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DataStage - Run Time Column Propagation. While I am developing DataStage jobs I came across a situation which surprise me and took some time to figure it out What was the issue. So We have source file and reference file which joins through look-up stage. At Look-up Stage we dropped few columns and load it into target file. surprisingly We see dropped Columns also even we were not configured. Reason is when we check "Run Time Column Propagation" option weather you required or not those columns will flow towards target. So Keep on eye on Run Time Column Propagation option when you are dropping columns.
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Day26of100 #datastage #datastagedeveloper #movingtowardsdataengineering #ARCHITECTURE_OF_DATASTAGE : #Client_Tier: Users interact with DataStage through client applications, such as the DataStage Designer, Director, and Administrator clients. The Designer is used for designing and developing data integration jobs. The Director is used for managing and monitoring job execution. The Administrator client is used for configuring and managing DataStage projects, users, and resources. #Server_Tier: The server tier consists of DataStage engine components responsible for executing data integration jobs. It includes the DataStage engine, which orchestrates job execution, handles metadata operations, and manages job repositories. The engine supports parallel processing and can distribute job execution across multiple processing nodes for scalability and performance. #Repository_Database: DataStage stores metadata, job definitions, and configuration information in a repository database. The repository database serves as a central repository for storing and managing metadata related to DataStage jobs, stages, parameters, and job schedules. It enables version control, lineage tracking, and collaboration among multiple users working on DataStage projects. #DataStage_Components: DataStage jobs are composed of various components, including stages, links, containers, and parameters. Stages represent individual processing units within a job and perform specific data integration tasks, such as reading data from sources, transforming data, and loading data into targets. Links define the flow of data between stages, specifying the sequence and direction of data movement within the job. Containers are reusable components that encapsulate groups of stages and logic within a job, promoting modularity and reusability. Parameters allow users to define and manage configurable properties within jobs, facilitating flexibility and customization. #Parallel_Processing: DataStage employs a parallel processing architecture to optimize job execution and maximize hardware utilization. Parallelism allows jobs to be divided into smaller, parallel tasks that can be executed concurrently across multiple processing nodes. DataStage dynamically allocates resources and workload distribution, ensuring efficient utilization of computing resources and minimizing job execution times. #Data_Integration_Connectivity: DataStage provides connectivity to a wide range of data sources and targets, including databases, flat files, cloud platforms, enterprise applications, and big data systems. It offers built-in connectors, adapters, and drivers to facilitate seamless data integration across heterogeneous environments. DataStage supports both batch and real-time data integration scenarios, enabling organizations to process data in near real-time and meet diverse business requirements. Follow karthikeyan udayakumar #learningeveryday #learningandgrowing
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DataStage users, When you create a simple source to target job in which you need to add a timestamp column in between, what stage do you use to both add the column and call a Current_timestamp() function? My understanding is that transformers take a toll on performance so I am wondering if you use a transformer or a different stage.
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