Description
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Course details
| IBM course code: KM423G | Category: IBM Infosphere / DataStage |
| Delivery: Online & on-site** | Course length in days: 2 |
Target audience
Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources.
Desired Prerequisites:
DataStage Essentials course or equivalent.
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
Unit 1 –Accessing databases
Topic 1: Connector stage overview
• Use Connector stages to read from and write to relational tables
• Working with the Connector stage properties
Topic 2: Connector stage functionality
• Before / After SQL
• Sparse lookups
• Optimize insert/update performance
Topic 3: Error handling in Connector stages
• Reject links
• Reject conditions
Topic 4: Multiple input links
• Designing jobs using Connector stages with multiple input links
• Ordering records across multiple input links
Topic 5: File Connector stage
• Read and write data to Hadoop file systems
Demonstration 1: Handling database errors
Demonstration 2: Parallel jobs with multiple Connector input links
Demonstration 3: Using the File Connector stage to read and write HDFS files
Unit 2 – Processing unstructured data
Topic 1: Using the Unstructured Data stage in DataStage jobs
• Extract data from an Excel spreadsheet
• Specify a data range for data extraction in an Unstructured Data stage
• Specify document properties for data extraction.
Demonstration 1: Processing unstructured data
Unit 3 – Data masking
Topic 1: Using the Data Masking stage in DataStage jobs
• Data masking techniques
• Data masking policies
• Applying policies for masquerading context-aware data types
• Applying policies for masquerading generic data types
• Repeatable replacement
• Using reference tables
• Creating custom reference tables
Demonstration 1: Data masking
Unit 4 – Using data rules
Topic 1: Introduction to data rules
• Using the Data Rules Editor
• Selecting data rules
• Binding data rule variables
• Output link constraints
• Adding statistics and attributes to the output information
Topic 2: Use the Data Rules stage to valid foreign key references in source data
Topic 3: Create custom data rules
Demonstration 1: Using data rules
Unit 5 – Processing XML data
Topic 1: Introduction to the Hierarchical stage
• Hierarchical stage Assembly editor
• Use the Schema Library Manager to import and manage XML schemas
Topic 2: Composing XML data
• Using the HJoin step to create parent-child relationships between input lists
• Using the Composer step
Topic 3: Writing Hierarchical data to a relational table
Topic 4: Using the Regroup step
Topic 5: Consuming XML data
• Using the XML Parser step
• Propagating columns
Topic 6: Transforming XML data
• Using the Aggregate step
• Using the Sort step
• Using the Switch step
• Using the H-Pivot step
Demonstration 1: Importing XML schemas
Demonstration 2: Compose hierarchical data
Demonstration 3: Consume hierarchical data
Demonstration 4: Transform hierarchical data
Unit 6: Updating a star schema database
Topic 1: Surrogate keys
• Design a job that creates and updates a surrogate key source key file from a dimension table
Topic 2: Slowly Changing Dimensions (SCD) stage
• Star schema databases
• SCD stage Fast Path pages
• Specifying purpose codes
• Dimension update specification
• Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions
Demonstration 1: Build a parallel job that updates a star schema database with two dimensions
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
