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Course Details

About Greenplum

The Greenplum Database builds on the foundations of open source database PostgreSQL.It primarily functions as a data warehouse and utilizes a shared-nothing, massively parallel processing (MPP) architecture. In this architecture, data is partitioned across multiple segment servers, and each segment owns and manages a distinct portion of the overall data; there is no disk-level sharing nor data contention among segments.

Greenplum Online Training Curriculum

Greenplum Intro & Architecture

Greenplum Concepts, Features and Benefits, Greenplum Master, Greenplum Segments, Greenplum Interconnect

Database Installation, Redundancy and Failover concepts

Segment Mirroring, Master Mirroring, Data Distribution in greenplum, Checking for Uneven Data Distribution, Database Installation and Configuration, Redundancy and Failover in Greenplum Database

Managing Roles and Privileges

Security Best Practices for Roles and Privileges, Access Control and Security, Creating New Roles (Users),  Altering Role Attributes,  Creating Groups (Role Membership),  Managing Object Privileges,

Configuring Client Authentication

Allowing Connections to Greenplum Database, Editing the pg_hba.conf File,  Limiting Concurrent Connections

Accessing the Database

Establishing a Database Session, Supported Client Applications, Greenplum Database Client Applications, PgAdmin III for Greenplum Database,  Database Application Interfaces,  Third-Party Client Tools,  Troubleshooting Connection Problems

Managing Workload and Resources

Overview of Greenplum Workload Management,  How Resource Queues Work in Greenplum Database,  Steps to Enable Workload Management,  Configuring Workload Management,  Resource Queues

Database Administration

Defining Database Objects (databases, schema, table, sequence etc),  Creating and using databaseso Creating and using schemas,  Creating and Using Sequences,  Indexes in Greenplum Database,  Creating and Managing Views

Managing Data

Vacuuming the Database, Querying Data,  Query Performance,  Explain Output, Analyze Output

Partitioning Large Tables

Understanding Table Partitioning in Greenplum Database, Deciding on a Table Partitioning Strategy, Creating Partitioned Tables,  Loading Partitioned Tables,  Maintaining Partitioned Tables

Greenplum Database Loading Tools Overview

About External Tables,  About gpload,  About COPY, Using the Greenplum Parallel File Server (gpfdist)

System Administration

Starting and Stopping Greenplum, Routine admin tasks,  Uploading Configuration File Changes Only

Configuring Your Greenplum System

About Greenplum Master and Local Parameters, Setting Configuration Parameters,  Setting a Local Configuration Parameter,  Setting a Master Configuration Parameter, Enabling Alerts and Notifications, Checking for Failed Segments, Recovering a Failed Segment, Recovering a Failed Master, Backing up and Restoring Databases, Backup and Restore Operations, Expanding a Greenplum System

Monitoring a Greenplum System

Monitoring Database Activity and Performance, Monitoring System State,  Enabling System Alerts and Notifications,  Checking System Stateo Checking Disk Space Usage

Routine System Maintenance Tasks

Routine Vacuum and Analyze, System Catalog Maintenance, Managing Greenplum Database Log Files

Performance Tuning

Common Causes of Performance Issues, Identifying Hardware and Segment Failures, Managing Workload,  Maintaining Database Statistics, Identifying Statistics Problems in Query Plans, Tuning Statistics Collection, Optimizing Data Distribution, Optimizing Your Database Design


Functions, Basic shell scripts

Investigating a Performance Problem

Checking System State, Checking Database Activity, Checking for Active Sessions (Workload), Checking for Locks (Contention),  Checking Query Status and System Utilization,  Troubleshooting Problem Queries, gp_toolkit administration schema, System catalog, Performance issues and tips, Automation scripts for GPDB administration

Greenplum Database Architecture

Greenplum Database utilizes a shared-nothing, massively parallel processing (MPP) architecture that has been designed for business intelligence and analytical processing. Most of today’s general-purpose relational database management systems are designed for Online Transaction Processing (OLTP) applications. Since these systems are marketed as supporting data warehousing and business intelligence (BI) applications, their customers have inevitably inherited this less-than-optimal architecture. The reality is that BI and analytical workloads are fundamentally different from OLTP transaction workloads and therefore require a profoundly different architecture.

OLTP transaction workloads require quick access and updates to a small set of records. This work is typically performed in a localized area on disk, with one or a small number of parallel units. Shared-everything architectures, in which processors share a single large disk and memory, are well suited to OLTP workloads. Shared-disk architectures, such as Oracle RAC, can also be effective for OLTP because each server can take a subset of the queries and process them independently while ensuring consistency through the shared-disk subsystem.

However, shared-everything and shared-disk architectures are quickly overwhelmed by the full-table scans, multiple complex table joins, sorting, and aggregation operations against vast volumes of data that represent the lion’s share of BI and analytical workloads. These architectures aren’t designed for the levels of parallel processing required to execute complex BI and analytical queries, and tend to bottleneck as a result of failures of the query planner to leverage parallelism, lack of aggregate I/O bandwidth, and inefficient movement of data between nodes.


The Greenplum Database shared-nothing architecture separates the physical storage of data into small units on individual segment servers each with a dedicated, independent, high-bandwidth channel connection to local disks. The segment servers are able to process every query in a fully parallel manner, use all disk connections simultaneously, and efficiently flow data between segments as query plans dictate. Because shared-nothing databases automatically distribute data and make query workloads parallel across all available hardware, they dramatically out perform general-purpose database systems on BI and analytical workloads.

Salary Trends

Average Greenplum Salary in USA is increasing and is much better than other IBM products.

Greenplum Training

Ref: Indeed.com

Course Reviews


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    • The online course session was good with a lot of discussion on the subject in depth. Demo's were good and the trainer cleared my doubts clearly.The trainer having a very good command on the subject.
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    • Real time/live scenario's were included in the training sessions. The trainer's has very good command on the subject. Thank you..
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      Claire Edwards
    • I like the training period from Tekslate. The course is very well designed that helps to keep track until we demonstrate subject mastery.
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    • Using most innovative teaching techniques, Tekslate intended to help students to learn through online. A great part of the coursework is allowed to use and earn certification by the time they finish t ...
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