IBM InfoSphere DataStage and Matillion Data Productivity Cloud compete in the data integration and ETL category. Based on scalability, Matillion appears to have the upper hand due to its ease of use and cloud-native capabilities.
Features: IBM InfoSphere DataStage features high scalability, parallel processing, and robust data integration capabilities. It offers strong metadata management and highly customizable configurations, making it ideal for handling large data volumes and complex transformations. Matillion Data Productivity Cloud excels with its cloud-native integrations, dynamic scaling capabilities, and ease of mid-pipeline data sampling. It offers visual simplicity and quick setup, especially useful in AWS environments.
Room for Improvement: IBM InfoSphere DataStage needs improvements in licensing costs, interface user-friendliness, and modern data sources support. It also requires enhanced cloud integration and documentation. Matillion Data Productivity Cloud would benefit from more frequent updates to keep up with API changes, enhanced data streaming capabilities, and adjustments to its pricing model. Improvements in backend integration and data capture features are also suggested.
Ease of Deployment and Customer Service: IBM InfoSphere DataStage operates primarily on-premises, with hybrid cloud options offering a stable but older system. Technical support varies by region, with some users experiencing delays. Matillion Data Productivity Cloud is mainly used in public cloud environments, providing easier deployment through AWS integration. Customer service is rated higher, with straightforward setup and responsive support.
Pricing and ROI: IBM InfoSphere DataStage is considered expensive, with enterprise-level pricing and higher initial investment costs but offers strong ROI through performance optimization. Matillion Data Productivity Cloud provides a more flexible consumption-based pricing model, offering cost-effectiveness by aligning costs with usage levels, although it is not the cheapest solution available.
Consequently, we adjusted our processes to use Matillion Data Productivity Cloud only for extraction and ingestion, while Snowflake handled all transformations and jobs.
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.
They communicate effectively and respond quickly to all inquiries.
Depending on the nature of data sets, volume, and mixture of different data, the scalability could be improved as manual code writing is still required.
The autoscale process works well, allowing the system to start another node automatically if the first machine reaches 80% capacity.
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.
The main areas for improvement are AI features and scalability.
Connections to BigQuery for extracting information are complex.
Pricing for IBM InfoSphere DataStage is moderate and not much expensive.
Matillion Data Productivity Cloud offers discounts and special deals, especially when dealing with high-volume clients or fewer existing clients in specific regions, like Spain.
The pricing is moderate, neither expensive nor cheap.
It is straightforward from a design and development perspective, and also for deployment.
IBM InfoSphere DataStage is very scalable, allowing us to extend it according to our processing needs.
The predefined connectors eliminate the need to write code for connectivity.
Matillion Data Productivity Cloud is effective for ingest functions, particularly when moving information to Snowflake and performing many transformations.
| Company Size | Count |
|---|---|
| Small Business | 23 |
| Midsize Enterprise | 4 |
| Large Enterprise | 25 |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 10 |
| Large Enterprise | 11 |
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.
Matillion Data Productivity Cloud features an intuitive graphical interface, seamless AWS integration, and efficient data management. Its tools streamline complex tasks for SFDC, RDS, Marketo, Facebook, and Google AdWords.
Matillion Data Productivity Cloud provides fast transformations with built-in verification, easy scheduling, and sampling. With automatic scalability and diverse data source support, it simplifies complex data tasks. Users benefit from cloud data warehousing and integrating data into Snowflake while appreciating its ease of use by non-technical teams. Enhancements can focus on frequent API adjustments, improved documentation, faster performance with less latency, and better error handling.
What are the key features of Matillion Data Productivity Cloud?
What benefits and ROI should users seek in reviews?
In industries such as technology, finance, and healthcare, Matillion Data Productivity Cloud is implemented to streamline ETL processes, optimize data pipeline construction, and enhance data migration efforts. It supports efficient data loading and integration between cloud and on-premises databases, aiding industries in managing data-driven projects.
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