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Hello everyone.
I'm Shika Kamp.
Welcome to the Site Reliability Engineering Conference of 2025.
Today we are gonna discuss about transforming monolithic SaaS,
the serverless evolution on AWS.
Welcome to our comprehensive exploration of serverless migration strategies
for SaaS applications using AWS.
This presentation will guide you through the journey from only the
architectures to flexible microservices that can dramatically reduce your.
Operational costs.
While accelerating development cycles, we'll examine practical implementation
steps, real world success, and critical concentrations for security
business intelligence integration.
By the end of this presentation, you will have a clear roadmap for
your own serverless transformation.
Now, let's say why migrate to serverless architectures?
Serverless architectures deliver compelling benefits that extend far beyond
the simple cost savings organization May.
Organizations migrating from monolithic systems report dramatically
reduced operational overhead.
So with typical cost reductions ranging from 30 to 60%, in some cases,
organizations leveraging event driven serverless patterns per specific
workloads have seen cost reductions exceeding 70% due to the precise resource
allocation and paper execution models.
So perhaps more importantly, these organizations experience a 40% boost in
the developer productivity by eliminating infrastructure management tasks.
This translates to fast innovation cycles and significantly reduce
time to market for new features.
So studies indicate that serverless adoption can accelerate feature deployment
by up to 50%, enabling business to respond rapidly to market changes.
Furthermore, the inherent scalability of serverless architecture leads to a
20 to 30% improvement in application resilience, including downtime
and enhancing the USR experience.
So beyond the cost and productivity, serverless architectures
contribute to improved scalability.
So which means like applications automatically scale based on demand
handling certain spikes in traffic with minimal manual intervention, or in
cases, no manual intervention at all.
This can lead to a five times more increase in peak traffic handling capacity
compared to traditional architectures.
So reduced time to market developers focus on code not infrastructure
leading to a faster development cycles.
Companies have reported to 25% decrease in time, in the time required to
launch these new products or features enhanced operational efficiency.
With managed services handling tasks like patching and scaling operation
teams can focus on strategic initiatives.
This can result in a 15 to 20% reduction in operational burden,
increased agility and flexibility.
Serverless enables rapid prototyping and experimentation facilitating
agile development practices.
Organizations find that they're able to test and deploy
these new ideas 30% faster.
Environment sustainability, paper use models, reduced waste wasted
resources contributing to a more sustainable computing practices.
So this one steady estimated a potential 10 to 20% decrease in the
energy consumption, but a certain workloads when migrated to serverless.
Global reach the server.
The serverless platform often provide a built in global distribution,
simplifying the deployment of applications to multiple regions.
Some services report latency reductions of 40% when moving to a global second.
A global edge based service.
S serverless location, simplified microservices implementation.
Serverless functions are ideal for building microservices,
allowing for a granular scaling and independent deployments.
Organizations see up to 35% decrease in complexity of managing
microservice deployments, even driven architecture adoption.
So serverless aligns naturally with event driven architectures
enabling real time data processing.
And asynchronous workflows.
This has been shown to improve real time processing speeds by
20 to 50% in certain use cases.
These benefits collectively contribute to more agile, cost effective and scalable
development environment, empowering organizations to innovate and compete
effectively in the digital landscape.
So when we un, let's try to understand a bit more about the monolith.
Challenge monolith.
SaaS applications create significant obstacles that
severely constrain the business agility and market responsiveness.
They're tightly coupled components Establish a critical
development bottleneck.
Where even minor modifications require comprehensive testing across the
entire application, dramatically.
Ex extending release cycles from days to weeks or months.
Specifically, a study found that companies utilizing monolithic architecture
experienced and average release cycle of 12 weeks for new features compared to
three weeks for those using microservices.
This translates to a 75.
Person reduction in deployment speed.
Furthermore regression testing for a single feature change in a
monolithic application can consume up to 40% of development time.
Diverting resources from innovation.
The fundamental inability to scale independent components
leads to substantial resource inefficiency, forcing organization
to over-provision infrastructure.
To accommodate peak demand scenarios.
For example, a typical e-commerce platform using a monolithic architecture
might allocate a server capacity for a black Friday level traffic
year around resulting in an average of th 60 to 70% of ideal resource
utilization during normal operations.
This over provisioning leads to 32.
50% increase in infrastructure cost compared to architectures that
will allow for granular scaling.
Moreover, the lack of independent scaling often results in degraded
performance during peak loads with reported latency increases up to five
seconds leading up to a 20% drop in.
User engagement.
So additional challenges include technology lock-in monolithic
architectures often rely on a single technology stack, making it
difficult to adopt new technologies or integrated more than cloud services.
This limits innovation and can lead up to 25% increase in maintenance overhead
as legacy systems become harder to support increased risk of failure.
A failure in one component can bring down the entire application
leading to a significant downtime.
So studies have shown that monolithic applications experience a 40% more
downtime incidents compared to microservices based architectures
difficulty in onboarding new developers.
The complexity of a large code banks makes a challenge for new developers
to understand and contribute, resulting in a 30% increase in onboarding time.
Limited ability to implement continuous delivery.
So the long release cycles associated with monolithic applications
hinder the adoption of continuous delivery, PR delivery practices,
slowing down the feedback loop and delaying the time to market.
Organizations report a 50% decrease in the ability to deliver small frequent updates.
Reduced innovation velocity.
The complexity of changing the code and the risk of associated
with changes slows down the rate at which the new features can be added.
One study indicated a decrease of roughly 20% in the number of
new features released per year.
Database contention.
Monolithic applications often rely on a single database, which can become
bottleneck as the application scales leading to the performance degradation.
So this can limit the applications ability to handle high transition,
high transaction volumes.
Database contention can, has been shown to cause 15 to 25%
reduction in transaction throughput.
These limitations underscore the need for more flexible and scalable
architecture, such as microservices or serverless to meet our demands
for the modern SaaS applications.
Now let's take a look at decomposing the monoliths into microservices.
So these transformation of microservices require a thoughtful
decomposition based on business domains rather than technical layers.
The study suggests that organizations that focus on business domain decompositions
here, 30 to 40% reduction in integration complexities, post migration,
identify the service boundaries.
I. Let's add map.
So we need to map the functional domains and business capabilities to
establish clear service boundaries using domain driven design principles
and event storming techniques, effective boundary identification
through event storming and context mapping reveals and natural service.
Demarcations that aligns with organizational structures
and business capabilities.
Specifically utilizing event storming workshops can reduce
service boundary definition by the time of by 20 to 25% and improve
alignment with business stakeholders.
Research indicates that well defined service boundaries result
in 15 to 20% improvement in the.
Team autonomy.
So define the service interfaces.
Create robust well controlled version APIs with well-documented
contracts that enforce loose couplings and enable independent
service evolution organizations.
Implementing API first strategies report a 35 to 40% increase in the developments.
P due to reduced dependency, adopting open API specifications can reduce the
API documentation efforts by up to 50%.
And improve developer onboarding.
Further mode version control of APIs has shown to decrease
breaking changes up to by 25%.
So refactor incrementally extract services systematically through the
strangler pattern, prioritizing high value low risk components while maintain
the maintaining the system stability.
Successful migrations leverage the STR strangler pattern methodology
systematically replacing the monolith functionality while preserving the system,
integrating implement a service mesh.
Deploy.
Sophisticated service discovery and communication layer that enables
resilient interservice communication, circuit breaking and observability.
A single mesh can improve latency back to 10 to 20% and reduce service value
rates by 90, 50, 30% through features like circuit breaking and retry policies.
Implementing comprehensive observability with the service me
leads to a 40 to 50 person reduction in the mean time to resolution.
It's called S-M-T-T-R for service related incidents.
Additional points to consider while decomposing the monoliths
into microservices could be organizational alignment.
Should the microservice architecture designs aligns with the organization
structure and culture that's fit as more important automation.
Automate deployment, testing and monitoring of update to
enable the continuous delivery and reduce the manual error.
So implementing the CICD pipelines can decrease the deployment times
up to by 60 to 70% security.
Implement robust security measures including authentication,
authorization, and encryption to protect the interservice communication.
Monitoring and observability.
Implement comprehensive monitoring and logging to gain insights
into the service performance by identifying potential issues.
Database management.
Address database management challenges by considering patterns such as
database per service or shared.
Database with careful schema design.
So by focusing on these principles and incorporating data driven designs
and organizations can effectively navigate the complexity complexities
of microservices, navi migration, and unlock the benefits of increased
agility, scalability, and resilience.
Now let's take a look at a couple of the AWS services.
The first one would be the AWS Lambda, the Serverless Foundation.
AWS Lambda serves as the computational cornerstone of the modern serverless
architectures executing code in response to events with zero server
provisioning or management overhead.
This paradigm shift fundamentally transforms the economics of cloud
computing by implementing a consumption based pricing model that changes precisely
for the compute resources consumed.
Studies indicate that the organizations are shifting to Lambda based
architecture experience, a 60 to 80% reduction in the operational
overhead related to server management.
Event, even executions functions instantly activate in response to
triggers from AWS services completely eliminating idle resource consumption
and delivering true paper use computing with millisecond precision.
In typical event driven scenarios, lambda functions demonstr a cold stock,
cold start latency of under a hundred milliseconds and 90% of invocations.
And subsequent worm invocations achieve latencies below 10 milliseconds.
This precise, this precision in resource allocation leads to a 20 to 30% reduction
in overall compute cost compared to traditional VM based event processing.
Automatic scaling lambda seamlessly in scales, concurrent executions to
match the real time workload demands without any configuration overhead,
effortlessly handling everything from a single request to thousands per second.
Lambda can scale to handle certain spikes in traffic, achieving a
99.99% availability rate, and can scale from zero to thousands of
concurrent executions in seconds.
Auto-scaling capabilities have been shown to reduce the peak load latency by 40 to
50% compared to manually scaled systems.
Polyglot support developers can gain freedom by leveraging multiple programming
languages across different functions.
Empowering teams to select the optimal technology stack for
each specific microservice.
The flexibility has been reported to decrease the development time
by 15 to 25% as teams can utilize preferred languages and libraries.
Furthermore, teams reported 10 to 15% increase in code maintainability by
utilizing the right tool for the job.
With Lambda millisecond billing granularity and zero friction scaling
capabilities, organizations can achieve unprecedented optimization.
Compared to traditional always on server instances, enterprises typically
realizes 70 to 90% reduction in compute costs for appropriate workloads
after migration, while simultaneously improving responsiveness and
eliminating capacity planning concerns.
Additional data points and benefits include reduced time to market,
improving default tolerance, simplified operational complexity, enhanced security.
Like cost optimization for intermittent workloads.
Integration with AWS ecosystem and global scalability.
By leveraging these benefits, organizations can achieve a significant
cost savings and improve operational efficiency and accelerate innovation.
Now let's take a look at the key serverless, a key AWS serverless services.
So AWS builds a robust ecosystem of serverless services that work
harmoniously with Lambda to create powerful cloud native solution.
So let's take a look at Aurora server as dynamically adjust the database capacity
in response to application demands without server management overhead.
So studies have shown that the Aurora serverless can reduce
database cost by 30 to 50% for applications with variable workloads.
It auto scales database capacity when within seconds maintaining a consistent
performance and reducing the need for manual capacity Planning for application
with unpredictable traffic patterns.
Aurora Serverless has been reported to decrease database administration time.
By up to 60% API gateway simplifies the creation of secure scalable APIs that
seamlessly trigger Lambda functions.
API Gateway can handle millions of API calls per second, ensuring
high availability and low latency.
Utilizing the API Gateway reduces development time for APAC creation
by 20 to 30%, and provides building in features for authentication,
authorization, and request violation.
API gateway Caching features can reduce can reduce a backend load up to 80% for
frequently accessed data, even breach.
So event bridge transform application architecture by providing serverless
event bus that intelligently routes events between decoupled services
and third parties as providers with minimal configuration.
Even Bridge can process millions of events per second with.
Near real time latency, typically under 500 milliseconds.
It simplifies integration with third party SaaS applications, reducing
integration time by 40 to 50% even bridge filtering and routing capabilities.
Reduce the complexity of managing event driven architectures, decreasing
development efforts by 25 to 30% S3.
S3 is called a simple storage service.
It provides durable object storage that integrates seamlessly with Lambda
and other server service S three's.
High durability with 99.9 nines and availability of 99.99%
make it ideal for storing and streaming large volumes of data.
S3 when notifications can trigger Lambda functions, enabling real
time data processing and analysis.
Organization utilizing the serverless architectures with S3 for data
storage have reported a 20 to 30% reduction in storage cost compared
to traditional storage solutions.
CloudWatch offers sophisticated observability capabilities providing
monitoring, logging analytics for serverless applications, CloudWatch
logs allows for real time log analysis.
Reducing troubleshooting time by 30 to 40%.
CloudWatch alarms can automatic electrical lambda functions or other AWS services
in response to performance metrics, enabling automate automated remediation.
CloudWatch dashboards provide a centralized review of application
performance, improving the visibility and reducing meantime to detection by 20,
meantime to de detection by 25 to 35%.
So additional benefits and data points to consider would be simply
simplified microservices architecture, the cost optimizations, increased
development velocity, enhance security, streamline, CICD pipelines.
So by leveraging these comprehensive ecosystems, organizations can build highly
scalable, cost effective and resilient serverless applications that drive the
innovation and business business growth.
Let's take a look at the real world success stories.
A FinTech transformation, a leading financial service provided slash
infrastructure cost by 45 percent, while accelerating the time to market
for a new features from months to mere days by migrating their transaction
processing system to lamb and animal db.
They achieved unprecedented operational efficiency and customer responsiveness.
So a health healthcare analytics, a healthcare analytics platform
revolutionized their operations by implementing serverless architecture
that scales instantaneously during peak reporting periods.
This eliminated persistent performance bottlenecks while delivering a remarkable
52% reduction in operational expenditure.
Allowing resources to be redirected toward page patient care initiatives.
Retail in recommendation engine, an e-commerce retailer reimagine
their recommendation engine using serverless microservices dramatically
reducing the response time from two weeks to just 200 milliseconds.
This 10 times performance improvement drove the substantial 30% increase in
conversion rates through hyper personal shopping experience that adapt in
real real time to customer behavior.
These transformative case studies illustrates the profound impact
of serverless architecture across diverse industries.
Beyond mere technical improvements organizations consistently
report enhanced business agility, substantial cost savings, and new
fund capacity to rapidly innovate and respond to market opportunities
that were previously unattainable with traditional infrastructure.
Let's take a look at DevOps cis.
Continuous improvement and continuous delivery.
First, serverless.
So server architectures requires sophisticated DevOps approach that
fundamentally embraces infrastructure as a code principles and fully
automate development pipeline.
So AWS code pipeline seamless integrated with Lambda to enable comprehensive
continuous delivery workflows that deploy.
Function.
So a PA configurations and database key changes are as unified atomic unit.
Studies shown that the organization adopting to mature server DevOps
practices CF 40 to 50% reduction in deployment related errors.
Infrastructure has code codify services resources with AWS cloud formation or
the serverless framework to guarantee reproducible version called as.
Infrastructure deployment across environment they are version controlled
and it reduces manual intervention errors up to 60 to 70% and accelerates
environment provisioning by 50 to 60%.
Version controlling IAC templates with GI ensures auditability and enables
rollbacks reducing recovery time by up to 30% Automated build pipeline to
establish a robust continuous integration with AWS code build to automatically
compiled package and validate lambda functions with the dependencies
code build reduces bill times by 70 to 80% compared to manual process.
Automated dependency management system ensures consistency and
reduces the reduces works on my machine issues by 40 to 50%.
Static code analysis and security scanning with pipeline have been shown
to reduce vulnerabilities by 25 to 30%.
Comprehensive testing.
Deploy a thermal test environments for thorough unit integration
and performance testing.
Of individual functions end to end workflows.
Thermal testing environments reduce the test environments reduce test
environments set up by 80 to 90% and improve test reliability.
Automated performances can detect re regressions early reducing the latency
issues in production by 20 to 30%.
Implementing contract testing for a PS ensures service compatibility,
reducing integration edit by 15 to 20%.
Stage deployments.
Leverage AWS code pipeline with canary deployment strategies to
methodo methodically introduce changes while continuously monitoring for
anomalies and performance impacts.
Canary deployments reduce the risk of widespread failures by 90 to 95% and
enable rapid rollbacks in case of issues.
Automated anomaly detection with CloudWatch alarms reduce meantime
to detection by 35 to 45%.
Stage deployments combined with feature flags, reduce the impact
of fail deployments by up to 50%.
Industry leading organizations implement sophisticated stage deployment strategies
with intelligent rollback capabilities, preserving the system integrity while
dramatically accelerating release cadence.
This transformative approach has enabled the forward thinking companies to achieve
deployment frequencies measured in hours.
Sometimes minutes compared to the weeks or months required with traditional
infrastructure models, specifically organizations using automated server
as CICD pipelines report a 70 to 80% reduction in deployment times.
Let's take a look at securing the serverless applications.
Securing serverless applications require a shift in approach from
traditional infrastructure protection to function level controls.
Each lambda function should operate with minimal permissions, allowing
the principle of leased privilege.
With carefully defined IAM rules, studies indicate that the organization
implementing a frying grained IAM roll reduce the risk of lateral
movement after the security breach.
B two 40 to 50%.
Function level IAM rules implement fine grain permission boundaries by
using the principle of leash privilege for each lambda function, restricting
access to only required resources.
Implementing function level IM roles reduces the attack surface by 20 to 30%
compared to broad service wide roles.
Utilizing the IM policies with resource level permissions reduces the impact
of compromise Credentials by up to 35%.
Secrets management restored the sensitive configuration in AWS Secrets
Manager with automatic rotation.
And secure retrieval by author authorized functions only.
Secrets Manager reduces the risk of hard coded credentials by
60 to 70% and automates secret rotation, reducing the window of
vulnerability, implement the least.
Privilege access to Secrets Manager ensures that only authorized function
can retrieve more sensitive data, input, validation, implement strict schema
validation of API at API level boundaries.
Using the API gateway request validators to prevent injection attacks.
API gateway validation reduces the risk of a SQL injection and cross site.
The scripting attacks by 30 to 40%.
Implement input validation at the API layer reduces the load on Lambda
functions, improving performance and security dependency scanning, integrate
automated vulnerability scanning into CICD pipelines to detect known vulnerabilities
in third party dependencies.
Dependency scanning reduces the risk of exploiting known
vulnerabilities by 25 to 35%.
Implementing software composition analysis, SEA tools in CICD
pipelines reduces the time to identify remediate vulnerabilities.
Data protection remains critical with encrypted requirements for
both data in transit and at rest.
AWS provides tools like KMS for, encrypting key management partners and
partner store for securing configuration, enabling a comprehensive security
posture for serverless applications.
So the additional data points and security practices to consider would be networks
segmentation, runtime, security and logging and monitoring security audits,
immutable deployments, ensuring that the Lambda function deployments are immutable.
Preventing unauthorized modifications, web application, firewall rules
encryption address, and in transit, regular security updates.
This is keep all the dependencies and runtime environments up to date
with the latest security patches.
Automated patches reduces these vulnerability windows as well.
By implementing the security practices, organizations can build secure and
resilient serverless applications that protect sensitive data.
And minimize the risk of security incidents.
Business intelligence integration.
Integrating business intelligence tools with serverless architectures
creates opportunities for real time decision making.
Analytics pipelines are built on event principles can process data as
it generate, as it gets generated.
Eliminating the batch processing delays and providing immediate
visibility into business operations.
Studies show that the organizations adopting serverless analytics
pipelines, reduced data, progressing processing latency by 60 to 80% compared
to traditional batch processing, real time analytics pipeline.
Serverless architectures enable event driven analysis pipelines
that process real time data delivering immediate insights rather
than delayed batch processing.
Even capture via even bridge even bridge allows to capture the e
realtime events from various AWS services and SaaS applications.
Streaming processing with kinesys enables realtime data ingestion and processing
kinesys data stream scan, ingest, and process terabytes of data per hour.
Providing real time data flow.
Kinesys data analytics allows for a real time SQL IES on streaming data, reducing
the need for complex transformation.
Transformation with Lambda Transform Lambda functions, perform realtime
transformations and enrichments.
Lambda functions can process events within milliseconds,
enabling realtime data processing.
Lambda functions can integrate with machine learning morals for
realtime data analysis, storage in data lakes or warehouses.
S3 data lakes and Redshift.
Data warehouses provide scalable and cost effective storage for process data.
S3 Data Lake Scan Store petabytes of data, enabling large scale data analysis.
Reshift Data Warehouse provide fast query performance for
complex analytical queries.
Visualization with QuickSight.
QuickSight provides interactive dashboards and visualizations
for real time data exploration.
QuickSight can generate interactive dashboards in seconds, enabling
real time data analysis.
Amazon QuickSight.
Paper session pricing model, quick site's, paper session pricing model reduces cost.
Intermittent users organizations reported 20 to 30% reduction in bi infrastructure
compared to traditional licensing model.
Direct integration with AWS data sources QuickSight seamlessly
integrates with AWS data sources, reducing the data preparation time.
ML powered insights.
QuickSight machine learning capabilities provide automated
insights and anomaly detection.
ML powered insights reduce the time to identify anomalies by 30 to 40%
embedded analytics capabilities.
QuickSight embedded analytics features enable seamless integration
of dashboards and applications.
Embedded analytics improve user engagement by providing data insights
within the application context.
Business outcomes.
The integration of server serverless analytics deliver a tangible benefits
beyond technical improvement, driving a better business decisions,
30% faster time to insight, reduced analytics infrastructure,
cost democratized data access.
Serverless analytics enables self data exploration, empowering business to
access data without IT intervention improved customer experience.
Real time.
Insights enable personalized com customer experience and
proactive service improvements.
Real time data driven responses.
Improve customer satisfaction score by an average of 50 to 20%.
By leveraging these ties, organizations can unlock the power of real time
data and significance business values.
So let's talk about our serverless migration roadmap.
So begin your serverless journey with a comprehensive assessment
of your current architecture.
Identifying the components suitable for initial migration Studies indicate that
the organizations that perform a thorough assessment before migrating ca 20 to 30%
reduction in migration related issues.
The ideal candidates are stateless services with variable workloads.
That benefit from Lambda automa automatic scaling and paper use
pricing model, specifically services with intermittent traffic patterns
or even driven workflows have shown to yield a 40 to 60% cost reduction
when migrated to serverless initial assessment and component selection.
Identify the stateless services such as a p. Points of data
transformation tasks are ideal for la.
Analyze work work workload patterns, identify services with variable or
un unpredictable workloads services with the peak to peak to throw traffic
ratios of fi five to one or higher benefit or higher benefit significantly
from serverless scalability.
Evaluate cost savings potential.
Calculate the co potential cost savings by comparing current infrastructure
costs with estimated lambda execution, cost, assess complexity
and dependence and consider the data processing pipelines pilot project.
And the next step would be the pilot project and foundational elements.
Start with the pilot project.
Begin with the pilot project to end to build team experience and
validate your approach Before broader adoption, build a team experience.
Pilot project helps teams to gain hands-on experience with the
serverless technologies and best practices, validate your approach.
Use the pilot project to validate your infrastructure as code templates and
CICD pipelines create reusable patterns.
Focus on creating reusable patterns and infrastructure as code templates
that aate future migrations.
Establish foundational elements, define standards and best practices
for serverless development, securing and operation standardized practices.
Reduce operational overhead by 20 to 30%.
Implement observability, implement cloud, CloudWatch, and other
observability tools to gain insights into application performance.
Organizational change and upskilling.
Invest in upskilling.
Remember that serverless transformation is both a technical
and an organizational change.
Invest in upskilling your teams to maximize the benefits
of this architectural shift.
Foster a DevOps culture promoted DevOps culture that emphasizes automation,
collaboration and continuous improvement.
Empower autonomous teams, organize teams around business capabilities,
fostering autonomy and ownership.
Encourage experimentation, create a culture of experimentation and
learning, allowing teams to explore new serverless technologies.
Establish a center of excellence, create a serverless center of excellence to provide
to provide guidance, best practices, and support for teams adopting serverless.
Address security concerns proactively Address security concerns by
implementing function level, IAM roles, and other security best practices.
By following these guidelines, organizations can navigate the
serverless journey to unlock full potential of this transformational
or architectural approach.
Yeah.
Thank you so much.
To conclude, we have seen how AWS serverless architecture empowers us to.
Move beyond the limitations of monolithic SaaS.
By embracing the microservices lambda and automated pipelines, we unlock
potential substantial cost reductions, accelerate the development, and
enhance security and scalability.
This transformation requires a strategic approach.
Accessing your architecture, piloting key services and investing in team upskilling.
The data speaks for itself.
Organizations adopting serverless experience, significant improvements,
have seen significant improvements in efficiency and speed.
Let's leverage the power of AWS to build agile, resilient, and cost effective SaaS
applications driving the innovations and achieving tangible business outcomes.
Thank you so much.
For listening through my presentation, I hope you have gained some insights
into the world of transforming monolithic into a cloud based
approach like AWS Amazon Web Services.
If you have any questions, you can always reach out to me
at reach ika comp@gmail.com.
I'll be more than happy to talk about this presentation or anything in general.
Thank you so much.
Thanks again.