Rohit Bhardwaj
Director of Architecture, Expert in cloud-native solutions
Rohit Bhardwaj is a Director of Architecture working at Salesforce. Rohit has extensive experience architecting multi-tenant cloud-native solutions in Resilient Microservices Service-Oriented architectures using AWS Stack. In addition, Rohit has a proven ability in designing solutions and executing and delivering transformational programs that reduce costs and increase efficiencies.
As a trusted advisor, leader, and collaborator, Rohit applies problem resolution, analytical, and operational skills to all initiatives and develops strategic requirements and solution analysis through all stages of the project life cycle and product readiness to execution.
Rohit excels in designing scalable cloud microservice architectures using Spring Boot and Netflix OSS technologies using AWS and Google clouds. As a Security Ninja, Rohit looks for ways to resolve application security vulnerabilities using ethical hacking and threat modeling. Rohit is excited about architecting cloud technologies using Dockers, REDIS, NGINX, RightScale, RabbitMQ, Apigee, Azul Zing, Actuate BIRT reporting, Chef, Splunk, Rest-Assured, SoapUI, Dynatrace, and EnterpriseDB. In addition, Rohit has developed lambda architecture solutions using Apache Spark, Cassandra, and Camel for real-time analytics and integration projects.
Rohit has done MBA from Babson College in Corporate Entrepreneurship, Masters in Computer Science from Boston University and Harvard University. Rohit is a regular speaker at No Fluff Just Stuff, UberConf, RichWeb, GIDS, and other international conferences.
Rohit loves to connect on http://www.productivecloudinnovation.com.
http://linkedin.com/in/rohit-bhardwaj-cloud or using Twitter at rbhardwaj1.
Presentations
Mastering Enterprise Architecture to create Roadmap- DEEP DIVE
Monday, 9:00 AM EST
Enterprise Architecture helps in describing what is the current state and helps build a future roadmap. Come prepared to solve many Enterprise Architecture challenges.
As part of the journey, we will explore TOGAF to build our architecture. First, we will create a Baseline Architecture. Next, we will explore the path for the Target Architecture. Finally, after identifying gaps between the two, we will apply a step-by-step process to prepare a roadmap.
“Organizations no longer want their enterprise architecture (EA) practice to be focused on standards, structure and control,” says Marcus Blosch, research vice president at Gartner.
“They want an EA practice that is focused on driving business outcomes, working in a flexible and creative way to help define the future and how to get there.”
We will explore the following domains:
– Data
– Technology
– Application
– Business
This talk will help you build a long-term IT Strategy which matches your Business Strategy.
Resilient Cloud Architecture Design Patterns
Tuesday, 8:30 AM EST
Resilient architecture is fundamental when working in distributed, cloud-based systems. Designing and architecting large-scale applications managing millions of requests brings unique challenges with availability, performance, and integration. You will need to make difficult choices and evaluate tradeoffs. Luckily, you can use different architecture patterns to make a distributed application more resilient. Based on evolutionary architecture, this approach enables you to create systems designed to evolve with the ever-changing software development ecosystem. Resilient architecture patterns will allow you to create systems that continue functioning even when components fail.
Join expert Rohit Bhardwaj to learn how to implement an evolutionary architecture approach and understand resilient architecture patterns. This training will explore architecture decisions you may need to make when evaluating your architecture to improve performance and resiliency. For example, you will no longer struggle to handle millions of requests per second or face issues when routing traffic.
What you'll learn — and how you can apply it
By the end of this live, hands-on, online course, you'll understand the following:
How to create responsive, maintainable, extensible architecture from resilient, elastic design utilizing message-driven services
How to design cost-effective Recovery Point Objectives (RPOs) and Recovery Time Objectives (RTOs)
How to identify blocking issues with microservices in the cloud
How to evaluate caching strategies that can help lower costs and protect from DOS attacks
And you'll be able to:
Design high availability, high scalability, low latency, and resilient architectures.
Analyze and review implementations.
Identify key scalability challenges in your company.
Prevent cascading failures and preserve functionality.
This training is for you because…
You have an existing need to evaluate your current architecture.
You want to understand best practices.
You need to design new systems and want to evaluate which pattern to use.
Prerequisites
Basic knowledge of software architecture
Familiarity with design principles
Thinking application as stateless for all the API calls makes the system available most of the time and requires creating a cache for common distributed data. Next, we examine how to deal with cascading failures and timeout scenarios. As part of auto-healing, applications need to Detect, Prevent, Recover, Mitigate, and Complement so that the service is resilient.
The key takeaways for the audience are as follows:
*Resiliency is essential for any feature in the cloud.
*Understanding the value chain is critical to identifying failure points.
*Challenges come in determining if there is a failure and designing the system for auto-
healing
*The focus should be first to prevent a failure from occurring.
*Identifying critical challenges in your company and tools and techniques to auto-heal and provide a sustainable solution
Course Schedule
Evolutionary Architecture:
– Scaling to 100 million customers
– Understanding Requirements - Empathy Map
– Fail Points
– Defining KPIs
Resilient Patterns:
– BulkHead pattern
– Routing Strategies
– Design Issues with Microservices
– API Gateway Pattern
– Database per Service Pattern
– Database Sharding Patterns
– Fan out Pattern
– Publish-Subscribe Pattern
– Command Query Responsibility Segregation (CQRS)
– Message filter pattern
– Topic-queue-chaining Pattern
– Message Partitioning Patterns
– Priority Queue Pattern
Caching:
– Caching and Failure Injection
– Distributed system challenges
– Caching Patterns
– Order in Chaos
– Resilient Steps
– Resources
Resilient Cloud Architecture Design Patterns
Tuesday, 10:30 AM EST
Resilient architecture is fundamental when working in distributed, cloud-based systems. Designing and architecting large-scale applications managing millions of requests brings unique challenges with availability, performance, and integration. You will need to make difficult choices and evaluate tradeoffs. Luckily, you can use different architecture patterns to make a distributed application more resilient. Based on evolutionary architecture, this approach enables you to create systems designed to evolve with the ever-changing software development ecosystem. Resilient architecture patterns will allow you to create systems that continue functioning even when components fail.
Join expert Rohit Bhardwaj to learn how to implement an evolutionary architecture approach and understand resilient architecture patterns. This training will explore architecture decisions you may need to make when evaluating your architecture to improve performance and resiliency. For example, you will no longer struggle to handle millions of requests per second or face issues when routing traffic.
What you'll learn — and how you can apply it
By the end of this live, hands-on, online course, you'll understand the following:
How to create responsive, maintainable, extensible architecture from resilient, elastic design utilizing message-driven services
How to design cost-effective Recovery Point Objectives (RPOs) and Recovery Time Objectives (RTOs)
How to identify blocking issues with microservices in the cloud
How to evaluate caching strategies that can help lower costs and protect from DOS attacks
And you'll be able to:
Design high availability, high scalability, low latency, and resilient architectures.
Analyze and review implementations.
Identify key scalability challenges in your company.
Prevent cascading failures and preserve functionality.
This training is for you because…
You have an existing need to evaluate your current architecture.
You want to understand best practices.
You need to design new systems and want to evaluate which pattern to use.
Prerequisites
Basic knowledge of software architecture
Familiarity with design principles
Thinking application as stateless for all the API calls makes the system available most of the time and requires creating a cache for common distributed data. Next, we examine how to deal with cascading failures and timeout scenarios. As part of auto-healing, applications need to Detect, Prevent, Recover, Mitigate, and Complement so that the service is resilient.
The key takeaways for the audience are as follows:
*Resiliency is essential for any feature in the cloud.
*Understanding the value chain is critical to identifying failure points.
*Challenges come in determining if there is a failure and designing the system for auto-
healing
*The focus should be first to prevent a failure from occurring.
*Identifying critical challenges in your company and tools and techniques to auto-heal and provide a sustainable solution
Course Schedule
Evolutionary Architecture:
– Scaling to 100 million customers
– Understanding Requirements - Empathy Map
– Fail Points
– Defining KPIs
Resilient Patterns:
– BulkHead pattern
– Routing Strategies
– Design Issues with Microservices
– API Gateway Pattern
– Database per Service Pattern
– Database Sharding Patterns
– Fan out Pattern
– Publish-Subscribe Pattern
– Command Query Responsibility Segregation (CQRS)
– Message filter pattern
– Topic-queue-chaining Pattern
– Message Partitioning Patterns
– Priority Queue Pattern
Caching:
– Caching and Failure Injection
– Distributed system challenges
– Caching Patterns
– Order in Chaos
– Resilient Steps
– Resources
Designing Well Architected Framework Workshop - Deep Dive
Tuesday, 1:00 PM EST
Secure, Efficient, Resilient, High-performing, Sustainable, and Cost-effective
Are your applications well-architected? This talk will explore the best practices for operational excellence, Security, Reliability, Performance Efficiency, and cost optimization. Think of systems and services which provide business values. Do you know if all of these services are well-architected? You will learn how to create mechanisms, a repeatable process that allows you to improve over time. We will explore the best practices using real-world examples to make them more concrete and actionable.
Well-Architected helps cloud architects build secure, high-performing, resilient, and efficient infrastructure for various applications and workloads. They are built around six pillars—operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability.
Join expert Rohit Bhardwaj to gain the knowledge and skills you need to solve current cloud implementation problems.
Topics covered
Design Principles
Scaling patterns
Architecture Design Principles
Capacity calculations
Impact of data on design decisions
Shared Responsibility Model
Reliability
Resilient Architecture principles
Herds of complex real-time distributed systems
Hands-on Exercises / Case Studies
Blast radius- fault isolation to protect your workload - 10 minutes
Availability patterns - 10 minutes
Recovery Point Objective and Recovery Time Objectives
Data backup data patterns
Routing Strategies - 10 minutes
Service quotas and constraints - 5 minutes
Design your workload service architecture - 5 minutes
Failure management in a distributed system
Monitoring workload resources
Calculating the response times
Fallacies of Distributed Systems
Testing reliability
Hands-on Exercises / Case Studies
Cost Optimization
Design cost-optimized storage
Cost-optimized compute
Data transfer costs
Manage demand and supply resources
Hands-on Exercises / Case Studies
Sustainability
User behavior patterns
Data access and usage patterns
Development and deployment processes
Hands-on Exercises / Case Studies
Performance Efficiency
Select the best-performing architecture
Choosing performant storage and databases?
No-SQL for performance
Caching strategies
DOS attacks
Tradeoffs to improve performance
Evolving your workload
Handle skewed data
CDN networks like Cloudfront to solve the caching requirements for static and Dynamic contents
Monitor and set alarms for performance and network issues
Hands-on Exercises / Case Studies
Operational Excellence
Principles for Perform Operation Infrastructure as code
Annotate Documentation - PlayBooks - Part of code
Create Runbooks - Server down
Capture failures and analyze them using Events and Real-Time Actions
KPIs for cloud dashboard
Incidence response - Root Cause Analysis
Hands-on Exercises / Case Studies
Security, Privacy, and Compliance
Manage identities for people and machines
Identify Access Management Role-Based, Service-Based, and Attribute-Based Access
Securely operate your workload.
Detect and investigate security events
Web Application Firewall
Virtual Private Cloud - design network topology
Protecting your network resources
Bastion Hosts
Data classification
Protecting data in Transit
Protecting data at Rest
Hands-on Exercises / Case Studies
Designing Well Architected Framework Workshop - Deep Dive
Tuesday, 3:00 PM EST
Secure, Efficient, Resilient, High-performing, Sustainable, and Cost-effective
Are your applications well-architected? This talk will explore the best practices for operational excellence, Security, Reliability, Performance Efficiency, and cost optimization. Think of systems and services which provide business values. Do you know if all of these services are well-architected? You will learn how to create mechanisms, a repeatable process that allows you to improve over time. We will explore the best practices using real-world examples to make them more concrete and actionable.
Well-Architected helps cloud architects build secure, high-performing, resilient, and efficient infrastructure for various applications and workloads. They are built around six pillars—operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability.
Join expert Rohit Bhardwaj to gain the knowledge and skills you need to solve current cloud implementation problems.
Topics covered
Design Principles
Scaling patterns
Architecture Design Principles
Capacity calculations
Impact of data on design decisions
Shared Responsibility Model
Reliability
Resilient Architecture principles
Herds of complex real-time distributed systems
Hands-on Exercises / Case Studies
Blast radius- fault isolation to protect your workload - 10 minutes
Availability patterns - 10 minutes
Recovery Point Objective and Recovery Time Objectives
Data backup data patterns
Routing Strategies - 10 minutes
Service quotas and constraints - 5 minutes
Design your workload service architecture - 5 minutes
Failure management in a distributed system
Monitoring workload resources
Calculating the response times
Fallacies of Distributed Systems
Testing reliability
Hands-on Exercises / Case Studies
Cost Optimization
Design cost-optimized storage
Cost-optimized compute
Data transfer costs
Manage demand and supply resources
Hands-on Exercises / Case Studies
Sustainability
User behavior patterns
Data access and usage patterns
Development and deployment processes
Hands-on Exercises / Case Studies
Performance Efficiency
Select the best-performing architecture
Choosing performant storage and databases?
No-SQL for performance
Caching strategies
DOS attacks
Tradeoffs to improve performance
Evolving your workload
Handle skewed data
CDN networks like Cloudfront to solve the caching requirements for static and Dynamic contents
Monitor and set alarms for performance and network issues
Hands-on Exercises / Case Studies
Operational Excellence
Principles for Perform Operation Infrastructure as code
Annotate Documentation - PlayBooks - Part of code
Create Runbooks - Server down
Capture failures and analyze them using Events and Real-Time Actions
KPIs for cloud dashboard
Incidence response - Root Cause Analysis
Hands-on Exercises / Case Studies
Security, Privacy, and Compliance
Manage identities for people and machines
Identify Access Management Role-Based, Service-Based, and Attribute-Based Access
Securely operate your workload.
Detect and investigate security events
Web Application Firewall
Virtual Private Cloud - design network topology
Protecting your network resources
Bastion Hosts
Data classification
Protecting data in Transit
Protecting data at Rest
Hands-on Exercises / Case Studies
AI-Enhanced Big Data: Integrating Private LLMs and Vector Databases
Tuesday, 5:00 PM EST
In this dynamic talk, we explore the fusion of AI, particularly ChatGPT, with data-intensive architectures. The discussion covers the enhancement of big data processing and storage, the integration of AI in distributed data systems like Hadoop and Spark, and the impact of AI on data privacy and security. Emphasizing AI's role in optimizing big data pipelines, the talk includes real-world case studies, culminating in a forward-looking Q&A session on the future of AI in big data.
This talk delves into the innovative integration of advanced AI models like ChatGPT into data-intensive architectures. It begins with an introduction to the significance of big data in modern business and the role of AI in scaling data solutions. The talk then discusses the challenges and strategies in architecting big data processing and storage systems, highlighting how AI models can enhance data processing efficiency.
A significant portion of the talk is dedicated to exploring distributed data systems and frameworks, such as Apache Hadoop and Spark, and how ChatGPT can be utilized within these frameworks for improved parallel data processing and analysis. The discussion also covers the critical aspects of data privacy and security in big data architectures, especially considering the implications of integrating AI technologies like ChatGPT.
The talk further delves into best practices for managing and optimizing big data pipelines, emphasizing the role of AI in automating data workflow, managing data lineage, and optimizing data partitioning techniques. Real-world case studies are presented to illustrate the successful implementation of AI-enhanced data-intensive architectures in various industries.
Introduction (10 mins)
- Unleashing the power of big data in modern businesses
- Importance of data-intensive architectures in scaling data solutions
- Introducing AI's role in big data, with a focus on ChatGPT
Part 1: Architecting for Big Data Processing and Storage (25 mins)
- Understanding the challenges of big data processing
- Designing scalable data storage solutions
- Achieving high availability and fault tolerance
- Integrating AI models like ChatGPT for enhanced data processing
Part 2: Distributed Data Systems and Frameworks (25 mins)
- Leveraging the potential of distributed processing tools
- Introduction to Apache Hadoop, Spark, and other frameworks
- Performing parallel data processing and analysis
- How ChatGPT and similar AI models can be utilized in distributed systems
Part 3: Handling Data Privacy and Security in Big Data Architectures (20 mins)
- Challenges and considerations for data privacy in big data environments
- Ensuring data security and confidentiality
- Adhering to compliance regulations in big data projects
- Discussing the implications of AI like ChatGPT on data privacy and security
Part 4: Best Practices for Managing and Optimizing Big Data Pipelines (20 mins)
- Data workflow orchestration and automation
- Data lineage and metadata management
- Data partitioning and optimization techniques
- Utilizing AI models like ChatGPT for optimizing big data pipelines
Case Studies and Real-World Applications (10 mins)
- Inspiring examples of successful data-intensive architecture implementations
- Learning from the experiences of leading organizations
- Case studies involving ChatGPT in big data solutions
Conclusion and Q&A (10 mins)
- Recapitulation of key takeaways
- Addressing questions and facilitating discussions with the audience
- Highlighting the future of AI and big data with technologies like ChatGPT
Overall, this talk aims to provide a comprehensive understanding of how AI, especially ChatGPT, can be integrated into data-intensive architectures to enhance big data processing, analysis, and management, preparing attendees to harness AI's potential in their big data endeavors.
Key Takeaways:
- AI's Impact on Big Data: Insight into how AI, especially ChatGPT, enhances big data processing and scalability.
- Designing AI-Integrated Systems: Strategies for building scalable, AI-enabled data processing and storage solutions.
- AI in Distributed Frameworks: Understanding the integration of AI in systems like Hadoop and Spark for improved data analysis.
- Data Privacy and Security: Best practices for maintaining data integrity and compliance in AI-enhanced big data environments.
- Optimizing Data Pipelines with AI: Techniques for using AI to automate data workflows and optimize data management.
- Real-World AI Applications: Learning from case studies where AI in data architectures has driven success.
- Future of AI in Big Data: Insights into the evolving role and potential of AI technologies like ChatGPT in big data.
- Interactive Learning: Engaging in discussions and Q&A for a deeper understanding of AI's role in big data.
Modernizing Legacy Systems: AI-Enhanced Cloud Adoption Frameworks
Thursday, 9:00 AM EST
“By 2030, 80 percent of heritage financial services firms will go out of business, become commoditized, or exist only formally but not competing effectively”, predicts Gartner.
This session explores the integration of AI, specifically ChatGPT, into cloud adoption frameworks to modernize legacy systems. Learn how to leverage AWS Cloud Adoption Framework (CAF) 3.0, Microsoft Cloud Adoption Framework for Azure, and Google Cloud Adoption Framework to build cloud-native architectures that maximize scalability, flexibility, and security. Designed for architects, technical leads, and senior IT professionals, this talk provides actionable insights and strategies for successful digital transformation.
Cloud adoption frameworks are essential for accelerating digital business transformation by leveraging the power of cloud technologies. This talk will guide you through the AWS Cloud Adoption Framework (CAF) 3.0, Microsoft Cloud Adoption Framework for Azure, and Google Cloud Adoption Framework, focusing on building cloud-native architectures that ensure scalability, flexibility, and security.
The session will delve into the strategic role of AI, particularly ChatGPT, in modernizing legacy systems. By understanding and implementing these frameworks, you will learn to navigate the complexities of transitioning from legacy systems to modern cloud-based architectures. This talk will provide practical steps and real-world case studies to help you effectively plan and execute your cloud adoption strategy.
Legacy systems can be assets and obstacles, providing reliable functionality but often becoming burdensome to maintain and evolve. In this talk, we will confront the challenges of working with legacy architectures and discover the strategic approaches for modernization. By examining the benefits and risks of incremental migration versus full system rewrites, attendees will learn the most suitable path for their unique situations.
Through practical examples and case studies, we will explore how successful organizations have revitalized their aging architectures, preserving the value of legacy investments while embracing innovation and adaptability. From small-scale legacy components to large-scale monolithic systems, we'll cover diverse modernization scenarios, allowing participants to glean insights applicable to their projects.
Whether your organization is facing budget constraints, a need for rapid modernization, or concerns about maintaining critical functionality, this talk offers a comprehensive guide to navigating the legacy landscape and crafting a roadmap to rejuvenate aging architectures.
Agenda:
Introduction:
- Overview of the session
- Importance of cloud adoption frameworks in digital transformation
- Introduction to AI and ChatGPT in modernizing legacy systems
Understanding Cloud Adoption Frameworks:
- Overview of AWS Cloud Adoption Framework (CAF) 3.0
- Introduction to Microsoft Cloud Adoption Framework for Azure
- Introduction to Google Cloud Adoption Framework
- Key components and benefits of each framework
Strategic Role of AI in Legacy Modernization:
- How AI, particularly ChatGPT, is revolutionizing the modernization of legacy systems
- Benefits of integrating AI in cloud adoption frameworks
Steps for Moving Legacy Systems to the Cloud:
- Assessing legacy systems and identifying modernization opportunities
- Using CAF frameworks to plan and execute migration strategies
- Incremental migration vs. full system rewrites
- Ensuring compliance, security, and performance during the transition
ChatGPT's Role in Legacy Analysis:
- Utilizing ChatGPT for analyzing legacy code
- Aiding in documentation and understanding complex, outdated codebases
- Practical examples of ChatGPT in legacy modernization
Building Cloud-Native Architectures:
- Designing scalable, flexible, and secure cloud-native solutions
- Leveraging cloud-native services and best practices
- Implementing continuous integration and continuous delivery (CI/CD) pipelines
Case Studies and Real-World Applications:
- Examples of successful legacy system modernizations using AI and cloud frameworks
- Lessons learned and best practices from leading organizations
Practical Tips and Best Practices:
- Actionable advice for managing and optimizing cloud migration
- Strategies for ensuring successful digital transformation
Conclusion and Q&A:
- Recapitulation of key takeaways
- Addressing final questions and facilitating discussions with the audience
- Highlighting the future of AI and cloud adoption in modernizing legacy systems
Participants will leave this session equipped with a robust understanding of how to leverage AI, particularly ChatGPT, in the context of legacy system modernization. You will gain strategic insights, practical tools, and actionable knowledge to lead your teams and projects towards successful, AI-enhanced modernization efforts, ensuring your organization remains competitive and agile in a rapidly evolving digital landscape.
Next-Gen Software Architecture with Large Language Models(LLMs) like ChatGPT
Thursday, 11:00 AM EST
This is a dynamic session exploring the integration of cutting-edge AI technologies into software architecture. This talk provides senior developers and architects with actionable insights on leveraging large language models like ChatGPT to enhance design processes, manage architectural tradeoffs, and achieve scalable, innovative solutions.
Overview of the session
Importance of large language models (LLMs) in software architecture
Introduction to ChatGPT and its relevance for software architects
Part 1:
The Role of Large Language Models in Software Architecture
Understanding the capabilities of LLMs like ChatGPT
Benefits of integrating LLMs in modern software development
Real-world examples of AI-enhanced software architecture
Part 2: Prompt Engineering for Architectural Tasks
Crafting effective prompts for ChatGPT
Strategies for creating precise and effective prompts
Examples of architectural prompts and their impact
Interactive Exercise: Participants craft and test their own prompts
Feedback and discussion on prompt effectiveness
Part 3: Optimizing Requirement Analysis with ChatGPT
Leveraging ChatGPT for requirement analysis and design
Integration of AI in empathizing with client needs and journey mapping
Cost estimations, compliance, security, and performance
Case Study: Using empathy map and customer journey map tools in conjunction with AI
Hands-On Exercise: Requirement analysis and design
Part 4: Managing Architectural Tradeoffs
Defining and understanding architectural tradeoffs
Exploring real-world tradeoff scenarios
Case Study 1: Scalability vs. Flexibility
Case Study 2: Time-to-Market vs. Maintainability
Leveraging AI insights to analyze tradeoffs
Group Discussion and Q&A
Part 5: Best Practices for Integrating AI in Software Architecture
Techniques for gathering and prioritizing project requirements
Aligning architectural decisions with business objectives
Evaluating risks and potential outcomes of tradeoffs
Assessing tools, technologies, and architectural patterns
AI-powered decision support with ChatGPT
Collaborative decision-making and involving stakeholders
Part 6: Achieving Sustainable Innovation
Leveraging tradeoffs to drive innovation and creativity
Recap of key points and takeaways
Panel Discussion with Industry Experts
AI in architectural innovation: ChatGPT in action
Q&A and Open Discussion with the Audience
Conclusion
Recapitulation of key takeaways
Addressing final questions and facilitating discussions with the audience
Highlighting the future of AI and big data with technologies like ChatGPT