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Course Outline
Module 1: Informatica Data Engineering Management Overview
- Data Engineering concepts
- Data Engineering Management features
- Benefits of Data Engineering Management
- Data Engineering Management architecture
- Data Engineering Management developer tasks
- Data Engineering Integration 10.4 new features
Module 2: Ingestion and Extraction in Hadoop
- Integrating DEI with Hadoop cluster
- 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 for Processing Data Between Oracle (SQOOP) to HDFS
- Lab: Configure SQOOP for processing data between an Oracle database and Hive
- Lab: Creating 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: Executing a mapping in Spark mode
- Lab: Connecting 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: Creating a Reusable Python Transformation
- Lab: Creating an Active Python Transformation
- Lab: Performing Hive Upserts
- Lab: Using Windowing Function LEAD
- Lab: Using Windowing Function LAG
- Lab: Creating 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: Converting Flat File data object to an Avro file
- Lab: Using 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: Flattening a complex port in a Mapping
- Lab: Building dynamic mappings using dynamic ports
- Lab: Building dynamic mappings using input rules
- Lab: Performing Dynamic Flattening of complex ports
- Lab: Parsing 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: Implementing Recommendation
- Lab: Implementing Insight
- Lab: Implementing 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: Monitoring Mappings using REST Operations Hub
- Lab: Viewing and analyzing 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.
