Apache Hop Data Integration Course
Master Apache Hop from the ground up and build production-ready data integration pipelines with confidence. This course covers everything from core architecture and pipeline design to advanced deployment, monitoring, and governance. Whether you are modernising legacy ETL systems or building new data workflows, you will gain the practical skills that employers need.
What you will learn:
You will learn how Apache Hop's engine, metadata layer, and GUI work together to power modern data integration. You will build pipelines that read, transform, and load data from databases, files, REST APIs, and streaming platforms. You will design orchestration workflows with branching logic, error handling, and automated notifications. You will deploy Hop via CLI, Docker, Kubernetes, and external schedulers like Apache Airflow. You will implement logging, auditing, data quality checks, and lineage tracking to meet enterprise governance standards. You will also explore advanced topics including metadata injection, Apache Beam execution, CI/CD integration, and custom plugin development.
How you study in practice Apache Hop Data Integration Course
How you practise Apache Hop Data Integration Course
For companies looking to train their teams
With Elevify for businesses, the course includes exercises and examples tailored to your company and its specific needs.
Course content
8 Chapters • 35 LessonsDuration between 4 and 360 hours (you decide)
Chapter 1HideHide detailsSee detailsIntroduction to Apache Hop
Introduction to Apache Hop
Lesson 1 • Navigating the Hop GUI
Introduces the Hop GUI layout, menus, and perspective system. Learners gain confidence navigating the interface before building any artifacts.
Lesson 2 • What Is Apache Hop
Covers Hop's origin, design philosophy, and position in the data integration landscape. Establishes context for all subsequent technical learning.
Lesson 3 • Apache Hop Architecture Overview
Explains the runtime engine, metadata layer, and GUI components. Learners understand how these layers interact before touching any tooling.
Lesson 4 • Installing and Configuring Apache Hop
Guides installation on local machines and verifies a working environment. Correct setup is prerequisite for all hands-on exercises.
Chapter 2HideHide detailsSee detailsProjects, Environments, and Metadata
Projects, Environments, and Metadata
Lesson 1 • Managing Environments
Explains how environments separate configuration from code for dev, test, and prod. Learners configure environment-specific variables without modifying pipeline logic.
Lesson 2 • Understanding Hop Projects
Defines a Hop project and its role in organizing pipelines and workflows. Connects project structure to team collaboration and version control.
Lesson 3 • Variables and Configuration Best Practices
Teaches variable scoping, resolution order, and parameterization strategies. Learners write portable pipelines that adapt to any environment without code changes.
Lesson 4 • Metadata Objects and the Metadata Store
Covers Hop's metadata store as the central registry for connections, run configurations, and more. Learners create and reuse metadata objects across pipelines.
Chapter 3HideHide detailsSee detailsBuilding Pipelines in Apache Hop
Building Pipelines in Apache Hop
Lesson 1 • Debugging and Testing Pipelines
Introduces breakpoints, preview mode, and row-level inspection for troubleshooting. Learners diagnose and fix common pipeline errors efficiently.
Lesson 2 • Joining and Aggregating Data
Explains merge join, lookup, and group-by transforms for combining and summarizing data. Learners produce aggregated datasets from multiple sources.
Lesson 3 • Pipeline Fundamentals
Introduces pipeline concepts, the canvas, and the hop (connection) between transforms. Learners understand data flow direction and row-based processing.
Lesson 4 • Data Transformation Transforms
Teaches filtering, mapping, type conversion, and string manipulation transforms. Learners apply multi-step transformations to clean and reshape raw data.
Lesson 5 • Input and Output Transforms
Covers the most common source and sink transforms for files, databases, and streams. Learners build end-to-end pipelines that read from and write to real data stores.
Chapter 4HideHide detailsSee detailsConnecting to Data Sources and Targets
Connecting to Data Sources and Targets
Lesson 1 • Messaging and Streaming Connectors
Introduces Kafka and JMS connectors for event-driven data ingestion. Learners build pipelines that consume and produce messages from streaming platforms.
Lesson 2 • REST and HTTP Integrations
Explains the REST client transform and HTTP post action for API-based data exchange. Learners consume and publish data via HTTP endpoints inside pipelines.
Lesson 3 • Relational Database Connectivity
Covers JDBC driver setup, connection pooling, and SQL execution transforms. Learners connect Hop to any JDBC-compliant database reliably.
Lesson 4 • File-Based Data Sources
Teaches reading from and writing to local, network, and cloud file systems using VFS. Learners handle diverse file formats and remote storage locations.
Chapter 5HideHide detailsSee detailsWorkflows in Apache Hop
Workflows in Apache Hop
Lesson 1 • Core Workflow Actions
Covers pipeline execution, shell script, file management, and notification actions. Learners assemble workflows that automate multi-step data processes.
Lesson 2 • Error Handling and Logging in Workflows
Explains workflow-level logging, error capture, and notification strategies. Learners configure workflows that surface failures clearly and recover gracefully.
Lesson 3 • Conditional Logic and Looping
Teaches success/failure branching, evaluation actions, and iterative execution patterns. Learners implement decision-driven workflows that respond to runtime conditions.
Lesson 4 • Workflow Concepts and Structure
Defines workflows, actions, and hops with success/failure routing. Learners distinguish workflows from pipelines and know when to use each.
Chapter 6HideHide detailsSee detailsAdvanced Pipeline Design Patterns
Advanced Pipeline Design Patterns
Lesson 1 • Metadata Injection for Dynamic Pipelines
Teaches the ETL Metadata Injection transform to drive pipeline behaviour from external metadata. Learners build template pipelines that adapt without manual reconfiguration.
Lesson 2 • Performance Tuning Pipelines
Explains row buffer sizing, parallel copies, and transform bottleneck analysis. Learners optimise pipeline throughput for large-scale data volumes.
Lesson 3 • Reusable Pipeline Fragments
Covers pipeline unit reuse via the pipeline executor and sub-pipeline patterns. Learners eliminate duplication and enforce consistency across large projects.
Lesson 4 • Error Handling Strategies in Pipelines
Covers error hop routing, rejected row capture, and dead-letter patterns. Learners build pipelines that isolate bad data without stopping the main flow.
Lesson 5 • Apache Beam Execution Engine
Introduces Beam as a distributed execution backend for Hop pipelines. Learners configure Beam runners and understand trade-offs vs. the local engine.
Chapter 7HideHide detailsSee detailsRunning and Deploying Apache Hop
Running and Deploying Apache Hop
Lesson 1 • Scheduling and Orchestration Integration
Covers triggering Hop from external schedulers and orchestration platforms. Learners integrate Hop into existing data platform scheduling ecosystems.
Lesson 2 • Hop Server Setup and Management
Covers Hop Server installation, configuration, and remote execution via the web interface. Learners register and monitor remote pipeline runs centrally.
Lesson 3 • Containerising Hop with Docker
Explains the official Hop Docker image, volume mounts, and environment injection. Learners run Hop pipelines in containers for portable, reproducible deployments.
Lesson 4 • Hop Command-Line Interface
Teaches hop-run and hop-conf CLI tools for scripted execution and configuration. Learners automate pipeline runs without the GUI.
Chapter 8HideHide detailsSee detailsMonitoring, Logging, and Governance
Monitoring, Logging, and Governance
Lesson 1 • Hop Logging Architecture
Explains log channels, log levels, and log sinks available in Hop. Learners configure logging to capture the right detail without overwhelming storage.
Lesson 2 • Data Lineage and Impact Analysis
Introduces Hop's lineage capabilities and integration with external lineage tools. Learners trace data flow from source to target across complex pipeline graphs.
Lesson 3 • Execution History and Audit Tables
Covers pipeline and workflow execution logging to relational audit tables. Learners query execution history to support operational reporting and SLA tracking.
Lesson 4 • Version Control and Change Management
Explains Git integration for Hop projects and branching strategies for pipeline code. Learners manage pipeline versions and collaborate safely in team environments.
Lesson 5 • Data Quality Monitoring in Pipelines
Teaches embedding data quality checks using validation transforms and rule engines. Learners detect and report data quality issues at runtime without external tools.
Your valid completion certificate
This course is for you:
Data analysts: ready to move beyond spreadsheets into automated pipeline development.
ETL developers: seeking a modern open-source alternative to proprietary integration tools.
Data engineers: wanting structured expertise in orchestration, deployment, and governance.
BI developers: looking to take ownership of the data preparation layer upstream.
Software developers: transitioning into data engineering roles with transferable coding instincts.
IT professionals: tasked with modernising legacy data infrastructure on limited budgets.
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