Description

Duration

2 Days

12 CPD hours

This course is intended for

Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources.

Overview

Use Connector stages to read from and write to database tables
Handle SQL errors in Connector stages
Use Connector stages with multiple input links
Use the File Connector stage to access Hadoop HDFS data
Optimize jobs that write to database tables
Use the Unstructured Data stage to extract data from Excel spreadsheets
Use the Data Masking stage to mask sensitive data processed within a DataStage job
Use the Hierarchical stage to parse, compose, and transform XML data
Use the Schema Library Manager to import and manage XML schemas
Use the Data Rules stage to validate fields of data within a DataStage job
Create custom data rules for validating data
Design a job that processes a star schema data warehouse with Type 1 and Type 2 slowly changing dimensions

This course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course.

Accessing databases

Connector stage overview

- Use Connector stages to read from and write to relational tables

- Working with the Connector stage properties

Connector stage functionality

- Before / After SQL

- Sparse lookups

- Optimize insert/update performance

Error handling in Connector stages

- Reject links

- Reject conditions

Multiple input links

- Designing jobs using Connector stages with multiple input links

- Ordering records across multiple input links

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

Processing unstructured data

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

Data masking

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

Using data rules

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

Use the Data Rules stage to valid foreign key references in source data

Create custom data rules

Demonstration 1: Using data rules

Processing XML data

Introduction to the Hierarchical stage

- Hierarchical stage Assembly editor

- Use the Schema Library Manager to import and manage XML schemas

Composing XML data

- Using the HJoin step to create parent-child relationships between input lists

- Using the Composer step

Writing Hierarchical data to a relational table

Using the Regroup step

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

Updating a star schema database

Surrogate keys

- Design a job that creates and updates a surrogate key source key file from a dimension table

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

Additional course details:

Nexus Humans KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing

By Nexus Human

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