IBM InfoSphere Advanced DataStage - Parallel Framework v11.5

• Describing the parallel processing architecture
• Describing pipeline and partition parallelism
• Describing the role of the configuration file
• Designing a job that creates robust test data

• Describing the main parts of the configuration file
• Describing the compile process and the OSH that the compilation process generates
• Describing the role and the main parts of the Score
• Describing the job execution process

• Understanding how partitioning works in the Framework
• Viewing partitioners in the Score
• Selecting partitioning algorithms
• Generating sequences of numbers (surrogate keys) in a partitioned, parallel environment

• Sorting data in the parallel framework
• Finding inserted sorts in the Score
• Reducing the number of inserted sorts
• Optimizing Fork-Join jobs
• Using Sort stages to determine the last row in a group
• Describing sort key and partitioner key logic in the parallel framework

• Describing how buffering works in parallel jobs
• Tuning buffers in parallel jobs
• Avoiding buffer contentions

• Describing virtual data sets
• Describing schemas
• Describing data type mappings and conversions
• Describing how external data is processed
• Handling nulls
• Working with complex data

• Creating a schema file
• Reading a sequential file using a schema
• Describing Runtime Column Propagation (RCP)
• Enabling and disabling RCP
• Creating and using shared containers

• Enabling Balanced Optimization functionality in Designer
• Describing the Balanced Optimization workflow
• Listing the different Balanced Optimization options.
• Pushing stage processing to a data source
• Pushing stage processing to a data target
• Optimizing a job accessing Hadoop HDFS file system
• Understanding the limitations of Balanced Optimizations 

An ETL tool, IBM InfoSphere DataStage is a component of IBM InfoSphere and the IBM Information Platforms Solutions family. It is offered in a variety of variants, including the Server Edition, the Enterprise Edition, and the MVS Edition, and it uses a graphical notation to design data integration solutions. Client-server architecture is used. Both Windows and Unix can be used to deploy the servers.

It is a potent data integration tool that is widely used in Data Warehousing projects to get the data ready for report generating.

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