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What you'll learn?
- Fundamentals of Data Integration: Understand the core concepts and types of data integration and explore real-world examples.
- Navigating IBM Information Server: Get acquainted with the components of IBM Information Server and its role in data integration.
- IBM Information Server Administration: Learn to navigate the IBM Information Server Administration Console and practice essential administrative tasks.
- Exploring IBM DataStage: Dive into the architecture of IBM DataStage, its key components, and practical uses
- Developing in IBM DataStage: Work hands-on in DataStage, create projects, explore job types, and utilize design elements for parallel processing.
- DataStage Administration: Acquire practical skills in DataStage administration, including user management, permissions, and environment variables.
- Metadata Management: Practice metadata management using DataStage Designer, importing, and exporting components.
- Creating Parallel Jobs: Engage in practical sessions to create parallel jobs, define parameters, and document your jobs effectively.
- Accessing Sequential Data: Hands-on experience in handling sequential data, utilizing the Sequential File stage, and managing reject links.
- Implementing Partitioning and Collecting Algorithms: Gain practical insights into partition parallelism, partitioning algorithms, and collecting strategies.
- Combining Data with Stages: Work with Lookup, Join, Merge, and Funnel stages, and practice their applications in real-world scenarios.
- Group Processing Stages: Learn to sort data effectively, remove duplicates, and utilize Aggregator stages in practical exercises.
- Transforming Data: Practice using the Transformer stage, constraints, and debugging techniques for data transformation.
- Repository Functions: Explore practical aspects of using the repository, finding differences between jobs, and performing impact analyses.
- Working with Relational Data: Engage in hands-on activities involving connector stages, reading and writing to database tables, and utilizing data connection ob
- Job Sequence Control: Gain practical experience in creating job sequences, defining triggers, and managing job activities through various stages.
- Real Practice: AWS Cloud Integration: Apply your skills to integrate data with AWS Cloud services in real-world scenarios.
Course Overview
Unlock the power of data integration with IBM DataStage, the industry-leading ETL (Extract, Transform, Load) tool. In this comprehensive course, you'll embark on a journey from data integration basics to advanced techniques, empowering you to harness the full potential of your data.
What You'll Learn:
-
Foundations of Data Integration: Begin by understanding the core concepts and types of data integration, laying a strong foundation for your journey.
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IBM Information Server: Explore the IBM Information Server ecosystem and its vital components to comprehend where DataStage fits in.
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Hands-On Administration: Get hands-on with DataStage administration tasks, managing users, roles, and permissions with ease.
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Mastering Metadata: Learn to work effectively with metadata, a crucial aspect of data integration, to streamline your processes.
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Parallel Jobs Creation: Dive into parallel job creation, understand its intricacies, and design efficient parallel jobs.
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Accessing Sequential Data: Master the art of accessing sequential data, a crucial skill in data integration.
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Advanced Algorithms: Explore partitioning and collecting algorithms, vital for efficient data processing.
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Combine Data Effectively: Get comfortable with stages like Lookup, Join, Merge, and Funnel to combine data seamlessly.
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Group Processing Stages: Learn to group process data, sort it, and aggregate it effectively.
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Transformer Stage: Dive deep into the Transformer stage and its capabilities for data transformation.
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Repository Functions: Understand repository functions, impact analysis, and how to compare different jobs.
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Relational Data Integration: Work with relational data using connector stages, read from and write to database tables.
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Job Sequence Control: Master job sequencing, control the flow of jobs, and create complex workflows.
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Real-world Practice: Apply your knowledge in real-world scenarios with practical AWS Cloud and Data Vault integration sessions.
Pre-requisites
- Basic Understanding of Data Concepts: A fundamental grasp of data concepts is recommended. Students should understand terms like data sources, data transformation, and data loading.
- SQL Knowledge (Optional): While not mandatory, having some familiarity with SQL (Structured Query Language) can be beneficial, especially when working with relational databases.
- Access to IBM DataStage: Ideally, students should have access to IBM DataStage software to practice and follow along with the course.
- IBM DataStage Software (Optional): If students want to practice the skills learned in the course, having access to IBM DataStage software is beneficial
- Desire to Learn: A genuine interest in data integration and a willingness to learn and practice the concepts taught in the course are essential.
Target Audience
- Data Professionals: Data analysts, data engineers who want to enhance their data integration skills using IBM DataStage.
- IT Professionals: IT specialists, software developers, and database administrators who need to work with data integration solutions.
- Business Analysts: Business analysts who want to understand how data integration impacts their data-driven decision-making processes.
- Students and Graduates: Students pursuing degrees or recent graduates looking to build a foundation in data integration and expand their job prospects.
- IBM DataStage Users: Users of IBM DataStage looking to deepen their knowledge, explore advanced features, and improve their job performance.
- Anyone Interested in Data Integration: If you have a general interest in data integration and want to learn how IBM DataStage can be used for these purposes, this course is suitable for you.
Curriculum 273 Lectures
-
Section 1 : Introduction to Data Integration
-
Section 2 : Data Integration in Data management
- Lecture 1 :
- The agenda of the session
- Lecture 2 :
- Data Integration in Data management
- Lecture 3 :
- Some concepts and kinds of Data Integration
- Lecture 4 :
- What does data integration look like?
-
Section 3 : Introduction to IBM Information Server
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Introduction to IBM Information Server
- Lecture 3 :
- Introduction to IBM Information Server (cont.)
- Lecture 4 :
- Key IBM Information Server Components
- Lecture 5 :
- IBM Information Server topology
- Lecture 6 :
- IBM Information Server topology Cont.
-
Section 4 : IBM Information Server Administration Console
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- IBM Information Server Administration Console
- Lecture 3 :
- IBM Information Server Administration Console (Cont.)
- Lecture 4 :
- Real Practice 1 – IBM Information Server Administration Console
-
Section 5 : Introduction to IBM DataStage
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- DataStage Architecture
- Lecture 3 :
- DataStage Administrator
- Lecture 4 :
- DataStage Designer
- Lecture 5 :
- DataStage Director
-
Section 6 : Developing in DataStage and Features
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Developing in DataStage
- Lecture 3 :
- DataStage project repository
- Lecture 4 :
- Types of DataStage Jobs
- Lecture 5 :
- Design Elements of Parallel Jobs
- Lecture 6 :
- Partition Parallelism
- Lecture 7 :
- Multi-Node Partitioning
- Lecture 8 :
- Job design versus execution
- Lecture 9 :
- Configuration File
- Lecture 10 :
- Configuration File (Cont.)
- Lecture 11 :
- Summary - Developing in IBM DataStage and Features
-
Section 7 : DataStage Administration
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit Objectives - DataStage Administration
- Lecture 3 :
- Information Server Web Console - Administration
- Lecture 4 :
- Web Console Login Window
- Lecture 5 :
- User and Group Management
- Lecture 6 :
- Creating a DataStage User ID
- Lecture 7 :
- Assign DataStage Roles
- Lecture 8 :
- DataStage Credentials
- Lecture 9 :
- DataStage Credentials Default Mapping
- Lecture 10 :
- Logging onto DataStage Administrator
- Lecture 11 :
- InforSphere DataStage Administration
- Lecture 12 :
- DataStage Administrator Tabs
- Lecture 13 :
- Environment variables
- Lecture 14 :
- Environment reporting variables
- Lecture 15 :
- DataStage Administrator Permissions tab
- Lecture 16 :
- Add users and groups
- Lecture 17 :
- Specify DataStage Role
- Lecture 18 :
- DataStage Administrator Logs Tab
- Lecture 19 :
- DataStage Administrator Parallel Tab
- Lecture 20 :
- Real Practice 2 - Task 1 - Information Server Administration - User & Group
- Lecture 21 :
- Real Practice 2 - Task 2 - Project Properties - General
- Lecture 22 :
- Real Practice 2 - Task 3 - Project Properties - Environment
- Lecture 23 :
- Real Practice 2 - Task 4 - Project Properties - User & Group
- Lecture 24 :
- Summary - DataStage Administration
-
Section 8 : Work with metadata
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit Objectives - Work with metadata
- Lecture 3 :
- Logging onto DataStage Designer
- Lecture 4 :
- DataStage Designer
- Lecture 5 :
- DataStage project repository
- Lecture 6 :
- Import and Export
- Lecture 7 :
- Exporting DataStage Components
- Lecture 8 :
- Export objects
- Lecture 9 :
- Import Procedure
- Lecture 10 :
- Import objects
- Lecture 11 :
- Real Practice 3 - Export & Import DataStage Components
- Lecture 12 :
- Summary - Work with metadata
-
Section 9 : Create Parallel Job
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit objectives - Create parallel jobs
- Lecture 3 :
- What is a parallel job?
- Lecture 4 :
- Tools Palette
- Lecture 5 :
- Create a new parallel job
- Lecture 6 :
- Drag stages and links from the Palette
- Lecture 7 :
- Rename links and stages
- Lecture 8 :
- Connection Properties
- Lecture 9 :
- Job parameters
- Lecture 10 :
- Define a job parameter
- Lecture 11 :
- Use a job parameter in a stage
- Lecture 12 :
- Add job documentation
- Lecture 13 :
- Job Properties window documentation
- Lecture 14 :
- Compile and run a job
- Lecture 15 :
- Compile, Errors or Successful message
- Lecture 16 :
- DataStage Director
- Lecture 17 :
- Run options
- Lecture 18 :
- Performance statistics
- Lecture 19 :
- Director Status View
- Lecture 20 :
- Job log, viewed from designer
- Lecture 21 :
- Other job log functions
- Lecture 22 :
- Director monitor
- Lecture 23 :
- Run jobs from the command line
- Lecture 24 :
- Parameter sets
- Lecture 25 :
- Create a parameter set
- Lecture 26 :
- Defining the parameters
- Lecture 27 :
- Defining values files
- Lecture 28 :
- Load a parameter set into a job
- Lecture 29 :
- Use parameter set parameters
- Lecture 30 :
- Run jobs with parameter set parameters
- Lecture 31 :
- Real Practice 4 - Task 1 - Database --> Transformer --> Database
- Lecture 32 :
- Real Practice 4 - Task 2 - Database --> Transformer --> Database with Parameter
- Lecture 33 :
- Summary - Create parallel jobs
-
Section 10 : Create parallel jobs - Access sequential data
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit objectives - Access sequential data
- Lecture 3 :
- How sequential data is handled
- Lecture 4 :
- Features of the Sequential File stage
- Lecture 5 :
- Job design with Sequential File Stages
- Lecture 6 :
- Sequential File stage properties
- Lecture 7 :
- Format Tab
- Lecture 8 :
- Column Tab
- Lecture 9 :
- Multiple Readers
- Lecture 10 :
- Writing to a sequential file
- Lecture 11 :
- Reject links
- Lecture 12 :
- Source and target reject links
- Lecture 13 :
- Setting the Reject Mode property
- Lecture 14 :
- Real Practice 5 - Task 1 - File --> Transformer --> File
- Lecture 15 :
- Real Practice 5 - Task 2 - File --> Transformer --> DataSet with Parameter
- Lecture 16 :
- Real Practice 6 - File --> Transformer --> DataSet
- Lecture 17 :
- Summary - Access sequential data
-
Section 11 : Partitioning and collecting algorithms
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit objectives - Partitioning and collecting algorithms
- Lecture 3 :
- Partition parallelism
- Lecture 4 :
- Stage partitioning
- Lecture 5 :
- DataStage hardware environments
- Lecture 6 :
- Partitioning algorithms
- Lecture 7 :
- Collecting
- Lecture 8 :
- Collecting (Cont.)
- Lecture 9 :
- Collecting algorithms
- Lecture 10 :
- Keyless versus keyed partitioning algorithms
- Lecture 11 :
- Round Robin and Random partitioning
- Lecture 12 :
- Entire partitioning
- Lecture 13 :
- Hash partitioning
- Lecture 14 :
- Modulus partitioning
- Lecture 15 :
- Auto partitioning
- Lecture 16 :
- Partitioning / collecting link icons
- Lecture 17 :
- More partitioning icons
- Lecture 18 :
- Specify a partitioning algorithm
- Lecture 19 :
- Specify a collecting algorithm
- Lecture 20 :
- Configuration file
- Lecture 21 :
- Example configuration file
- Lecture 22 :
- Adding $APT_CONFIG_FILE as a job parameter
- Lecture 23 :
- Editing configuration files
- Lecture 24 :
- Summary - Partitioning and collecting algorithms
-
Section 12 : Combine Data
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit objectives - Combine Data
- Lecture 3 :
- Combine Data
- Lecture 4 :
- Lookup, Join, Merge stages
- Lecture 5 :
- Lookup Stage features
- Lecture 6 :
- Lookup types
- Lecture 7 :
- Match Lookup Stage Example
- Lecture 8 :
- Lookup Stage with an equality match
- Lecture 9 :
- Define the Lookup key
- Lecture 10 :
- Specify the output columns
- Lecture 11 :
- Lookup failure actions
- Lecture 12 :
- Specifying lookup failure actions
- Lecture 13 :
- Lookup stage with reject link
- Lecture 14 :
- Real Practice 7 - Task 1 - Combine data with Lookup Stage
- Lecture 15 :
- Join stage
- Lecture 16 :
- Job with Join Stage
- Lecture 17 :
- Join Stage Properties
- Lecture 18 :
- Output Mapping tab
- Lecture 19 :
- Merge stage
- Lecture 20 :
- Merge Stage Job
- Lecture 21 :
- Merge Stage Properties
- Lecture 22 :
- Funnel stage
- Lecture 23 :
- Funnel Stage Example
- Lecture 24 :
- Funnel Stage Properties
- Lecture 25 :
- Real Practice 7 - Task 2 - Combine data with Join Stage
- Lecture 26 :
- Real Practice 7 - Task 3 - Combine Data with Merge Stage
- Lecture 27 :
- Real Practice 7 - Task 4 - Combine Data with Funnel Stage
- Lecture 28 :
- Summary - Combine Data
-
Section 13 : Group processing stages
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit objectives - Group processing stages
- Lecture 3 :
- Group processing stages
- Lecture 4 :
- Sort data
- Lecture 5 :
- Sorting alternatives
- Lecture 6 :
- In-Stage sorting
- Lecture 7 :
- Sort Stage Properties tab
- Lecture 8 :
- Specify sort keys
- Lecture 9 :
- Partition sorts
- Lecture 10 :
- Aggregator stage
- Lecture 11 :
- Job with Aggregator Stage
- Lecture 12 :
- Aggregation types
- Lecture 13 :
- Aggregation types (Cont.)
- Lecture 14 :
- Output Mapping Tab
- Lecture 15 :
- Output Columns tab
- Lecture 16 :
- Calculation aggregation type
- Lecture 17 :
- Grouping methods
- Lecture 18 :
- Remove duplicates
- Lecture 19 :
- Remove Duplicates stage job
- Lecture 20 :
- Remove Duplicates stage properties
- Lecture 21 :
- Fork - Join Job Design
- Lecture 22 :
- Real Practice 8 - File --> Transformer --> DataSet --> Aggregator --> File
- Lecture 23 :
- Summary - Group processing stages
-
Section 14 : Transfromer Stage
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit Objectives - Transformer stage
- Lecture 3 :
- Transformer stage
- Lecture 4 :
- Job with a Transformer stage
- Lecture 5 :
- Inside the Transformer stage
- Lecture 6 :
- Transformer stage elements
- Lecture 7 :
- Transformer stage elements
- Lecture 8 :
- Constraints
- Lecture 9 :
- Constraints example
- Lecture 10 :
- Define a constraint
- Lecture 11 :
- Use the expression editor
- Lecture 12 :
- Otherwise links for data integrity
- Lecture 13 :
- Real Practice 9 - File --> Transformer --> File
- Lecture 14 :
- Parallel job debugger
- Lecture 15 :
- Set breakpoints
- Lecture 16 :
- Edit breakpoints
- Lecture 17 :
- Running a parallel job in the debugger
- Lecture 18 :
- Add columns to the watch list
- Lecture 19 :
- Checkpoint - Transformer stage
-
Section 15 : Repository functions
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit objectives - Repository funcions
- Lecture 3 :
- Quick find
- Lecture 4 :
- Found results
- Lecture 5 :
- Advanced Find window
- Lecture 6 :
- Advanced Find options
- Lecture 7 :
- Using the found results
- Lecture 8 :
- Performing an impact analysis
- Lecture 9 :
- Initiating an impact analysis
- Lecture 10 :
- Results in text format
- Lecture 11 :
- Results in graphical format
- Lecture 12 :
- Displaying the dependency graph
- Lecture 13 :
- Displaying the dependency path
- Lecture 14 :
- Viewing column-level data flow
- Lecture 15 :
- Finding where a column originates
- Lecture 16 :
- Displayed results
- Lecture 17 :
- Finding the difference between two jobs
- Lecture 18 :
- Initiating the comparison
- Lecture 19 :
- Comparison results
- Lecture 20 :
- Saving to an HTML file
- Lecture 21 :
- Summary - Repository functions
-
Section 16 : Work with relational data
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit Objectives - Work with relational data
- Lecture 3 :
- Connector stages
- Lecture 4 :
- Reading from database tables
- Lecture 5 :
- Connector stage GUI
- Lecture 6 :
- Navigation panel
- Lecture 7 :
- Connection properties
- Lecture 8 :
- Usage properties - Session and Before/After SQL
- Lecture 9 :
- Writing to database tables
- Lecture 10 :
- DB2 Connector GUI
- Lecture 11 :
- Connector write properties
- Lecture 12 :
- Data connection objects
- Lecture 13 :
- Data connection objects
- Lecture 14 :
- Creating a new data connection object
- Lecture 15 :
- Real Practice 11 - Relational Database --> Transformer --> File
- Lecture 16 :
- Summary - Work with relational data
-
Section 17 : Job Sequence Control
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Unit Objectives - Job Sequence control
- Lecture 3 :
- What is a job sequence?
- Lecture 4 :
- Basics for creating a job sequence
- Lecture 5 :
- Job sequence stages
- Lecture 6 :
- Job sequence example
- Lecture 7 :
- Job Activity stage properties
- Lecture 8 :
- Job Activity trigger
- Lecture 9 :
- Execute Command stage
- Lecture 10 :
- Notification Activity stage
- Lecture 11 :
- User Variables stage
- Lecture 12 :
- Wait for File stage
- Lecture 13 :
- Sequencer stage
- Lecture 14 :
- Nested Condition stage
- Lecture 15 :
- Loop stages
- Lecture 16 :
- Summary - Job control
-
Section 18 : AWS Cloud Integration
- Lecture 1 :
- The agenda of this session
- Lecture 2 :
- Real Practice 12 - AWS Cloud Data Integration with IBM DataStage S3 Connector
-
Section 19 : Data Vault 1.0 & 2.0 Integration
- Lecture 1 :
- Data Vault Introduction
- Lecture 2 :
- Data Vault Integration with IBM DataStage
- Lecture 3 :
- Real Practice 13 - Mini Project 1 - Data Vault 1.0 DWH with IBM DataStage
- Lecture 4 :
- Real Practice 13 - Mini Project 2 - Data Vault 2.0 DWH with IBM DataStage
-
Section 20 : Summary Session
- Lecture 1 :
- What have learned so far
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Instructor
2173 Course Views
5 Courses
I am an experienced and accomplished author with a deep passion for data strategy and management. With over 15 years of hands-on experience in the IT industry, I have gained extensive knowledge and expertise in various domains, including banking, insurance, retail, and more.
Throughout my career, I have held several senior positions such as:
- Data Division Director of an insurance company
- Deputy Data Governance & Analytics of the Group (Real Estate, Retail, Hospitality, Commerce, ...)
- Deputy Chief Data Officer cum Senior IT Strategy Expert
- Head of Data & Analytics Service
- Project Director/ Project Manager
- Enterprise & Solution Architect of bank
That have allowed me to make significant contributions to the field. And right now, as a director of Data Division, I played a pivotal role in shaping data initiatives and driving the effective use of data within organizations. Additionally, as a Data Governance & Analytics expert, I focused on establishing robust data governance frameworks and harnessing the power of analytics to drive actionable insights.
With my diverse background and extensive industry experience, I have developed a strong understanding of the challenges and opportunities in the data management landscape. Through my work as an author, I am dedicated to sharing my knowledge, insights, and practical strategies with students like you.
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