1: Introduction to the parallel framework architecture
- 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
2: Compiling and executing jobs
- 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
3: Partitioning and collecting data
- 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
4: Sorting data
- 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
5: Buffering in parallel jobs
- Describe how buffering works in parallel jobs
- Tune buffers in parallel jobs
- Avoid buffer contentions
6: Parallel framework data types
- Describe virtual data sets
- Describe schemas
- Describe data type mappings and conversions
- Describe how external data is processed
- Handle nulls
- Work with complex data
7: Reusable components
- 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
8: Balanced Optimization
- 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
