Data Engineering Integration for Developers

Developer Tool for Big Data Developers

This course is for software 10.5. Learn to speed up Data Engineering Integration with large ingestion, incremental loading, transformations, complicated file processing, dynamic mappings, and Python. Examine how to reuse application logic for Data Engineering use cases while monitoring, troubleshooting, and best practices.

Objectives

After successfully completing this course, students should be able to:

• Mass ingest data to Hive and HDFS
• Perform incremental loads in Mass Ingestion
• Perform initial and incremental loads
• Integrate with relational databases using SQOOP
• Perform transformations across various engines
• Execute a mapping using JDBC in Spark mode
• Perform stateful computing and windowing
• Process complex files
• Parse hierarchical data on Spark engine
• Run profiles and choose sampling options on Spark engine
• Execute Dynamic Mappings
• Create Audits on Mappings
• Monitor logs using REST Operations Hub
• Monitor logs using Log Aggregation and troubleshoot
• Run mappings in Databricks environment
• Create mappings to access Delta Lake tables
• Tune performances of Spark and Databricks jobs

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