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Course Outline
Module 1: Informatica Data Engineering Management Overview
- Core concepts of Data Engineering
- Features of Data Engineering Management
- Benefits of Data Engineering Management
- Architecture of Data Engineering Management
- Developer responsibilities in Data Engineering Management
- New features in Data Engineering Integration 10.4
Module 2: Ingestion and Extraction in Hadoop
- Integrating DEI with a Hadoop cluster
- Understanding Hadoop file systems
- Data Ingestion to HDFS and Hive using SQOOP
- Mass Ingestion to HDFS and Hive – Initial load
- Mass Ingestion to HDFS and Hive – Incremental load
- Lab: Configure SQOOP to process data between Oracle (SQOOP) and HDFS
- Lab: Configure SQOOP to process data between an Oracle database and Hive
- Lab: Create Mapping Specifications using Mass Ingestion Service
Module 3: Native and Hadoop Engine Strategy
- Data Engineering Integration engine strategy
- Hive Engine architecture
- MapReduce
- Tez
- Spark architecture
- Blaze architecture
- Lab: Execute a mapping in Spark mode
- Lab: Connect to a Deployed Application
Module 4: Data Engineering Development Process
- Advanced Transformations in Data Engineering Integration Python and Update Strategy
- Hive ACID Use Case
- Stateful Computing and Windowing
- Lab: Create a Reusable Python Transformation
- Lab: Create an Active Python Transformation
- Lab: Perform Hive Upserts
- Lab: Use Windowing Function LEAD
- Lab: Use Windowing Function LAG
- Lab: Create a Macro Transformation
Module 5: Complex File Processing
- Data Engineering file formats – Avro, Parquet, JSON
- Complex file data types – Structs, Arrays, Maps
- Complex Configuration, Operators and Functions
- Lab: Convert Flat File data object to an Avro file
- Lab: Utilize complex data types – Arrays, Structs, and Maps in a mapping
Module 6: Hierarchical Data Processing
- Hierarchical Data Processing
- Flatten Hierarchical Data
- Dynamic Flattening with Schema Changes
- Hierarchical Data Processing with Schema Changes
- Complex Configuration, Operators and Functions
- Dynamic Ports
- Dynamic Input Rules
- Lab: Flatten a complex port in a Mapping
- Lab: Build dynamic mappings using dynamic ports
- Lab: Build dynamic mappings using input rules
- Lab: Perform Dynamic Flattening of complex ports
- Lab: Parse Hierarchical Data on the Spark Engine
Module 7: Mapping Optimization and Performance Tuning
- Validation Environments
- Execution Environment
- Mapping Optimization
- Mapping Recommendations and Insight
- Scheduling, Queuing, and Node Labeling
- Mapping Audits
- Lab: Implement Recommendation
- Lab: Implement Insight
- Lab: Implement Mapping Audits
Module 8: Monitoring Logs and Troubleshooting in Hadoop
- Hadoop Environment Logs
- Spark Engine Monitoring
- Blaze Engine Monitoring
- REST Operations Hub
- Log Aggregator
- Troubleshooting
- Lab: Monitor Mappings using REST Operations Hub
- Lab: View and analyze logs using Log Aggregator
Module 9: Intelligent Structure Model
- Intelligent Structure Discovery Overview
- Intelligent Structure Model
- Lab: Use an Intelligent Structure Model in a Mapping
Module 10: Databricks Overview
- Databricks overview
- Steps to configure Databricks
- Databricks clusters
- Notebooks, Jobs, and Data
- Delta Lakes
Module 11: Databricks Integration
- Databricks Integration
- Components of the Informatica and the Databricks environments
- Run-time process on the Databricks Spark Engine
- Databricks Integration Task Flow
- Pre-requisites for Databricks integration
- Cluster Workflows
- Demo: Set up Databricks connection
- Demo: Run a mapping with Databricks Spark engine
Requirements
Developer Tool for Big Data Developers
21 Hours
Testimonials (2)
Very useful in because it helps me understand what we can do with the data in our context. It will also help me
Nicolas NEMORIN - Adecco Groupe France
Course - KNIME Analytics Platform for BI
It's a hands-on session.
