Key considerations & best practices of AWS cloud native applications

White paper

AWS Development & Testing for Cloud-based Applications

Key Considerations & Best Practices
mobile app and web development challenges

Development & AWS Testing for Cloud Applications

This white paper guides project managers, developers, testers, systems architects, enterprises and anyone involved in software applications development activities.

It covers aspects of the AWS as a development platform, AWS cloud products and solutions, building cloud native applications, advantages of AWS, support, pipelines, automation, testing tools and techniques.

mobile app and web development challenges

Millions of customers leverage AWS cloud products and solutions to build sophisticated applications with increased flexibility, scalability and reliability.

AWS has established itself as a leading platform for enterprise development teams with a variety of command-line tools and software development kits (SDKs) to deploy and manage applications and services. AWS SDKs are available for any number of platforms and programming languages including Java, PHP, Python, Node.js, Ruby, C++, Android and iOS.

Advantages & exploitation
Cloud native applications

Cloud native is an approach to building and running applications that fully exploit the advantages of the cloud computing model.

Developed and deployed properly, cloud native applications provide substantial benefits including high-level services, greater elasticity, on-demand delivery, and streamlined global deployment.

The AWS development platform is aligned entirely with the cloud native approach and all its benefits.

Packaged as lightweight containers
Packaged as lightweight containers
Developed with best-of-breed languages and frameworks
Developed with best-of-breed languages and frameworks
Designed as loosely coupled micro-services
Designed as loosely coupled micro-services
Deployed on elastic cloud infrastructures
Deployed on elastic cloud infrastructures
Managed through an Agile DevOps process
Managed through an Agile DevOps process
Dynamically orchestrated
Dynamically orchestrated
“Cloud native technologies empower organizations to build and run scalable applications in modern, dynamic environments such as public, private, and hybrid clouds.”
CNCF Cloud Native Definition v1.0
Cloud Native Applications

Why Development Teams Use AWS to Build

AWS has become the preferred platform for cloud native applications.

Some of its many advantages include:

Programming Build and Integration Support

AWS provides scalable platform, integration, development and deployment support to web centric programming languages like Ruby, NodeJS, JavaScript, CSS, HTML, Python and more. It also provides platforms like Elastic Bean Stalk to build and run this code.

dynamic scalability in a cloud native stack
Fully Controlled Scale-Up and Scale-Down

Cloud applications require a mechanism to scale up and down as web traffic increases or decreases, thereby making it flexible and dynamic in nature. AWS-ECS, Autoscaling, and AWS-EKS provide full-featured functionality to address dynamic scalability in a cloud native stack.

Notification, User Authentication and Message Queuing

AWS SNS helps to coordinate the delivery of push notifications for apps, to either subscribing endpoints, or clients. AWS SQS is a message queuing system that helps developers de-couple as well as scale distributed systems, serverless apps, and microservices that are deployed on cloud native stacks. AWS Cognito facilitates the control of user authentication on the cloud native stack.

Cost Effective Storage and Secured Network Layer Support

Cloud Native Applications require storage and network capability that is mutable and flexible in nature. AWS S3 storage helps to provide online backup as well as archive data and application programs. AWS VPC, Subnets, and Elastic IP help to build flexible network segmentation over cloud space to keep it secure and safe from external non-authorized access.

Fault-Tolerant Microservices

Amazon ECS (Elastic Container Service) is a highly scalable, high-performance container orchestration service that supports Docker containers that enable run and scale containerized cloud applications on the cloud native stack. It is containers without servers making it secure and cost effective. In addition, Amazon EKS runs the Kubernetes management infrastructure across multiple availability zones to eliminate a single point of failure.

Code Management

AWS also offers CodeDeploy, CodeCommit and CodePipeline to manage code in the cloud native stack. CodeDeploy helps to deploy code at scale, with a focus on rapid development and rapid deployment in mission-critical situations where the cost of failure is high. Code Commit is a managed revision control service that hosts Git repositories and works with all Git-based tools and CodePipeline is used to model and automate software release process on the cloud native stack.


12-Factor App Support


Main objective of this factor is to have all cloud application code in revision control – If events are shared (such as a common Amazon API Gateway API), then the Lambda function code can be used for those events to put in the same repository. Otherwise, AWS Lambda break functions can be used along with event sources into their own repositories.

2 Dependencies

To build special processing or business logic the best solution may be to create a purposeful library using following languages – Node.js, Python: pip, Java: Maven, C#: NuGet and Go: Go get Packages.


AWS Lambda and AWS API Gateway can be used to set configuration information using the environment in which each service runs.

4 Backing Services

AWS Lambda has a default model for this factor. Typically, it is possible to reference any database or data store as an external resource via HTTP endpoint or DNS name.

5 Build, Release, Run

AWS CodeDeploy, CodeCommit and CodePipeline can be utilized for this factor.

6 Port Binding

AWS Lambda can be used with either of these three invocation modes – Synchronous, Asynchronous and Stream-based.

7 Concurrency

AWS Lambda can be used which has massive concurrency and scaling capacity.

8 Stateless Processes

AWS Lambda functions can be used and treated as being stateless, despite the ability to potentially store some information locally between execution environment re-use.

9 Disposability

AWS best practices help to identify where to place certain logic, how to re-use execution environments, and how by configuring function for more memory to get a proportional increase in CPU available. With AWS X-Ray, it is possible to gather some insight as to what a function is doing during an execution and to make adjustments accordingly.

10 Environment Parity

AWS serverless application model allows users to model serverless applications in greatly simplified AWS CloudFormation syntax. With this model, it can use CloudFormation’s capabilities – such as Parameters and Mappings to build dynamic templates.

11 logs

AWS Cloud watch logs as well as API Gateway provides two different method of logs – 1) Execution logs 2) Access logs.

12 Admin Processes

Typically, cloud functions are scoped down to single or limited use cases and have individual functions for different components of the web application. Even if they share a common invoking resource, such as an API Gateway endpoint and stage, it is possible to separate the individual API resources and actions to their own Lambda functions, so it is possible to build design that meets the requirement.

systematic handling of test environments

Defining & Creating Pipeline for Cloud Native

One of the challenges to testing in the cloud lies in finding ways to create a test environment that contains all of the necessary app configurations and test data. This process can be time-consuming for testers and developers alike. The model below presents a systematic way of handling the different test environments in the cloud to overcome these challenges.

Functionally, there should be no change in the test approach between web-based and cloud environment. However, more focus may be required in certain aspects such as performance, security, environment availability, integration with interfaces, customer experience and adherence to SLAs when testing in a cloud-based environment.

The test strategy and test plan document may also change depending on the service models and implementation models selected. Choice of tools and techniques should be similar to that of web-based applications, however, during tool selection it is important to make sure that they are available on cloud and that they support the pay-as-you-use model.

Local Environment
Local Environment


Locally deploy

Manual verification

Static code analysis

Dev Environment
Dev Environment

CI Job:

Build and deploy

Smoke tests

Static code analysis

Integration Environment
Integration Environment

CI Job:

Build and deploy

Integration tests

Security and performance tests

STAGING Environment
STAGING Environment

CI Job:

Build and deploy

Regression tests

Manual verification

Regression, exploratory

Pre-POD Environment
Pre-POD Environment

E2E tests

Load tests

Security tests

Production Environment
Production Environment

Release tests

Smoke tests

Metrics verification

Testing & verification

Robust Automation

Cloud native applications lend themselves to robust test automation.

cloud native application testing and verification automation
automated aws testing

Almost all rounds of testing can be automated

detect interface defects for applications on cloud

There should be more focus on verifying the communication paths and interactions between components to detect interface defects for applications on cloud

aws software testing services

Even the smallest piece of testable software is in scope

verify interactions

It is also critical to verify interactions at the boundary of an external service to assure that it meets the contract expected by a consuming relevant cloud service


Testing Tools & Techniques for Cloud Native

The right tools and thoughtfully designed acceptance criteria are key success factors for cloud native testing. The table below is a good guide based on our experience:
Testing Type
Acceptance Criteria designed for CN Applications
Testing Type

Unit/Component Testing


Testing Framework: JUnit, NUnit, RSpec

Automation Tools: Pact/QMetry Automation Framework

Acceptance Criteria designed for CN Applications

1. Boundary condition

2. Status code and Status message verification

3. Positive and Negative business flows

Testing Type

Integration Testing


Testing Framework: JUnit, NUnit, RSpec

Automation Tools: Pact/QMetry Automation Framework

Acceptance Criteria designed for CN Applications

1. Response chaining

2. Status code and Status message verification at the end level

3. Session management

4. Positive and Negative scenarios with respect to third party

5. Consumer scenarios to verify nothing is breaking after service deployment

Testing Type

Contract Testing


Automation Tools: Pact/QMetry Automation Framework

Acceptance Criteria designed for CN Applications

1. Each consumer needs to verify each contract

  • Key and value pair
  • Value type

2. Positive and Negative contact verification

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AWS Cloud-Based Applications

AWS Testing Approaches

Picking the right testing strategy to address specific challenges.

Functional Performance Testing (FPT)

Functional Performance Testing (FPT) means conducting functional testing of the business use cases under the application load conditions. It includes a suite of tests all under server load conditions; e.g.

  • End-user business scenarios
  • Validation of the data breach (data swap)
  • Multi-user/Concurrent testing
  • Validation of the session continuity
  • Validation of auto scaling
Proper FPT Testing
Proper FPT Testing

The table below compares the differences and impact of a proper FPT approach in most enterprises.

Before (Current Activities)
  • 1Very limited functional testing under load (eg. Data Swap)
  • 2Very limited multi user testing (in Appt Center Booking Flow)
  • 3Lack of awareness among team members on edge conditions/corner conditions that resulted
    1. a) Potential high risk for bad member experience
    2. b) Compromise on member PHI data
  • 4Lack of enforcement as a practice across the squad members
After – Successful FPT Rollout
  • 1 Formalized and documented approach for Functional Performance Testing (FPT)
  • 2 FPT Suite contains collection of tests including data breach validations
  • 3 Functional verification of business use cases while application under load
  • 4 Enforcement of FPT via DoD Checklist (Squad level and Release QA checklist)
Outcome – Business BenefitsIncreased test coverage that results in reduced risk level data swap conditions
FPT Testing Types
FPT Testing Types
Type of testing
Type of testing

1Data Swap Validation (multi-user, negative concurrency)


Helps in validating the data integrity for the end-user data


Validate the data integrity of the end-users with single user or multi-user.

Multi User Testing → Up to 5 TPS for Service Layer via Live Services

Concurrent Users (High Traffic) → Up to 50 TPS for Service Layer via Virtualized Services

Type of testing

2Auto-Scaling Policy Validation


Helps in identifying the potential issues related to data loss, session management, session continuity, performance degradation during the auto scaling of the infrastructure


Verify the application behavior is working as expected when infrastructure resources are updated via auto scaling (scale in or scale out) policy

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Security Testing

Security is absolutely key when working in the cloud and there are a number of important factors when deciding how to approach it.

  • The more the security, the more load on the performance, so decisions about what to secure need to be made at the architecture level
  • It is not possible to attain 100% security
  • Never presume anything in regard to security
  • Never trust the dependencies of the application
  • Set traps in code and in repositories (Canary Tokens)
  • Always identify the communication method used between:
    • Users and Resources
    • Between different Resources
  • Always audit the logs and API calls (Cloud Trail and Cloud Watch)
Encryption in AWS
Encryption in AWS

The table below compares the differences and impact of a proper FPT approach in most enterprises.

Encryption in Flight
Encryption in Flight

Data should be encrypted before sending and after receiving, but only server and sender should know how to do this.

  • SSL Certificates help with Encryption (HTTPS)
  • It ensures that no MITM (Man-in-the-middle) attack can happen
Encryption at Rest
Encryption at Rest

Server-side encryption

  • Data should be encrypted after received so, even in case the server is hijacked, the hacker could be able to read the data
  • Data should be decrypted before being sent back to the client
  • Encryption/decryption keys should be managed somewhere (KMS) and the server should have rights to access them
Client-Side Encryption
Client-Side Encryption

Data is encrypted by the client and never decrypted by the server.

  • Data should be always decrypted by the client only in this case (Envelope Encryption)
Types of Security
Types of Security

There are two types of Security to focus on – User-Based and Resource-Based.

User-Based Security
User-Based Security

Is managed by IAM, which helps determine what kinds of API calls should be allowed from a specific user according to their authorization level.

  • IAM acts as the center of the AWS environment and is integrated with all the AWS services, so it plays the role of central security provider for the whole AWS environment.
  • There is typically only one IAM role per application.
  • Best practices include never writing IAM credentials in code, setting up Multi-Factor Authentication (MFA) for the root user, and using least privilege principals.
Resource-Based Security
Resource-Based Security

Depends on every resource, according to its communication method and data usage method.

Load & Performance Testing

Load and performance testing is intended to determine the responsiveness, throughput, reliability, and/or scalability of a system under a given workload. It typically involves:

  • Assessing production readiness
  • Evaluating against performance criteria
  • Comparing performance characteristics of multiple systems or system configurations
  • Finding the source of performance problems
  • Supporting system tuning
  • Finding throughput levels
Why Load & Performance Test?

There are two types of Security to focus on – User-Based and Resource-Based.

cloud applications respons test

Speed Does the application respond quickly enough for the number of users?

aws cloud applications stability test

Stability Is the application stable under expected and unexpected user load?

aws application scalability

Scalability Will the application handle load beyond expected user demand?

aws applications load test

Confidence Do we believe the application will handle the required amount of load?

Testing Considerations
Five distinct types of Load & Performance Testing
1. AWS Load Testing

Tests the application for its performance on normal and peak usage by checking the response to user requests consistently within accepted tolerance.

Key Considerations

  • What is the max load the application is able to hold before the application starts behaving unexpectedly?
  • How much data the database is able to handle before system slowness or the crash is observed?
  • Any network related issues to be addressed?
2. AWS Stress Testing

Involves testing beyond normal operational capacity, often to a breaking point, in order to observe the results. This kind of test is started with good load test for which the application has already been tested. The load is increased slowly up to the point where the system fails to respond.

Key Considerations

  • What is the max load a system can sustain before it breaks down?
  • How the system breaks down?
  • Is the system able to recover once it’s crashed?
3. AWS Spike Testing

Involves increasing the system load rapidly using bursts of concurrent users to check behavior of the system. The goal is to determine whether performance will suffer, the system will fail, or if it is able to handle dramatic changes in load.

Key Considerations

  • Is the application able to handle sudden burst of user’s load?
  • Working of system failback mechanism due to unexpected load?
4. AWS Volume Testing

Involves huge volumes of data exchanged in database and testing its relation to performance of application. This can be performed in short and long duration.

Key Considerations

  • Are the database queries tuned to be performant?
  • What is the effect of heavy database on application performance?
5. AWS Endurance Testing

Also known as Soak Testing, is typically done to determine if the system can sustain the continuous expected load. During tests, memory utilization is monitored to detect potential leaks. For example, in software testing, a system may behave exactly as expected when tested for 1 hour but when the same system is tested for 3 hours, problems such as memory leaks cause the system to fail or behave randomly.

AWS Load and Performance Testing: Parameters

Response Time – the total amount of time it takes to respond to a request for service. That service can be anything from a memory fetch, to a disk IO, to a complex database query, or loading a full web page.


Calculated as requests/unit of time. The time is calculated from the start of the first sample to the end of the last sample. This includes any intervals between samples, as it is supposed to represent the load on the server.

The formula is:

Throughput = (number of requests) / (total time).


The measure of responsiveness that represents the time taken to complete execution of a request. It can also represent sum of several latencies or subtasks.

90% Line (Percentile)

The 90th percentile is the value for which 90% of the data points are smaller. This is a standard statistical measure.


A set of tests run to capture performance metric data for the purpose of evaluating the effectiveness of subsequent tuning activities performed to the application.


A process of comparing system performance against the baseline that is created internally or against an industry standard recognized by another organization.

Transaction Response Time

Represents the time taken for the application to complete a defined transaction or business process.

Hits per Seconds

Hits are requests of any kind made from the virtual client to the application being tested (Client to Server). It is measured by number of Hits and the higher the Hits Per Second, the more requests the application is handling per second.


The amount of data transferred across the network is called throughput. It considers the amount of data transferred from the server to client only and is measured in bytes/sec.

Performance Testing
1. Requirements Gathering

Collecting key project information including performance testing goals (expected users, response time, etc.), required credentials, application URL, tool preference, etc.

2. Test Planning

Creating complete test plans that incorporate objectives, scope, approach, and focus of the software testing effort. Test planning includes planning load test with the reference of Capacity Planning conducted at the time of Application Development. Capacity Planning helps determine what type of hardware and software configuration is required to meet application needs, and how many resources an application uses. The main goal is to identify the right amount of resources required to meet service demands now and in the future.

3. Script Creation

In this step, actual script is created per the business flows using Performance Testing tools such as JMeter, Load Runner, etc. A sample execution with 1-5 users is typically done to verify created script.

4. Load Test Execution

Users execute created script with provided parameters (such as # of user’s, ramp-up, ramp-down time, etc.). It requires monitoring servers for memory utilization, CPU utilization, Disk I/O, etc.

5. Result Analysis and Reporting

In this phase, users take the test results and perform analysis and create a report which identifies performance bottlenecks such as memory leak, server errors, response time, etc.

6. Tuning and Fixing

The above reports enable users to fix these bottlenecks and continue to tune the application until specified goals are met.

aws application testing methodology
mobile app and web development challenges
Load & Performance Testing

Tools Used

Open source
Open source

Open STA




Paid tools
Paid tools

LoadRunner (most popular)

Neo Load

Skill Performer


Cloud-based Applications
Tools to Complement & Accelerate
AWS Architecture – In General
AWS Cloud based application architecture
Migration Sequence
Migration Sequence

Database Server


Tables, users, functions, procedures, triggers etc.


Datapump, Import, Export, SQL developer

Migration Sequence

Data Transfer


All tables data


AWS DMS, PL/SQL script, Talend etc.

Migration Sequence

Application Server


Forms fmb, fmx, configuration and setup files


SCP, Oracle 11g forms converter and engine

Migration Sequence





Oracle fusion middleware

Migration Sequence



Application users, roles, DB roles


AWS DMS or third party tool

Migration Sequence



Groups, users, roles, privileges


AWS console

Migration Sequence



Application and database


Amazon S3 buckets and RMAN

Disaster Recovery Planning in AWS

natural disasters recovery planning in aws
Natural Types of Disasters
  • Agricultural diseases and pests
  • Damaging winds
  • Drought and water shortage
  • Earthquakes
  • Emergency diseases (pandemic influenza)
  • Extreme heat
  • Floods
  • Hurricanes and tropical storms
  • Landslides and debris flow
  • Thunderstorms
man-made and technological disasters recovery planning in aws
Man-Made & Technological Types of Disasters
  • Power service disruption and blackout
  • Nuclear power plant and nuclear blast
  • Radiological emergencies
  • Chemical threat and biological weapons
  • Cyber attacks
  • AWS console
  • Civil unrest
Disaster recovery measures can be classified mainly into three types:
Preventive measures

Aimed at preventing an event from occurring

Corrective measures

Aimed at fixing a system in case of a negative event or disaster

Detective measures

Aimed at detecting and discovering negative events

Key factors

Recovery Time Objective (RTO) & Recovery Point Objective (RPO)

The right DR plan for an organization involves many factors, but few are as important as RTO and RPO.

The terms are similar, but fundamentally different. RTO is the maximum length of time after an outage that your company is willing to wait for the recovery process to finish. RPO is the maximum amount of data loss your company is willing to accept as measured in time.

Disasters recovery plan
Disaster Recovery Approaches
There are four primary approaches for DR on AWS:
Backup and recovery
  • In most traditional environments, data is backed up to tape and sent off-site regularly
  • It can take a long time to restore your system in the event of a disruption or disaster
  • Amazon S3 is an ideal destination for backup data that might be needed quickly to perform a restore
  • Transferring data to and from Amazon S3 is typically done through the network, and accessible from any location
  • You can use AWS Import/Export to transfer very large data sets by shipping storage devices directly to AWS
  • For longer-term data storage where retrieval times of several hours are adequate, there is Amazon Glacier, which has the same durability model as Amazon S3
  • Amazon Glacier and Amazon S3 can be used in conjunction to produce a tiered backup solution
Preparation Phase

The figure below shows data backup options to Amazon S3, from either on-site infrastructure or from AWS.

data backup options to Amazon S3
Recovery Phase

The following diagram shows how you can quickly restore a system from Amazon S3 backups to Amazon EC2

restore a system from Amazon S3 backups to Amazon EC2
Key steps for backup and recovery:
  • Selecting an appropriate tool or method to back up data into AWS
  • Ensuring an appropriate retention policy for the data
  • Ensuring appropriate security measures are in place for the data, including encryption and access policies
  • Regularly testing the recovery of data and system restoration
Pilot Light

The term describes a DR scenario in which a minimal version of an environment is always running in the cloud:

  • The on-premise database server mirrors data-to-data volumes on AWS
  • The database server on cloud is always activated for frequent or continuous incremental backup
  • The application and caching server replica environments are created on cloud and kept in standby mode — very few changes take place over time
  • AMIs can be updated periodically
  • If the on-premise system fails, the application and caching servers get activated
  • Users are re-routed using elastic IP addresses to the ad hoc environment on cloud
  • Recovery should take just a few minutes
Preparation Phase

The figure below shows the preparation phase.

Key steps include:

  • Setting up Amazon EC2 instances to replicate or mirror data
  • Ensuring access to all supporting custom software packages available in AWS
  • Creating and maintaining AMIs of key servers where fast recovery is required
  • Regularly running and testing servers, and applying software updates and configuration changes
  • Automating the provisioning of AWS resources where possible
preparation phase
Recovery Phase

Key steps for the Pilot Light Recovery Phase include:

  • Starting the application Amazon EC2 instances from custom AMIs
  • Resizing existing database/data store instances to process the increased traffic
  • Adding additional database/data store instances to give the DR site resilience in the data tier
  • Changing DNS to point at the Amazon EC2 servers
  • Installing and configuring any non-AMI based systems, ideally in an automated way
recovery phase
Warm stand-by

This approach is a scaled-down version of a fully functional environment and is always running in the cloud. It extends the Pilot Light elements and preparation and further shortens the recovery time. By identifying business-critical systems, users can fully duplicate them on AWS and have them always on – running on a minimum-sized fleet of Amazon EC2 instances on the smallest sizes possible. In a disaster, the system can scale up quickly to handle the production load.

Preparation Phase

The following figure shows the preparation phase for a warm standby solution, in which an on-site solution and an AWS solution run side-by-side.

Key steps in the Preparation Phase include:

  • Setting up Amazon EC2 instances to replicate or mirror data
  • Creating and maintaining AMIs
  • Running an application using a minimal footprint of Amazon EC2 instances or AWS infrastructure
  • Patching and updating software and configuration files in line with the live environment
preparation phase for a warm standby solution
Recovery Phase

In the case of failure of the production system, the standby environment can scale up quickly for production load, and DNS records are changed to route all traffic to AWS.

Key steps include:

  • Increasing the size of Amazon EC2 fleets in service with the load balancer
  • Starting applications on larger Amazon EC2 instance types as needed
  • Manually changing the DNS records, or using Amazon Route 53 automated health checks so that all traffic is routed to the AWS environment
  • Using auto scaling to right-size the fleet or accommodate the increased load
  • Adding resilience or scale up to the database
scale up quickly for production load

Multi-Site is the optimal backup and DR approach. All activities in the preparatory stage are similar to a warm standby; except that AWS backup on Cloud is also used to handle some portions of the user traffic using Route 53. When a disaster strikes, the rest of the traffic that was pointing to the on premise servers is re-routed to AWS. Using auto scaling techniques, multiple EC2 instances are deployed to handle full production capacity.

Users can increase the availability of their Multi-Site solution by designing Multi-AZ architectures. The cost of this scenario is determined by how much production traffic is handled by AWS during normal operation. In the recovery phase, you pay only for what you use for the duration that the DR environment is required at full scale.

Preparation Phase

In a Multi-Site Approach, preparation includes:

  • Setting up the AWS environment to duplicate the production environment
  • Setting up traffic routing technology to distribute incoming requests to both sites
  • Configuring automated failover to re-route traffic away from the affected site
Multi-Site is the optimal backup and DR approach
Recovery Phase

The following figure shows the change in traffic routing in the event of an on-site disaster.

Key steps for recovery in a Multi-Site DR approach include:

  • Either manually, or by using DNS failover, changing DNS weighting so that all requests are sent to the AWS site
  • Leveraging application logic for failover to use local AWS database servers for all queries
  • Using auto scaling to automatically right-size the AWS fleet if possible
multi-site approach preparation phase

AWS offer four levels of DR support across a spectrum of complexity and time

AWS DR support across a spectrum
Execution & Approach
Failing Back

Once you have restored your primary site to a working state, you will need to restore your normal service, which is often referred to as a “failback.”

There are different ways to execute it based on which DR approach has been deployed.

For Backup & Restore
For Pilot Light, Warm Standby, and Multi-Site
For Backup & Restore

Freeze data changes to the DR site

For Pilot Light, Warm Standby, and Multi-Site

Establish reverse mirroring/replication from the DR site back to the primary site, once the primary site has caught up with the changes.

For Backup & Restore

Take a backup

For Pilot Light, Warm Standby, and Multi-Site

Freeze data changes to the DR site.

For Backup & Restore

Restore the backup to the primary site

For Pilot Light, Warm Standby, and Multi-Site

Re-point users to the primary site.

For Backup & Restore

Re-point users to the primary site

For Pilot Light, Warm Standby, and Multi-Site

Unfreeze the changes.

For Backup & Restore

Unfreeze the changes

Data Replication

When replicating data to a remote location users should consider several factors:

Distance between the sites

Distance between the sites – longer distances are typically subject to more latency or jitter

Available bandwidth

Available bandwidth – the breadth and variability of the interconnections

Data rate required by the application

Data rate required by the application – should be lower than the available bandwidth

Replication technology

Replication technology – should be parallel (so that it can use the network effectively)

Execution & Approach

DR/Business Continuity Planning

Business Continuity Planning also is a key part of DR. The graphic below outlines how effective Business Continuity approach works.


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Cloud Factory Model gaining upfront visibility on resource, skill, effort and, timeline and risks for the migration.


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aws consulting partner Immersion-Day Program

Our project plan for AWS cloud hosting and related software services are classified into three areas: Gap Analysis; Migrate & Adopt; and Sustain & Scale. The planning process helps customers build collaboration between product-as-a-service, data, and cloud services to take full advantage of the scalability, cost and agility advantages of the AWS platform.

Apexon helps customers classify their applications on a spectrum of complexity and criticality based on direct impact to business-critical functions. We also develop migration plans that cater to each customer’s specific business needs. In addition, Apexon’s cloud factory model and readily available accelerators help customers gain a holistic picture of “what it really takes” in terms of resources, skills, effort, timelines and risks as they progress on their AWS cloud journey.

Apexon & AWS
AWS Services

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