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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 tasks in Data Engineering Management
- New features in Data Engineering Integration 10.4
Module 2: Ingestion and Extraction in Hadoop
- Integrating DEI with Hadoop clusters
- Understanding Hadoop file systems
- Data ingestion into HDFS and Hive using Sqoop
- Initial load for Mass Ingestion to HDFS and Hive
- Incremental load for Mass Ingestion to HDFS and Hive
- Lab: Configure Sqoop to process data between Oracle and HDFS
- Lab: Configure Sqoop for processing data between an Oracle database and Hive
- Lab: Create mapping specifications using Mass Ingestion Service
Module 3: Native and Hadoop Engine Strategy
- Engine strategy for Data Engineering Integration
- 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 cases
- Stateful computing and windowing
- Lab: Create a reusable Python transformation
- Lab: Create an active Python transformation
- Lab: Perform Hive upserts
- Lab: Use the LEAD windowing function
- Lab: Use the LAG windowing function
- 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 objects to Avro files
- Lab: Use complex data types—Arrays, Structs, and Maps—in mappings
Module 6: Hierarchical Data Processing
- Hierarchical data processing
- Flattening 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 environments
- Mapping optimization
- Mapping recommendations and insights
- Scheduling, queuing, and node labeling
- Mapping audits
- Lab: Implement recommendations
- Lab: Implement insights
- 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
- Overview of Intelligent Structure Discovery
- Intelligent Structure Model
- Lab: Use an Intelligent Structure Model in a mapping
Module 10: Databricks Overview
- Overview of Databricks
- Steps to configure Databricks
- Databricks clusters
- Notebooks, jobs, and data
- Delta Lakes
Module 11: Databricks Integration
- Databricks integration
- Components of the Informatica and Databricks environments
- Runtime process on the Databricks Spark engine
- Databricks integration task flow
- Prerequisites for Databricks integration
- Cluster workflows
- Demo: Set up Databricks connection
- Demo: Run a mapping with the 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.
