Description
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Course details
| IBM course code: KM404G | Category: IBM Infosphere / DataStage |
| Delivery: Online & on-site** | Course length in days: 3 |
Target audience
Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek an understanding of the parallel framework architecture.
Desired Prerequisites:
IBM InfoSphere DataStage Essentials course or equivalent and at least one year of experience developing parallel jobs using DataStage.
Instructors
The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.
Added value
Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.
Agenda and course syllabus
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
Do you need to adapt this syllabus to your needs? Are you interested in other courses? Ask us without obligation.
Locations for on-site delivery
- Austria: Vienna
- Belgium: Brussels, Ghent
- Denmark: Cophenhagen
- Estonia: Tallinn
- Finland: Helsinki
- France: Paris, Marseille, Lyon
- Germany: Berlin, Munich, Cologne, Hamburg
- Greece: Athens, Thessaloniki
- Italy: Rome
- Louxemburg: Louxembourg (city)
- Netherlands: Amsterdam
- Norway: Oslo
- Portugal: Lisbon, Braga, Porto, Coimbra
- Slovakia: Bratislava
- Slovenia: Bratislava
- Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
- Sweden: Stockholm
- Turkey: Ankara
- United Kingdom: London
