Greenplum Course Overview

Greenplum Course Overview

The Greenplum course is designed to empower learners with the skills and knowledge necessary to master the Greenplum Database, an advanced, fully featured, open-source data warehouse. It provides a comprehensive understanding of how to leverage Greenplum for large-scale analytics processing.

Module 1: INTRODUCTION sets the stage by providing an overview of Greenplum's architecture and introduces the psql CLI utility, essential for interacting with the database.

Module 2: DEFINING AND SECURING THE DATABASE dives into Database objects with DDL, DML, and DQL, ensuring that learners understand how to define and secure a Greenplum database effectively.

Module 3: Data loading AND DISTRIBUTION explains how to efficiently implement Table storage models, manage Data loading, and utilize Table partitioning for optimized data distribution.

Module 4: DATA MODELING & DESIGN focuses on the best practices in data modeling and the Physical design decisions that impact performance.

Module 5: PERFORMANCE ANALYSIS & TUNING teaches learners how to use the Pivotal Query Optimizer, understand different SQL joins, profile queries, and apply Query tuning and Indexing strategies for optimal performance.

Module 6: ONLINE ANALYTICAL PROCESSING, Window functions covers advanced analytics with Window functions, built-in functions, and the creation of User-defined functions and types.

By completing this course and aiming for Greenplum certification, learners will be well-prepared to build, manage, and optimize Greenplum databases for high-performance data analytics.

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  • Live Training (Duration : 40 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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Koenig's Unique Offerings

Course Prerequisites

To ensure that participants can effectively engage with the Greenplum training course and derive the maximum benefit from its content, the following prerequisites are recommended:


  • Basic understanding of database concepts and familiarity with database management systems.
  • Knowledge of SQL (Structured Query Language), including DDL (Data Definition Language), DML (Data Manipulation Language), and DQL (Data Query Language).
  • Familiarity with command-line interfaces (CLI) and basic Unix/Linux commands, as Greenplum often requires interaction with a Unix/Linux environment.
  • Some experience with data modeling and design concepts, which will aid in understanding physical design decisions within Greenplum.
  • An introductory level of knowledge about data warehousing and analytical databases is advantageous but not mandatory.
  • Interest in learning about distributed database systems and their performance tuning.

These prerequisites are intended to provide a foundation that will help learners to more readily absorb the course material. They are not meant to be barriers but rather stepping stones to a successful training experience with Koenig Solutions.


Target Audience for Greenplum

The Greenplum course by Koenig Solutions offers a comprehensive dive into Greenplum Database, targeting IT professionals focused on data management and analytics.


  • Data Engineers
  • Database Administrators (DBAs)
  • Data Analysts
  • Business Intelligence (BI) Professionals
  • ETL Developers
  • Data Scientists with a focus on data warehousing
  • SQL Developers
  • IT professionals working with big data and analytics platforms
  • System Architects designing data solutions
  • Database Professionals looking to learn about MPP (Massively Parallel Processing) databases
  • Technical Project Managers overseeing data projects
  • Data Warehouse Specialists


Learning Objectives - What you will Learn in this Greenplum?

Introduction to the Course's Learning Outcomes and Concepts Covered

In the Greenplum course, participants will gain a comprehensive understanding of Greenplum's MPP architecture, database design, data loading, query optimization, and performance tuning to effectively manage and analyze large-scale datasets.

Learning Objectives and Outcomes

  • Understand the architecture and components of Greenplum, an MPP database system.
  • Utilize the psql command-line interface for interacting with the Greenplum database.
  • Learn Data Definition Language (DDL), Data Manipulation Language (DML), and Data Query Language (DQL) for database management.
  • Implement table storage models, compression, and manage tablespaces for efficient data organization.
  • Master the techniques for efficient data loading and distribution across the Greenplum cluster.
  • Design and implement table partitioning strategies to optimize query performance.
  • Develop robust data models tailored for Greenplum's parallel processing capabilities.
  • Make informed physical design decisions that influence database performance.
  • Analyze and tune database performance using the Pivotal Query Optimizer and other profiling tools.
  • Apply advanced SQL techniques, including window functions and user-defined functions and types, for complex analytical queries.

Target Audience for Greenplum

The Greenplum course by Koenig Solutions offers a comprehensive dive into Greenplum Database, targeting IT professionals focused on data management and analytics.


  • Data Engineers
  • Database Administrators (DBAs)
  • Data Analysts
  • Business Intelligence (BI) Professionals
  • ETL Developers
  • Data Scientists with a focus on data warehousing
  • SQL Developers
  • IT professionals working with big data and analytics platforms
  • System Architects designing data solutions
  • Database Professionals looking to learn about MPP (Massively Parallel Processing) databases
  • Technical Project Managers overseeing data projects
  • Data Warehouse Specialists


Learning Objectives - What you will Learn in this Greenplum?

Introduction to the Course's Learning Outcomes and Concepts Covered

In the Greenplum course, participants will gain a comprehensive understanding of Greenplum's MPP architecture, database design, data loading, query optimization, and performance tuning to effectively manage and analyze large-scale datasets.

Learning Objectives and Outcomes

  • Understand the architecture and components of Greenplum, an MPP database system.
  • Utilize the psql command-line interface for interacting with the Greenplum database.
  • Learn Data Definition Language (DDL), Data Manipulation Language (DML), and Data Query Language (DQL) for database management.
  • Implement table storage models, compression, and manage tablespaces for efficient data organization.
  • Master the techniques for efficient data loading and distribution across the Greenplum cluster.
  • Design and implement table partitioning strategies to optimize query performance.
  • Develop robust data models tailored for Greenplum's parallel processing capabilities.
  • Make informed physical design decisions that influence database performance.
  • Analyze and tune database performance using the Pivotal Query Optimizer and other profiling tools.
  • Apply advanced SQL techniques, including window functions and user-defined functions and types, for complex analytical queries.
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