Mastering IBM InfoSphere DataStage for Enterprise Data Integration and Automation
You're under pressure. Data pipelines are breaking. Stakeholders demand faster insights. Migration deadlines loom. You’re expected to deliver rock-solid integration workflows - but the tools are complex, the documentation is fragmented, and the margin for error keeps shrinking. Every delayed ETL job costs time, money, and credibility. You know DataStage holds the solution, but without structured, expert-led mastery, you’re piecing together workarounds instead of building scalable architectures that stand up to enterprise demands. Inside Mastering IBM InfoSphere DataStage for Enterprise Data Integration and Automation, you don't just learn the interface - you gain a strategic command of the entire platform, from foundational job design to fault-tolerant automation at scale. This course transforms technical uncertainty into repeatable, board-level execution capability. Engineers like you, from Tier 1 banks to global logistics firms, have used this program to cut pipeline development time by 60%, deliver data migration projects ahead of schedule, and lead integration initiatives without relying on external consultants. One learner, Amina Rao, Principal Data Architect at a Fortune 500 financial services provider, delivered a cross-system consolidation in 18 days - a project previously estimated at 45. This is your bridge from reactive troubleshooting to proactive data engineering leadership. No more guesswork, fragmented tutorials, or outdated playbooks. You’ll walk through every layer of DataStage with precision, building real-world jobs, orchestrating workflows, and hardening them for production. The result? You go from overwhelmed to empowered - with not only deep technical fluency but also a formal Certificate of Completion issued by The Art of Service, recognised globally and frequently cited in promotions and internal mobility reviews. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, On-Demand, Built for Real Careers
This is a self-paced learning experience with immediate online access. There are no fixed dates, no weekly check-ins, and no artificial time pressure. You move at your speed, on your schedule - ideal for working professionals balancing delivery timelines and skill development. Most learners complete the core content in 4–6 weeks with consistent effort. However, many report implementing specific workflows - such as CDC integration or parameterized job sequences - within the first 72 hours of starting. Full Lifetime Access & Ongoing Updates
Once enrolled, you get lifetime access to all course materials. This includes every future update, revision, and newly added real-world integration pattern, at no additional cost. IBM evolves. Your skills must too. This course evolves with them. - Immediate access upon enrollment confirmation
- 24/7 global availability
- Fully mobile-friendly experience - learn on any device, anytime
Expert Support & Guidance
You’re not alone. Throughout the course, direct instructor support is available for technical clarification, design patterns, and architecture guidance. Questions are answered promptly by certified DataStage architects with 15+ years of enterprise delivery experience. Industry-Recognised Certification
Upon completion, you earn a verified Certificate of Completion issued by The Art of Service - a globally trusted credential in enterprise technology training. This certificate is frequently referenced in performance reviews, internal promotions, and technical leadership applications. Transparent Pricing, Zero Hidden Fees
The advertised price includes everything: all modules, hands-on exercises, downloadable assets, templates, and certification. No subscriptions, no renewal fees, no surprise charges. What you see is what you get. Secure Payment Options
We accept all major payment methods including Visa, Mastercard, and PayPal. Your transaction is encrypted and processed securely. No additional steps or third-party approvals are required. Zero-Risk Enrollment: Satisfied or Refunded
We guarantee your satisfaction. If you complete the first two modules and feel the course isn’t delivering on its promise, email us within 14 days for a full refund - no questions asked. This removes all risk and places confidence firmly in your hands. Confirmation & Access Process
After enrollment, you’ll receive a confirmation email. Your course access details, including login and navigation instructions, will be sent in a separate message once your account is fully provisioned. This ensures a secure and error-free onboarding experience. Will This Work For Me? Absolute Confidence, Regardless of Your Starting Point
Yes, this works - even if you’re new to ETL, transitioning from another integration tool, or returning to DataStage after years away. The curriculum is designed for clarity, not assumed knowledge. We start with core architecture principles and build methodically. This works even if you work in a heavily regulated environment like healthcare or finance, where audit trails, metadata governance, and job lineage are non-negotiable. You’ll learn to build compliant, auditable workflows from day one. This works even if your current role doesn’t yet involve DataStage - many enrollees have used this course to successfully pivot into data engineering, ETL development, or integration architecture roles. This course is trusted by data professionals in enterprises across North America, Europe, and Asia-Pacific. Our graduates include data engineers at multinational banks, integration specialists in government agencies, and cloud migration leads in logistics firms - all using the same structured, repeatable methodology taught here. Your success is not left to chance. This course eliminates ambiguity, reduces your learning curve, and gives you the confidence to lead with authority.
Extensive and Detailed Course Curriculum
Module 1: DataStage Foundations and Enterprise Context - Understanding the role of ETL in modern data architecture
- Positioning DataStage within hybrid and cloud data ecosystems
- Comparing DataStage with alternative integration tools
- Overview of InfoSphere Information Server components
- Understanding metadata repositories and the shared metadata model
- Installing and configuring IBM InfoSphere Information Server (conceptual walkthrough)
- Navigating the Web Console and Designer client
- Logging into the Director and understanding user roles
- Configuring security and access controls
- Setting up project-level design standards
Module 2: Core Job Design Principles and Architecture - Understanding sequential vs parallel processing models
- Designing jobs for performance and maintainability
- Using the Designer client to create new jobs
- Job properties, descriptions, and documentation standards
- Understanding compile vs run-time behaviour
- Managing dependencies between jobs
- Organising jobs into functional projects
- Using job parameters for flexibility
- Designing reusable job templates
- Version control best practices for job exports
Module 3: Sequential and Columnar File Processing - Connecting to flat files using Sequential File stages
- Handling delimited, fixed-width, and variable-length records
- Reading and writing CSV, TXT, and DAT files
- Processing compressed files (GZIP, ZIP) securely
- Configuring field mappings and column definitions
- Dealing with embedded delimiters and text qualifiers
- Using Columnar File stages for Parquet and ORC formats
- Importing schema definitions from external sources
- Validating file input structure before processing
- Setting up error handling for malformed records
Module 4: Database Integration and Connectivity - Configuring ODBC, JDBC, and native database connectors
- Connecting to DB2, Oracle, SQL Server, and MySQL
- Retrieving data using SQL queries in Source stages
- Writing data back to target tables
- Configuring connection strings securely
- Managing credentials using encrypted properties
- Using Lookup stages for reference data enrichment
- Optimising SQL pushdown for performance
- Handling bulk loads with DB2 Load and Oracle External Tables
- Managing transaction controls and commit settings
Module 5: Transformations and Business Logic - Using Transformer stages to apply business rules
- Writing stage variables and derivation expressions
- Mapping input to output columns accurately
- Implementing conditional logic with constraints
- Creating derived columns for reporting and audit
- Applying data type conversions
- Using built-in functions for strings, dates, and numbers
- Creating custom routines in DataStage BASIC
- Standardising data using reusable transforms
- Handling null values and missing data gracefully
Module 6: Data Quality and Cleansing Techniques - Implementing data profiling within DataStage
- Using the QualityStage integration pipeline
- Detecting duplicates using probabilistic matching
- Standardising names, addresses, and phone numbers
- Validating email and URL formats
- Flagging records for manual review
- Building feedback loops for data stewards
- Reconciling cleansed data with source systems
- Creating audit reports for data quality trends
- Integrating with governance frameworks like GDPR and CCPA
Module 7: Parallel Processing and Pipeline Optimisation - Understanding the parallel engine architecture
- Choosing between server and parallel jobs
- Configuring configuration files for parallel execution
- Distributing data using hash, round-robin, and entire methods
- Understanding partitioning and collecting stages
- Minimising data movement across nodes
- Tuning buffer sizes and memory allocation
- Using sequential to parallel and back transformations
- Monitoring data skew and load balancing
- Designing for horizontal scalability
Module 8: Error Handling and Fault Tolerance - Configuring reject links and error columns
- Routing bad records to quarantine tables
- Logging error details for troubleshooting
- Using Try-Catch patterns in job sequences
- Implementing retry logic for transient failures
- Setting up alerts for job failures
- Creating error summary reports
- Using After Job Reset and Before Job Subroutine hooks
- Designing idempotent jobs for safe reruns
- Recovering from partial failures without data loss
Module 9: Job Sequencing and Process Orchestration - Building job sequences in the Director client
- Chaining jobs using triggers and dependencies
- Using Start, End, and Wait-for-File stages
- Passing parameters between jobs
- Using nested sequences for modular design
- Scheduling sequences with cron and Control-M integration
- Monitoring sequence execution timelines
- Handling conditional branching in workflows
- Implementing time-based and event-driven triggers
- Designing restartable sequences
Module 10: Parameterisation and Dynamic Configuration - Using job parameters for environment-specific settings
- Passing parameters from sequences to jobs
- Storing parameters in external files and tables
- Using environment variables securely
- Building dynamic SQL statements
- Generating file paths at runtime
- Switching sources and targets without code changes
- Using parameter sets for dev, test, and prod
- Validating parameters before execution
- Documenting parameter usage across projects
Module 11: Real-Time and Change Data Capture (CDC) - Integrating with GoldenGate and other CDC tools
- Processing log-based change streams
- Mapping insert, update, and delete operations
- Synchronising data in near real-time
- Handling late-arriving dimension records
- Using CDC stages in parallel jobs
- Detecting duplicates in streaming data
- Building micro-batch ingestion pipelines
- Ensuring transactional consistency
- Monitoring CDC pipeline latency
Module 12: Cloud and Hybrid Data Integration - Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
Module 1: DataStage Foundations and Enterprise Context - Understanding the role of ETL in modern data architecture
- Positioning DataStage within hybrid and cloud data ecosystems
- Comparing DataStage with alternative integration tools
- Overview of InfoSphere Information Server components
- Understanding metadata repositories and the shared metadata model
- Installing and configuring IBM InfoSphere Information Server (conceptual walkthrough)
- Navigating the Web Console and Designer client
- Logging into the Director and understanding user roles
- Configuring security and access controls
- Setting up project-level design standards
Module 2: Core Job Design Principles and Architecture - Understanding sequential vs parallel processing models
- Designing jobs for performance and maintainability
- Using the Designer client to create new jobs
- Job properties, descriptions, and documentation standards
- Understanding compile vs run-time behaviour
- Managing dependencies between jobs
- Organising jobs into functional projects
- Using job parameters for flexibility
- Designing reusable job templates
- Version control best practices for job exports
Module 3: Sequential and Columnar File Processing - Connecting to flat files using Sequential File stages
- Handling delimited, fixed-width, and variable-length records
- Reading and writing CSV, TXT, and DAT files
- Processing compressed files (GZIP, ZIP) securely
- Configuring field mappings and column definitions
- Dealing with embedded delimiters and text qualifiers
- Using Columnar File stages for Parquet and ORC formats
- Importing schema definitions from external sources
- Validating file input structure before processing
- Setting up error handling for malformed records
Module 4: Database Integration and Connectivity - Configuring ODBC, JDBC, and native database connectors
- Connecting to DB2, Oracle, SQL Server, and MySQL
- Retrieving data using SQL queries in Source stages
- Writing data back to target tables
- Configuring connection strings securely
- Managing credentials using encrypted properties
- Using Lookup stages for reference data enrichment
- Optimising SQL pushdown for performance
- Handling bulk loads with DB2 Load and Oracle External Tables
- Managing transaction controls and commit settings
Module 5: Transformations and Business Logic - Using Transformer stages to apply business rules
- Writing stage variables and derivation expressions
- Mapping input to output columns accurately
- Implementing conditional logic with constraints
- Creating derived columns for reporting and audit
- Applying data type conversions
- Using built-in functions for strings, dates, and numbers
- Creating custom routines in DataStage BASIC
- Standardising data using reusable transforms
- Handling null values and missing data gracefully
Module 6: Data Quality and Cleansing Techniques - Implementing data profiling within DataStage
- Using the QualityStage integration pipeline
- Detecting duplicates using probabilistic matching
- Standardising names, addresses, and phone numbers
- Validating email and URL formats
- Flagging records for manual review
- Building feedback loops for data stewards
- Reconciling cleansed data with source systems
- Creating audit reports for data quality trends
- Integrating with governance frameworks like GDPR and CCPA
Module 7: Parallel Processing and Pipeline Optimisation - Understanding the parallel engine architecture
- Choosing between server and parallel jobs
- Configuring configuration files for parallel execution
- Distributing data using hash, round-robin, and entire methods
- Understanding partitioning and collecting stages
- Minimising data movement across nodes
- Tuning buffer sizes and memory allocation
- Using sequential to parallel and back transformations
- Monitoring data skew and load balancing
- Designing for horizontal scalability
Module 8: Error Handling and Fault Tolerance - Configuring reject links and error columns
- Routing bad records to quarantine tables
- Logging error details for troubleshooting
- Using Try-Catch patterns in job sequences
- Implementing retry logic for transient failures
- Setting up alerts for job failures
- Creating error summary reports
- Using After Job Reset and Before Job Subroutine hooks
- Designing idempotent jobs for safe reruns
- Recovering from partial failures without data loss
Module 9: Job Sequencing and Process Orchestration - Building job sequences in the Director client
- Chaining jobs using triggers and dependencies
- Using Start, End, and Wait-for-File stages
- Passing parameters between jobs
- Using nested sequences for modular design
- Scheduling sequences with cron and Control-M integration
- Monitoring sequence execution timelines
- Handling conditional branching in workflows
- Implementing time-based and event-driven triggers
- Designing restartable sequences
Module 10: Parameterisation and Dynamic Configuration - Using job parameters for environment-specific settings
- Passing parameters from sequences to jobs
- Storing parameters in external files and tables
- Using environment variables securely
- Building dynamic SQL statements
- Generating file paths at runtime
- Switching sources and targets without code changes
- Using parameter sets for dev, test, and prod
- Validating parameters before execution
- Documenting parameter usage across projects
Module 11: Real-Time and Change Data Capture (CDC) - Integrating with GoldenGate and other CDC tools
- Processing log-based change streams
- Mapping insert, update, and delete operations
- Synchronising data in near real-time
- Handling late-arriving dimension records
- Using CDC stages in parallel jobs
- Detecting duplicates in streaming data
- Building micro-batch ingestion pipelines
- Ensuring transactional consistency
- Monitoring CDC pipeline latency
Module 12: Cloud and Hybrid Data Integration - Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Understanding sequential vs parallel processing models
- Designing jobs for performance and maintainability
- Using the Designer client to create new jobs
- Job properties, descriptions, and documentation standards
- Understanding compile vs run-time behaviour
- Managing dependencies between jobs
- Organising jobs into functional projects
- Using job parameters for flexibility
- Designing reusable job templates
- Version control best practices for job exports
Module 3: Sequential and Columnar File Processing - Connecting to flat files using Sequential File stages
- Handling delimited, fixed-width, and variable-length records
- Reading and writing CSV, TXT, and DAT files
- Processing compressed files (GZIP, ZIP) securely
- Configuring field mappings and column definitions
- Dealing with embedded delimiters and text qualifiers
- Using Columnar File stages for Parquet and ORC formats
- Importing schema definitions from external sources
- Validating file input structure before processing
- Setting up error handling for malformed records
Module 4: Database Integration and Connectivity - Configuring ODBC, JDBC, and native database connectors
- Connecting to DB2, Oracle, SQL Server, and MySQL
- Retrieving data using SQL queries in Source stages
- Writing data back to target tables
- Configuring connection strings securely
- Managing credentials using encrypted properties
- Using Lookup stages for reference data enrichment
- Optimising SQL pushdown for performance
- Handling bulk loads with DB2 Load and Oracle External Tables
- Managing transaction controls and commit settings
Module 5: Transformations and Business Logic - Using Transformer stages to apply business rules
- Writing stage variables and derivation expressions
- Mapping input to output columns accurately
- Implementing conditional logic with constraints
- Creating derived columns for reporting and audit
- Applying data type conversions
- Using built-in functions for strings, dates, and numbers
- Creating custom routines in DataStage BASIC
- Standardising data using reusable transforms
- Handling null values and missing data gracefully
Module 6: Data Quality and Cleansing Techniques - Implementing data profiling within DataStage
- Using the QualityStage integration pipeline
- Detecting duplicates using probabilistic matching
- Standardising names, addresses, and phone numbers
- Validating email and URL formats
- Flagging records for manual review
- Building feedback loops for data stewards
- Reconciling cleansed data with source systems
- Creating audit reports for data quality trends
- Integrating with governance frameworks like GDPR and CCPA
Module 7: Parallel Processing and Pipeline Optimisation - Understanding the parallel engine architecture
- Choosing between server and parallel jobs
- Configuring configuration files for parallel execution
- Distributing data using hash, round-robin, and entire methods
- Understanding partitioning and collecting stages
- Minimising data movement across nodes
- Tuning buffer sizes and memory allocation
- Using sequential to parallel and back transformations
- Monitoring data skew and load balancing
- Designing for horizontal scalability
Module 8: Error Handling and Fault Tolerance - Configuring reject links and error columns
- Routing bad records to quarantine tables
- Logging error details for troubleshooting
- Using Try-Catch patterns in job sequences
- Implementing retry logic for transient failures
- Setting up alerts for job failures
- Creating error summary reports
- Using After Job Reset and Before Job Subroutine hooks
- Designing idempotent jobs for safe reruns
- Recovering from partial failures without data loss
Module 9: Job Sequencing and Process Orchestration - Building job sequences in the Director client
- Chaining jobs using triggers and dependencies
- Using Start, End, and Wait-for-File stages
- Passing parameters between jobs
- Using nested sequences for modular design
- Scheduling sequences with cron and Control-M integration
- Monitoring sequence execution timelines
- Handling conditional branching in workflows
- Implementing time-based and event-driven triggers
- Designing restartable sequences
Module 10: Parameterisation and Dynamic Configuration - Using job parameters for environment-specific settings
- Passing parameters from sequences to jobs
- Storing parameters in external files and tables
- Using environment variables securely
- Building dynamic SQL statements
- Generating file paths at runtime
- Switching sources and targets without code changes
- Using parameter sets for dev, test, and prod
- Validating parameters before execution
- Documenting parameter usage across projects
Module 11: Real-Time and Change Data Capture (CDC) - Integrating with GoldenGate and other CDC tools
- Processing log-based change streams
- Mapping insert, update, and delete operations
- Synchronising data in near real-time
- Handling late-arriving dimension records
- Using CDC stages in parallel jobs
- Detecting duplicates in streaming data
- Building micro-batch ingestion pipelines
- Ensuring transactional consistency
- Monitoring CDC pipeline latency
Module 12: Cloud and Hybrid Data Integration - Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Configuring ODBC, JDBC, and native database connectors
- Connecting to DB2, Oracle, SQL Server, and MySQL
- Retrieving data using SQL queries in Source stages
- Writing data back to target tables
- Configuring connection strings securely
- Managing credentials using encrypted properties
- Using Lookup stages for reference data enrichment
- Optimising SQL pushdown for performance
- Handling bulk loads with DB2 Load and Oracle External Tables
- Managing transaction controls and commit settings
Module 5: Transformations and Business Logic - Using Transformer stages to apply business rules
- Writing stage variables and derivation expressions
- Mapping input to output columns accurately
- Implementing conditional logic with constraints
- Creating derived columns for reporting and audit
- Applying data type conversions
- Using built-in functions for strings, dates, and numbers
- Creating custom routines in DataStage BASIC
- Standardising data using reusable transforms
- Handling null values and missing data gracefully
Module 6: Data Quality and Cleansing Techniques - Implementing data profiling within DataStage
- Using the QualityStage integration pipeline
- Detecting duplicates using probabilistic matching
- Standardising names, addresses, and phone numbers
- Validating email and URL formats
- Flagging records for manual review
- Building feedback loops for data stewards
- Reconciling cleansed data with source systems
- Creating audit reports for data quality trends
- Integrating with governance frameworks like GDPR and CCPA
Module 7: Parallel Processing and Pipeline Optimisation - Understanding the parallel engine architecture
- Choosing between server and parallel jobs
- Configuring configuration files for parallel execution
- Distributing data using hash, round-robin, and entire methods
- Understanding partitioning and collecting stages
- Minimising data movement across nodes
- Tuning buffer sizes and memory allocation
- Using sequential to parallel and back transformations
- Monitoring data skew and load balancing
- Designing for horizontal scalability
Module 8: Error Handling and Fault Tolerance - Configuring reject links and error columns
- Routing bad records to quarantine tables
- Logging error details for troubleshooting
- Using Try-Catch patterns in job sequences
- Implementing retry logic for transient failures
- Setting up alerts for job failures
- Creating error summary reports
- Using After Job Reset and Before Job Subroutine hooks
- Designing idempotent jobs for safe reruns
- Recovering from partial failures without data loss
Module 9: Job Sequencing and Process Orchestration - Building job sequences in the Director client
- Chaining jobs using triggers and dependencies
- Using Start, End, and Wait-for-File stages
- Passing parameters between jobs
- Using nested sequences for modular design
- Scheduling sequences with cron and Control-M integration
- Monitoring sequence execution timelines
- Handling conditional branching in workflows
- Implementing time-based and event-driven triggers
- Designing restartable sequences
Module 10: Parameterisation and Dynamic Configuration - Using job parameters for environment-specific settings
- Passing parameters from sequences to jobs
- Storing parameters in external files and tables
- Using environment variables securely
- Building dynamic SQL statements
- Generating file paths at runtime
- Switching sources and targets without code changes
- Using parameter sets for dev, test, and prod
- Validating parameters before execution
- Documenting parameter usage across projects
Module 11: Real-Time and Change Data Capture (CDC) - Integrating with GoldenGate and other CDC tools
- Processing log-based change streams
- Mapping insert, update, and delete operations
- Synchronising data in near real-time
- Handling late-arriving dimension records
- Using CDC stages in parallel jobs
- Detecting duplicates in streaming data
- Building micro-batch ingestion pipelines
- Ensuring transactional consistency
- Monitoring CDC pipeline latency
Module 12: Cloud and Hybrid Data Integration - Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Implementing data profiling within DataStage
- Using the QualityStage integration pipeline
- Detecting duplicates using probabilistic matching
- Standardising names, addresses, and phone numbers
- Validating email and URL formats
- Flagging records for manual review
- Building feedback loops for data stewards
- Reconciling cleansed data with source systems
- Creating audit reports for data quality trends
- Integrating with governance frameworks like GDPR and CCPA
Module 7: Parallel Processing and Pipeline Optimisation - Understanding the parallel engine architecture
- Choosing between server and parallel jobs
- Configuring configuration files for parallel execution
- Distributing data using hash, round-robin, and entire methods
- Understanding partitioning and collecting stages
- Minimising data movement across nodes
- Tuning buffer sizes and memory allocation
- Using sequential to parallel and back transformations
- Monitoring data skew and load balancing
- Designing for horizontal scalability
Module 8: Error Handling and Fault Tolerance - Configuring reject links and error columns
- Routing bad records to quarantine tables
- Logging error details for troubleshooting
- Using Try-Catch patterns in job sequences
- Implementing retry logic for transient failures
- Setting up alerts for job failures
- Creating error summary reports
- Using After Job Reset and Before Job Subroutine hooks
- Designing idempotent jobs for safe reruns
- Recovering from partial failures without data loss
Module 9: Job Sequencing and Process Orchestration - Building job sequences in the Director client
- Chaining jobs using triggers and dependencies
- Using Start, End, and Wait-for-File stages
- Passing parameters between jobs
- Using nested sequences for modular design
- Scheduling sequences with cron and Control-M integration
- Monitoring sequence execution timelines
- Handling conditional branching in workflows
- Implementing time-based and event-driven triggers
- Designing restartable sequences
Module 10: Parameterisation and Dynamic Configuration - Using job parameters for environment-specific settings
- Passing parameters from sequences to jobs
- Storing parameters in external files and tables
- Using environment variables securely
- Building dynamic SQL statements
- Generating file paths at runtime
- Switching sources and targets without code changes
- Using parameter sets for dev, test, and prod
- Validating parameters before execution
- Documenting parameter usage across projects
Module 11: Real-Time and Change Data Capture (CDC) - Integrating with GoldenGate and other CDC tools
- Processing log-based change streams
- Mapping insert, update, and delete operations
- Synchronising data in near real-time
- Handling late-arriving dimension records
- Using CDC stages in parallel jobs
- Detecting duplicates in streaming data
- Building micro-batch ingestion pipelines
- Ensuring transactional consistency
- Monitoring CDC pipeline latency
Module 12: Cloud and Hybrid Data Integration - Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Configuring reject links and error columns
- Routing bad records to quarantine tables
- Logging error details for troubleshooting
- Using Try-Catch patterns in job sequences
- Implementing retry logic for transient failures
- Setting up alerts for job failures
- Creating error summary reports
- Using After Job Reset and Before Job Subroutine hooks
- Designing idempotent jobs for safe reruns
- Recovering from partial failures without data loss
Module 9: Job Sequencing and Process Orchestration - Building job sequences in the Director client
- Chaining jobs using triggers and dependencies
- Using Start, End, and Wait-for-File stages
- Passing parameters between jobs
- Using nested sequences for modular design
- Scheduling sequences with cron and Control-M integration
- Monitoring sequence execution timelines
- Handling conditional branching in workflows
- Implementing time-based and event-driven triggers
- Designing restartable sequences
Module 10: Parameterisation and Dynamic Configuration - Using job parameters for environment-specific settings
- Passing parameters from sequences to jobs
- Storing parameters in external files and tables
- Using environment variables securely
- Building dynamic SQL statements
- Generating file paths at runtime
- Switching sources and targets without code changes
- Using parameter sets for dev, test, and prod
- Validating parameters before execution
- Documenting parameter usage across projects
Module 11: Real-Time and Change Data Capture (CDC) - Integrating with GoldenGate and other CDC tools
- Processing log-based change streams
- Mapping insert, update, and delete operations
- Synchronising data in near real-time
- Handling late-arriving dimension records
- Using CDC stages in parallel jobs
- Detecting duplicates in streaming data
- Building micro-batch ingestion pipelines
- Ensuring transactional consistency
- Monitoring CDC pipeline latency
Module 12: Cloud and Hybrid Data Integration - Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Using job parameters for environment-specific settings
- Passing parameters from sequences to jobs
- Storing parameters in external files and tables
- Using environment variables securely
- Building dynamic SQL statements
- Generating file paths at runtime
- Switching sources and targets without code changes
- Using parameter sets for dev, test, and prod
- Validating parameters before execution
- Documenting parameter usage across projects
Module 11: Real-Time and Change Data Capture (CDC) - Integrating with GoldenGate and other CDC tools
- Processing log-based change streams
- Mapping insert, update, and delete operations
- Synchronising data in near real-time
- Handling late-arriving dimension records
- Using CDC stages in parallel jobs
- Detecting duplicates in streaming data
- Building micro-batch ingestion pipelines
- Ensuring transactional consistency
- Monitoring CDC pipeline latency
Module 12: Cloud and Hybrid Data Integration - Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Connecting DataStage to AWS S3, Redshift, and RDS
- Integrating with Azure Blob Storage and Synapse
- Using Google Cloud Storage and BigQuery connectors
- Configuring secure cross-cloud data transfers
- Handling proxy and firewall settings
- Encrypting data in transit and at rest
- Managing cloud credentials with Key Management Services
- Designing for cloud cost optimisation
- Building hybrid ETL flows between on-prem and cloud
- Monitoring cloud data transfer performance
Module 13: Performance Tuning and Scalability - Analysing job performance using logs and metrics
- Identifying bottlenecks in transformation logic
- Tuning buffer sizes and parallelism settings
- Optimising lookup performance with sorted inputs
- Reducing disk I/O with in-memory processing
- Using surrogate keys to speed joins
- Parallelising independent job branches
- Choosing optimal partitioning strategies
- Monitoring CPU and memory usage
- Generating performance benchmark reports
Module 14: Metadata Management and Lineage Tracking - Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Understanding the Metadata Repository (MDM) structure
- Viewing data lineage across jobs and projects
- Documenting business definitions and ownership
- Exporting lineage reports for auditors
- Analysing impact of changes to source systems
- Integrating with Collibra and Alation
- Tagging assets with business context
- Using metadata for impact analysis
- Automating metadata documentation
- Enforcing naming conventions and standards
Module 15: DevOps, CI/CD, and Environment Management - Exporting and importing jobs using dsjob commands
- Versioning jobs with Git integration
- Automating deployments using shell scripts
- Setting up dev, test, stage, and prod environments
- Validating jobs before promotion
- Using Jenkins for continuous integration
- Parameterising environment-specific configurations
- Creating deployment checklists
- Rolling back failed deployments
- Documenting release notes for integration teams
Module 16: Security, Compliance, and Governance - Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Implementing role-based access control (RBAC)
- Encrypting sensitive job parameters
- Auditing job execution and access logs
- Masking PII data in test environments
- Integrating with enterprise SSO and LDAP
- Managing data retention policies
- Complying with SOX, HIPAA, and GDPR
- Documenting data handling procedures
- Using secure connections (SSL/TLS)
- Performing security reviews for integration jobs
Module 17: Monitoring, Logging, and Operational Support - Using the Director to monitor job status
- Interpreting log files and error messages
- Setting up custom alerting rules
- Integrating with Splunk and Datadog
- Creating operational dashboards
- Analysing job run times and success rates
- Generating daily execution summaries
- Handling batch windows and SLAs
- Dealing with job timeouts and hangs
- Documenting runbook procedures for L1/L2 support
Module 18: Real-World Project: Enterprise Data Warehouse Integration - Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Defining project scope and success criteria
- Designing star schema dimensions and facts
- Extracting data from multiple OLTP systems
- Transforming and cleansing source data
- Implementing slowly changing dimensions (SCD Type 1, 2, 3)
- Loading data into a data warehouse target
- Building aggregate tables for reporting
- Scheduling daily and monthly batches
- Validating data accuracy across layers
- Generating business-facing reconciliation reports
Module 19: Real-World Project: Cloud Data Lake Modernisation - Assessing legacy ETL pipelines for cloud migration
- Designing a cloud-native data lake architecture
- Replacing on-prem batch jobs with scalable cloud flows
- Partitioning data by date and source system
- Using Parquet format for performance and compression
- Implementing metadata tagging for discoverability
- Building ingestion pipelines from on-prem to cloud
- Automating file discovery and processing
- Validating data completeness and consistency
- Documenting the migration process for audit teams
Module 20: Certification Preparation and Career Application - Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways
- Reviewing key concepts for internal assessments
- Practicing with scenario-based exercises
- Preparing your project portfolio
- Documenting completed integration patterns
- Creating before-and-after performance comparisons
- Writing case studies for internal presentations
- Mapping skills to enterprise job roles
- Using the Certificate of Completion in job applications
- Negotiating salary increases with verified expertise
- Accessing alumni resources and continued learning pathways