documentation. DataStage faces challenges with cost and support speed, whereas AWS Glue needs better monitoring capabilities and more support for big data technologies.
Ease of Deployment and Customer Service: IBM InfoSphere DataStage is well-suited for on-premises and hybrid cloud environments, while AWS Glue excels in private, public, and hybrid cloud deployments. DataStage users have mixed customer service experiences but find technical support reliable. AWS Glue users benefit from AWS's extensive global support. The deployment model of AWS Glue offers more flexibility in cloud environments.
Pricing and ROI: IBM InfoSphere DataStage is considered expensive, with high licensing costs, especially for small and medium enterprises. However, it's competitive against other enterprise tools. AWS Glue's flexible pay-as-you-go pricing model is appealing for scalable cost management, potentially making it more affordable for dynamic workloads. Both solutions provide strong ROI for high-performance integration needs, though DataStage involves higher initial setup costs.
I advocate using Glue in such cases.
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.
AWS's documentation is reliable, and careful reference often resolves missed upgrade details.
We also have the flexibility to submit a feature request to be included as part of the wishlist, potentially becoming a product feature in subsequent releases.
IBM tech support has allocated dedicated resources, making it satisfactory.
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 managed service, it reduces management burdens.
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.
Learning the latest functionalities is crucial, and while challenging, it is a vital part of staying current and ensuring an efficient ETL process.
I wonder if it supports other areas, such as cloud environments with open source support, or EdgeShift.
The solution needs improvement in connectivity with big data technologies such as Spark.
AWS charges based on runtime, which can be quite pricey.
Costing depends on resource usage, and cost optimization may involve redesigning jobs for flexibility.
Regarding AWS Glue's pricing, it is not more expensive; rather, it is very reasonable, but it is not cheap.
Pricing for IBM InfoSphere DataStage is moderate and not much expensive.
AWS Glue has reduced efforts by 60%, which is the main benefit.
AWS Glue also enhances job scheduling and orchestration capabilities, integrating with AWS Glue Studio for comprehensive data workflow management.
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.
IBM InfoSphere DataStage is very scalable, allowing us to extend it according to our processing needs.
The failure detection has been very useful for us, as well as the load balancing feature.
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 6 |
| Large Enterprise | 32 |
| Company Size | Count |
|---|---|
| Small Business | 23 |
| Midsize Enterprise | 4 |
| Large Enterprise | 25 |
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.
IBM InfoSphere DataStage is a high-quality data integration tool that aims to design, develop, and run jobs that move and transform data for organizations of different sizes. The product works by integrating data across multiple systems through a high-performance parallel framework. It supports extended metadata management, enterprise connectivity, and integration of all types of data.
The solution is the data integration component of IBM InfoSphere Information Server, providing a graphical framework for moving data from source systems to target systems. IBM InfoSphere DataStage can deliver data to data warehouses, data marts, operational data sources, and other enterprise applications. The tool works with various types of patterns - extract, transform and load (ETL), and extract, load, and transform (ELT). The scalability of the platform is achieved by using parallel processing and enterprise connectivity.
The solution has various versions, catering to different types of companies, which include the Server Edition, the Enterprise Edition, and the MVS Edition. Depending on which version a company has bought, different goals can be achieved. They include the following:
IBM InfoSphere DataStage can be deployed in various ways, including:
IBM InfoSphere DataStage Features
The tool has various features through which users can integrate and utilize their data effectively. The components of IBM InfoSphere DataStage include:
IBM InfoSphere DataStage Benefits
This solution offers many benefits for the companies that utilize it for data integration. Some of these benefits include:
Reviews from Real Users
A data/solution architect at a computer software company says the product is robust, easy to use, has a simple error logging mechanism, and works very well for huge volumes of data.
Tirthankar Roy Chowdhury, team leader at Tata Consultancy Services, feels the tool is user-friendly with a lot of functionalities, and doesn't require much coding because of its drag-and-drop features.
We monitor all Cloud Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
