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Post By Admin Last Updated At 2020-09-12
How to deploy the Pega Platform on AWS?

Pega is a leading cloud software platform that mainly focuses on customer engagement and operational excellence. It is under the Pega-systems Inc. that develops platforms for CRM, BPM, and digital process automation for enterprises. Pega Platform is developed to provide best-in-class, easy, and quickly build applications. Moreover, deploying the Pega Platform on Amazon AWS gives the benefit of the flexibility of the Pega Platform Cloud option.

In this blog, we are going to discuss the general instructions for building a classic Pega Platform environment on AWS.

Pega platform deployment ecosystem

The following are the most usual Pega Platform environments that are deployed to AWS:

  • Building a single-node or test domain is the simplest configuration. It generally includes a single Amazon Elastic Compute Cloud (Amazon EC2) instance with the Pega Platform enabled. Moreover, it also includes an Amazon Relational Database Service or Amazon RDS instance with it. Besides, this configuration is useful for testing of a proof-of-concept in general.
  • Generally, a shared test environment consists of several Amazon EC2 instances, an Amazon RDS instance, along with an AWS Elastic Load Balancer (AWS ELB) to support creation across different teams. Here, we can use auto-scaling for the high-availability of service.
  • Usually, an advanced multi-layer ecosystem is useful for production implements or pre-production testing. In this environment, individual layers of servers are added to direct particular needs like front-end and back-end processing, and database services.

Best Practices and Presumptions

The following are the directions for it:

  • User has access to the AWS Management Console or AMC.
  • The users will use Amazon RDS for PostgreSQL. If they choose to use another supported database, the general content within this post still applies to the user’s environment.
  • Furthermore, within the same AWS area, all Pega Platform resources are provisioned.
  •  All the AWS and Pega Platform resources are held within a single Amazon Virtual Private Cloud or VPC.
  •  A single Pega Platform node is included within each Amazon EC2 instance.

Point to Note: The Amazon RDS service does not allow installing any user-defined functions (UDFs). Therefore, before the user lodges this platform, he needs to set (bypass.udf.generation=true) under the ( file.

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Pre-requirements of device

Before continuing further in Pega Platform deployment, we have to ensure that we have access to the below things:

  •       Access to installing the Pega Platform file.
  •       A JDK back-up file.
  •       A proper backup or support for the app server.
  •       A JAR file access through JDBC driver.

Typical ecosystems

Each of the usual environments must include similar components with enhancing complex jobs.

Test environment to build Pega Platform

The reference environment for building tests offers a small Pega Platform ecosystem at a lower price. This set up is best suitable for proof-of-concept creation and testing the platform. The below image helps us to get knowledge of a simple development environment using a single AWS EC2 instance.

Amazon AWS resources for Pega Platform:

The following are the various resources available for deploying the Pega platform. ;-

  •  AWS area — an independent or single area is useful for all AWS resources.
  •  Amazon VPC — all available resources for this environment exist within an independent Amazon VPC.
  •  AWS EC2 instance— the test ecosystem requires only an independent AWS EC2 instance that executes all Pega Platform roles overall.
  •  Amazon RDS instance —All Pega Platform database assets exist within a single database.
  •  Availability zone — an independent availability zone is useful for the Amazon EC2 and the Amazon RDS instance.
  • Security groups — the said environment consists of two security groups to build several security zones for each layer. Moreover, there are two separate security groups for each zone;
  •  An individual security group/community for the AWS EC2 instance
  •  Individual security community for the Amazon RDS instance

Development environment

The following general steps will help us to build a testing environment with the use of the AWS Management Console (AMC):

Begin a single Amazon EC2 instance that meets the following basic needs:

  • Kind of instance — m4.large
  • Memory — There is an availability of a minimum of 10 GB of disk space. It also includes a minimum of 8 GB space within the temporary Dir. of the root file system.
  • Security group — develop a new security community or group. Open all ports need to allow traffic into the user app server. And to allow for remote access for the admin.
  • Start a single Amazon RDS instance that meets the below stated basic needs:
  • Area — here also users can use the same AWS area as used in the EC2 instance.
  • An Availability zone — Users can use the same availability zone as used within the AWS EC2 instance.
  • A DB Instance class — A size of db.m4.large
  • Security community— Build a new security batch. And open all ports necessary to enable traffic into the Amazon RDS instance from the app server. Moreover, it also allows the same for remote access for administration.

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Shared development environment

The shared ecosystem for this platform building supports several creative teams. The application layer with load-balancing makes it an exact development environment for a small, non-mission-critical application. Moreover, the shared creation environment is less costly than an advanced environment system.

A usually shared development environment consists of the below AWS resources available:

The area and AWS VPC are the same as used in the earlier ecosystem.

  • AWS ELB — the user can use either a Classic Load Balancer (CLB) or an Application Load Balancer (ALB) as load-balancers for Pega Platform apps.
  • Amazon EC2 instances — Several AWS EC2 instances will enhance the capability and availability of the Pega Platform ecosystem. Furthermore, these Amazon/AWS EC2 instances execute all the Platform roles.
  • Amazon RDS and the Availability zones are the same as usual in the early phase.
  • The Amazon EC2 instances are started across two different availability zones for high-availability and failover cases.
  • Moreover, the Amazon RDS instances are set up within a several-AZ deployment for highly-available and failover zones.
  • Auto-Scaling group — The Amazon EC2 instances start within an auto-scaling community for high-availability and failure cases.
  • Security service group — The below ecosystem has three security communities or groups to build several security groups for each layer:
  • A single security section for the Amazon/AWS EC2 instances.
  • A second security batch for the Amazon RDS instance.
  • And a third security batch for the AWS ELB category.

Building the shared development environment

The following usual steps help the user to develop a test environment through an AMC- AWS Management Console:

  • Start an environment that meets the criteria for the development or test environment.
  • Build an AWS ELB that is useful for a user for load balancing the EC2 instances setting up within an auto-scaling group.
  • Develop a snapshot of the current AWS EC2 instance and from that image, build an Amazon Machine Image (AMI).
  • Produce the auto-scaling section.
  • Users can utilize the latest AMI to build the auto-scaling section launch set-up. Select the current security batch built when the user started the test environment.
  • Design and set up the auto-scaling batch to make sure that at least a single AWS EC2 instance is started and running in each available zone.
  • Set up load balancing to use the AWS ELB that user-built within the step above.
  • Moreover, make the Amazon RDS instance up-to-date:
  • For high availability and failure cases, allow multi-AZ deployment alternatives.
  • Make up-to-date the DB Instance class based on the user capacity needs. For this, utilize any one of the below values:
  • The db.m4.xlarge
  • An db.m4.2xlarge
  • Also the db.m4.4xlarge

Advanced multi-layer environments

The advanced multi-layer ecosystem is generally useful for performance deployments, or pre-development testing. Like load or force testing. Besides, users can utilize AWS EC2 auto-scaling batches and an Amazon RDS multi-AZ implementation for high availability and failure.

This environment consists of independent tiers for particular Pega Platform roles. The roles can be any of the two: either user-facing or back-end. For example; Pega Platform roles have the following scope:

  • Universal — the universal role is the default for any Pega Platform node. It executes the tasks of all front and back-end Pega Platform junctions.
  • WebUser— this is a front-end role that executes only UI functions.
  • Search — the following back-end role executes only search-related jobs like indexing, query processing, and crawling.
  • SMA — it’s a back-end role useful for serving System Management Application (SMA).
  • BIX — this is a back-end support role to support Business Intelligence Exchange (BIX).
  • Background Processing — the back-end role performs only Agent Schedules and batch job functions.
  • Decision making — this is another back-end role to optimize DSM-related jobs. Moreover, the decision making role can be further specialized to manage particular components of DSM, for instance:
  • The Cassandra Data Node-CDN
  • A Virtual Business Director-VBD
  • An Adaptive Data Manager-ADM

Developing the advanced multi-tier environment

The following are the usual steps to develop an advanced multi-layer environment. This is built with the help of the AWS Management Console (AMC):

Develop an environment that meets the need for a shared development environment.

Create a new layer:

Create an AWS ELB layer that will be useful to load-balance the AWS EC2 instances setup under an auto-scaling batch.

Build an auto-scaling batch:

The user can utilize the custom-built AMI for the shared development environment. This is useful to build the auto-scaling group to start the configuration.


Users can modify the instance type based on their capacity needs. This is optional for the users engaged. The below mentioned are the valid values:

 A) m4.large

 B) m4.xlarge

C) m4.2xlarge

Build and configure the auto-scaling community at first. This is to make sure that a minimum of a single AWS EC2 instance is started running in each available zone.

The user has to set-up a load balancing structure to use the AWS ELB he built within step 2a.

Make an update to the Amazon RDS instance type.

To manage high availability and failure, allow the multi-AZ deployment choice.

Make Up-to-date the DB Instance class based on the user’s capacity needs. Use one of the following values for instance:

1.     db.m4.xlarge

2.     db.m4.2xlarge

3.     db.m4.4xlarge

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Pega Platform features

The following are the best features of this best Platform.

Application Design

The app design features include UI Code Extending, Programming Code Extending, Customized Branding, Application Templates, and the Workflows.

Data Control

The data control feature includes Data Shifting, Data Synchronize, and Data Security.


The publishing feature of the Pega framework includes application Deployment, Platform Compatibility, and Lifecycle Management.


The automation feature of the platform consists of Workflow Mapping.

Automation Building

The automation building consists of Workflow Recording, Pre-Built Templates, and custom Automation features.

Capabilities of Pega Platform

Build, automate, and engage is the key theme of this famous platform. It aims to build an app with low code, open architecture, readily available for consumers, and have continuous delivering capacity. The feature like low code app development helps in regular collaboration of the team, to work smartly, and scale with higher confidence. Moreover, by using low code makes it easier for developers to make the product market-ready within a short time. Similarly, the open architecture helps to use endless possibilities and make the data-free.

This platform helps to build automation apps that use end-to-end digital process transformation. It is mainly useful in the areas of CRM, BPM, robotic automation, and workforce intelligence, etc. Another capacity of this platform is to build a customer engagement platform for its clients. The application built using this framework helps to engage the customers in one place with many needs. It also helps to produce lots of products that would help customers and sellers meet naturally. The platform also helps in the data integration process.

Deploying the Pega platform on the AWS platform also makes it much easier with several possibilities as above.

Thus, it is clear with this statement that Pega offers best-in-class products with a lot of stuff.

Summing Up

Getting updated is always a need for everyone in every field. Similarly, in the IT world also many updates take place in a day. In the Pega framework also updates are continuing to grow day-by-day and it appears with the latest features.

By deploying the Pega Platform on AWS it takes it to the cloud world by making it easier for the end-users and developers too. It offers quickly build apps that are useful for end-users in the simplest way. Developers get more space to explore new things in this regard. To get more insights into this space, jump into Pega Online Training with experts.