Matillion — Cloud Data Integration & ETL Development

Module 1: Introduction to Matillion

• What is Matillion and its position in the modern cloud data stack
• Matillion ETL vs. Matillion Data Productivity Cloud (MDPC): understanding the product evolution
• Matillion architecture: agent-based design, cloud warehouse pushdown, and job execution model
• Matillion vs. Azure Data Factory vs. dbt: when to use each tool
• Setting up your Matillion environment and connecting to a cloud data warehouse

Module 2: Matillion Interface and Core Concepts

• Navigating the Matillion ETL interface: project canvas, job designer, and component library
• Understanding Matillion job types: Orchestration Jobs and Transformation Jobs
• Projects, environments, and version management in Matillion
• Matillion components overview: input, transform, output, and run categories
• Best practices for organizing Matillion projects and job naming conventions

Module 3: Data Ingestion and Input Components

• Loading data from cloud storage: S3, Azure Blob, and Google Cloud Storage input components
• Database input components: querying SQL Server, Oracle, MySQL, and PostgreSQL sources
• Matillion Query Result input: using SQL SELECT as a transformation source
• REST API Query component for ingesting data from web APIs and SaaS platforms
• File format handling: CSV, JSON, Parquet, and Avro ingestion with Matillion

Module 4: Transformation Components and Data Processing

• Calculator component: building derived columns and business logic transformations
• Filter component: row-level filtering and conditional data selection
• Join component: inner, left, right, and cross joins between data streams
• Aggregate component: GROUP BY aggregations, sum, count, and window functions
• Rank, Distinct, Sort, and Limit components for data shaping and ordering

Module 5: Advanced Transformation Patterns

• Pivot and Unpivot components for reshaping wide and narrow data structures
• Assert component: inline data quality validation and pipeline failure handling
• Convert Type component: data type casting and format standardization
• Detect Changes component: identifying inserts, updates, and deletes for SCD processing
• Implementing Slowly Changing Dimension Type 1 and Type 2 patterns in Matillion

Module 6: Orchestration Jobs and Pipeline Control Flow

• Orchestration Job design: sequencing jobs, handling success, failure, and unconditional paths
• Run Transformation Job and Run Orchestration Job components for modular pipeline design
• Iterator component: looping over query results and grid variable values dynamically
• If component: conditional branching based on variable values and job outcomes
• Implementing error handling and notification patterns in Orchestration Jobs

Module 7: Variables, Parameters, and Reusable Pipeline Design

• Matillion variable types: job variables, environment variables, and grid variables
• Using variables for dynamic SQL, file paths, date ranges, and runtime parameters
• Grid variables: processing multiple values in a single iterator loop
• Building reusable shared jobs for common transformation and ingestion patterns
• Parameterized pipeline design: building metadata-driven ETL with Matillion variables

Module 8: Scheduling, Automation, and Monitoring

• Matillion native scheduler: configuring cron-based job scheduling
• Triggering Matillion jobs via REST API for event-driven pipeline execution
• Integration with external orchestrators: Apache Airflow and AWS Step Functions
• Matillion job monitoring: execution history, component-level logs, and error inspection
• Setting up email notifications and alerting for pipeline failures and completion

Module 9: Matillion on Snowflake — Advanced Integration

• Snowflake-specific Matillion components: Snowflake Query, Load, and Unload
• Leveraging Snowflake pushdown SQL for maximum transformation performance
• Matillion with Snowflake dynamic tables and streams for incremental processing
• Managing Snowflake virtual warehouse selection within Matillion pipeline design
• Matillion and Snowflake Medallion Architecture: building Bronze, Silver, and Gold pipelines

Module 10: Capstone Projects and Career Readiness

• End-to-end project: build a complete cloud data integration solution using Matillion from source ingestion to warehouse delivery
• Implementing an incremental load pipeline with change detection and SCD Type 2 processing
• Portfolio preparation and GitHub documentation of Matillion pipeline projects
• Interview preparation: Matillion concepts, pipeline design, troubleshooting, and certification exam scenarios
• Career pathways: Matillion ETL Developer, Cloud Data Engineer, Data Integration Architect

Experience in the Industry Gain knowledge from experts with practical Matillion — Cloud Data Integration & ETL Development project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture  and cognizant  employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.