Q1. What is IBM DataStage, and why is it used?
Ans. IBM DataStage is a powerful tool provided by IBM for designing, developing, and executing applications that fill data into data warehouses. It accomplishes this by extracting data from databases on Windows servers.
DataStage offers graphical visualizations for data integrations and supports data extraction from multiple sources. Due to its capabilities, it is considered one of the most potent Extract, Transform, Load (ETL) tools available. Companies can choose from different versions of DataStage, including Server Edition, MVS Edition, and Enterprise Edition, based on their specific requirements.
Q2. What are the characteristics of DataStage?
Ans. IBM DataStage possesses several key characteristics:
- It can be deployed on local servers and the cloud, making it flexible and adaptable to different environments.
- DataStage is user-friendly and significantly enhances the speed and flexibility of data integration processes.
- It has robust support for big data, allowing access through various methods such as JDBC integrator, JSON support, and distributed file systems.
Q3. Describe the DataStage architecture briefly.
Ans. The architecture of IBM DataStage follows a client-server model and varies for different versions. It consists of several components: Client components, Servers, Stages, Table definitions, Containers, Projects, and Jobs.
Q4. How can we run a job using the command line in DataStage?
Ans. To run a DataStage job using the command line, the command is: dsjob -run -jobstatus <projectname> <jobname>
Q5. List a few functions we can execute using the ‘dsjob’ command.
Ans. Various functions can be performed using the ‘dsjob’ command, including running a job, stopping a job, getting job information, displaying job reports, listing projects, jobs, stages, links, parameters, logs, and more.
Q6. What is a flow designer in IBM DataStage?
Ans. The flow designer in IBM DataStage is a web-based user interface used for creating, editing, loading, and running jobs within DataStage.
Q7. What are the main features of the flow designer?
Ans. The flow designer offers several key features, such as its usefulness in handling jobs with numerous stages, the ability to work with existing jobs without migration, and the convenience of using a provided palette for adding and removing connectors and operators on the designer canvas through simple drag-and-drop operations.
Q8. What is an HBase connector in DataStage?
Ans. An HBase connector is a tool used in DataStage to establish connections with databases and tables within the HBase database. Its primary functions include reading and writing data to and from the HBase database, performing parallel data reading, and utilizing HBase as a view table.
Q9. What is a Hive connector?
Ans. The Hive connector is another useful tool in DataStage that supports partition modes when reading data. This can be achieved through modulus partition mode and minimum-maximum partition mode.
Q10. List the different tiers of Infosphere Information Server
Ans. The Infosphere Information Server comprises the following tiers:
Client Tier: Used for development and complete administration via client programs and consoles.
Services Tier: Provides standard services like metadata and logging, along with module-specific services, using an application server, product modules, and other services.
Engine Tier: Contains logical components responsible for running jobs and tasks for product modules.
Metadata Repository Tier: Includes the metadata repository, analysis database, and computer for sharing metadata, shared data, and configuration information.
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Q11. What are the types of parallel processing in DataStage?
Ans. DataStage employs two types of parallel processing:
Data Partitioning: Breaks down records into partitions for processing, leading to increased efficiency in a linear model.
Data Pipelining: This involves data extraction from the source, passing it through a sequence of processing functions, and obtaining the required output.
Q12. What is OSH in DataStage?
Ans. OSH stands for Orchestrate Shell, a scripting language used internally by the parallel engine in DataStage.
Q13. What are the Players in DataStage?
Ans. Players are the workhorse processes in DataStage responsible for performing parallel processing. They are assigned to operators on each node.
Q14. What is a collection library in DataStage?
Ans. The collection library in DataStage consists of a set of operators used to collect partitioned data.
Q15. What are the types of collectors available in the collection library of DataStage?
Ans. The collection library offers different types of collectors, including Stromberg, Reindorsing, and Ordered collectors.
Q16. How is the source file populated in DataStage?
Ans. The source file can be populated in DataStage using SQL queries or the row generator extraction tool.
Q17. List all the different tiers of Infosphere Information Server.
Ans. The Infosphere Information Server is structured into different tiers, each serving a specific purpose:
- Client tier
- Services tier
- Engine tier
- Metadata Repository tier
Q18. Describe the Client tier of the Infosphere Information Server briefly.
Ans. The Client tier in Infosphere Information Server is where developers and administrators work with client programs and consoles to handle development and complete administration tasks.
Q19. Describe the Services tier of Infosphere Information Server briefly.
Ans. The Services tier of Infosphere Information Server provides standard services such as metadata, logging, and other module-specific services. It includes an application server, various product modules, and additional product services.
Q20. Describe the Engine tier of the Infosphere Information Server briefly.
Ans. The Engine tier of Infosphere Information Server consists of logical components responsible for running jobs and other tasks related to the product modules.
Q21. Describe the Metadata Repository tier of the Infosphere Information Server briefly.
Ans. The Metadata Repository tier of Infosphere Information Server includes the metadata repository, the analysis database, and the computer. It is a shared space for metadata, shared data, and configuration information.
Q22. What are the types of parallel processing in DataStage?
Ans. DataStage supports two types of parallel processing:
Data Partitioning: This involves breaking down records into partitions for processing, increasing efficiency in a linear model.
Data Pipelining: This approach extracts data from the source and processes it through a sequence of functions to produce the desired output.
Q23. What is OSH in DataStage?
Ans. OSH stands for Orchestrate Shell. It is a scripting language used internally by the parallel engine in DataStage.
Q24. How is the source file populated in DataStage?
Ans. The source file in DataStage can be populated using SQL queries or the row generator extraction tool.
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Frequently Asked Questions
Q1. What type of DataStage do we have?
Ans. DataStage Manager defines a set of functions in the routine. The DataStage program has three routines: job control routine, before or after subroutine, and transform function.
Q2. What’s the DataStage in ETL?
Ans. DataStage is an ETL tool to retrieve, measure, and transform data from source to destination; these sources may include relational databases, sequential files, archives, external data files, enterprises, etc.
Q3. How do you define stage types in DataStage?
Ans. When using databases and file stages, DataStage has three types: server job database stages. Server Job File Stages. Dynamic Relational Stages.
Q4. What’s the DataStage type of tool?
Ans. IBM DataStage is the most efficient method of data integration available to help you create, develop and manage jobs that bring together information in various ways. At its core, the DataStage tool supports extract, transform and load (ETL) and extract, load, and transform (ELT) patterns.
Q5. In DataStage, what’s the SCD Type 3?
Ans. Only information on the previous value of a dimension is entered in the database for the Type 3 Slow Change Dimension. An ‘old ‘or ‘previous’ column is created, which stores the immediate previous attribute.
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