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

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

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

• Sort data in the parallel framework
• Find inserted sorts in the Score
• Reduce the number of inserted sorts
• Optimize Fork-Join jobs
• Use Sort stages to determine the last row in a group
• Describe sort key and partitioner key logic in the parallel framework

• Describe how buffering works in parallel jobs
• Tune buffers in parallel jobs
• Avoid buffer contentions

• Describe virtual data sets
• Describe schemas
• Describe data type mappings and conversions
• Describe how external data is processed
• Handle nulls
• Work with complex data

• Create a schema file
• Read a sequential file using a schema
• Describe Runtime Column Propagation (RCP)
• Enable and disable RCP
• Create and use shared containers

• Enable Balanced Optimization functionality in Designer
• Describe the Balanced Optimization workflow
• List the different Balanced Optimization options.
• Push stage processing to a data source
• Push stage processing to a data target
• Optimize a job accessing Hadoop HDFS file system
• Understand the limitations of Balanced Optimizations