The primary use case is that we are using IBM InfoSphere DataStage to extract the data from the different source systems to build the warehouse.

I use IBM InfoSphere DataStage mostly for the ETL process of extraction, transforming, or loading data. For example, when I need to extract information from consumer tables, I then perform cleansing, remove duplicates, do standardization, and finally load the data into a data warehouse itself.

I was working with a client that was a bank. As a bank has many transactions per day, I was using IBM InfoSphere DataStage to customize a new table for the company that we were providing services. We performed the whole process of extraction, transforming, or loading.

I also use the orchestrator in IBM InfoSphere DataStage to run pipelines, schedule pipelines, and define roles.

We are using IBM DataFeeds for data conversion activities and also for data warehousing. Our team focuses on converting data from a legacy system to a modernized platform. We use this solution quickly for data conversion and for the data warehousing aspect.

We used various ETA steps, such as SDGs and data management. We employed DataStage for integration, governance, data management, and data quality cleaning steps. Additionally, we leveraged real-time data applications for projects in both government and corporate sectors. I have over five years of experience with DataStage and am familiar with all steps, methodologies, best practices, and ETL strategies.

The tool is used for ETL. It is one of the leading tools in the industry.

We primarily use IBM InfoSphere DataStage for ETL scenarios, extracting, transforming, and loading data across various systems efficiently. Although we experimented with using it for real-time or on-time integrations in the past few years, our main focus remains on its strength in traditional batch data integration processes.

The primary use case for IBM InfoSphere DataStage is related to standard business intelligence (BI) and extract, transform, load (ETL) operations.

I use it for ETL processes and data quality. 

We have integrated multiple data sources into a single data warehouse. For this, we used to build complex ETL jobs and datasets to integrate data from multiple sources into a single data warehouse. So, these are basically the use cases.

I'm working for an insurance provider. They have applications where they register claims, insurance, et cetera. We get a flat file from the vendor and put those flat files into our Oracle Data Warehouse and report on the data. We publish those reports to our institutional investors, partners, and business users, including banks. 

IBM InfoSphere DataStage was mostly used for ETL and data integration purposes, so extract, transfer, and load, including some data quality use cases. My team used the solution to extract data from various sources, do some business transformations, load the data into a target database, or generate files.

We migrate data from old databases to new databases. 

Right now, I'm working for a telecom company. So, we are using IBM InfoSphere DataStage for constructing ETL jobs for them so that they can load data from their various different sources into their warehouse.

We use the solution for data warehousing and data migration. 

The solution is mainly used for, marketing campaigns, customer segmentation, and home loans.

We use it for many kinds of projects. For example, we use it for business intelligence, master data management, data quality, data governance, data integration to SAP, Oracle retail, and real-time integration.

The product is primarily used for  intense data transformation; it's part of the risk management, and dataflow, and is sourcing data from the data warehouse on the SAP Sybase platform.

The primary use case is for ETL purposes for when we're building data warehouses or data marts. We use it to get the data from different disparate sources, do some ETL on them, and we use DataStage and then load them into the data warehouse, database, or data mart.

This solution used to be on-premises, but they've recently come out with a hybrid offering.

We are supporting a healthcare domain vendor located in the US. We get data from various domains, such as health insurance. We have member data, provider data, and consumer data. We also have client-related stuff and broker-related commission data. 

We get the data from these domains, and after receiving it, we apply the transformation rules, such as joints. We also do the standardization of data by formatting and doing field validations, such as formatting the date field and doing data and time validations. We also do other normal transformations with some business logic. After applying all this, we send the data to the business.

We are a consulting company and we use this solution for our clients. We set up the data for them. We have various healthcare-related information from their vendor and business partners. They have integrated them and get data reports from it.

We use IBM InfoSphere DataStage to extract data from different sources and perform business logic. It helps us in data transformation and loading into our data warehouse. The tool is also used for reporting purposes and selecting different extract files.

IBM InfoSphere DataStage is a core ETL tool. We use it with source systems like mainframes. DataStage is perfectly suited for extracting data from mainframes.

I work as a consultant and I have several projects with the Russian banks. My main expertise is building data warehouses and I use the product as a ETL. 

We started using this solution as we needed a system upgrade. We had to move the Db2 data to a AS400 system. 

I am a consultant. I provide product information for our clients.

The most powerful use of this solution is for full delivery integration. Aside from the ETL aspect, it makes data lineage. We are customers of IBM InfoSphere and I'm head of IT integration and finance transformation.

We use it for creating a pattern for data integration with our data vault. We have also used it for creating APIs.

We are a solution provider and this is one of the products that we implement for our clients.

This solution has an end-to-end process used for data integration.

We primarily use this product for data loading. We take data from different applications and databases and put it into our data warehouse. 

We are using this solution with enterprise customers for ETL (Extract, Transform and Load). The clients are just using it for retail processes.  

I'm an enterprise and information architect, and we're a customer of IBM. I personally don't use the product but assist companies in the market do for making the integration picture and design.

We primarily use the solution for the UTS tool as well a for billing and data. We use it to clean the data from different systems and for pulling in data.

My customers use it. I don't use this solution. It's used to extract data from several different sources, as well as to store data and load it into a data warehouse.

My company is a consulting firm and I'm a managing partner. We consult for large size companies in the telecommunications, banking and insurance sectors. We partner with IBM. We're not using the latest version of DataStage but one of the more recent ones. I use the product for a migration project. It's a DB2 to Oracle migration project and the source database is mainframe. I need DataStage for that purpose.

It is in the environment of our client, who is a large Russian bank. They are in the top 20, as of August, and have the re-maintenance project of their data warehouse solution based on IBM technologies. They use IBM BWD, a banking data model, on Netezza and DataStages in ETL tools. It is a native case.

We are using the on-premise deployment model.

We implement this solution for our customers. The majority of them are Enterprise companies.

We are a consultancy company, so we are not using DataStage for our own purposes. We deploy it for our customers. We use it to supply data integration and data warehousing solutions based on the specific needs of clients.

We primarily use the solution for ETL jobs and movement of data.

Complex data integration projects which require integration from multiple data sources.