GigaSpaces PAAS For Cloud Based Java Applications

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Session presented at the 2nd IndicThreads.com Conference on Cloud Computing held in Pune, India on 3-4 June 2011. http://CloudComputing.IndicThreads.com

Technology Education

Gigapaces PAAS For Cloud Based Java Applications Karan Nangru Xebia IT Architects

What is GigaSpaces XAP? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Tier Based Architecture Tier integration impacts scalability, performance, and reliability

The Gigaspaces Architecture A single model for full life cycle enterprise systems

From J2EE to XAP Component J2EE XAP Persistency JPA Hibernate, JPA Messaging JMS,MDB JMS, Polling/Notify Container , Native Messaging API Security JAAS, SSL Spring Security, SSL Web Session Management HttpSession HttpSession (via Jetty) Transaction Management JTA Spring Transaction via Jini Transaction Manager Data Access JDBC , Session Bean, Entity Bean JDBC, Space, JPA Remoting EJB, IIOP, RMI Spring remoting over LRMI Web Servlet, JSP Servlet, JSP (via Jetty) Packaging and deployment ear , war jar , war Contexts and Dependency Injection JSR 299 Spring IOC System Management JMX JMX , SNMP, Native Admin API Java Naming and Directory Service JNDI Jini Lookup Service

What Else…. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Scope of this session….. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Space Based Architecture – Scaling Diagram 1. Car wash, the tier based way: a different warehouse for each step in the process.

Space Based Architecture – The Unit System Diagram 2. An all-in-one "washing unit" containing soaping, watering and drying services.

Space Based Architecture – Multiple Units Diagram 3. Scaling the system by adding more "car washing units"

Space Based Architecture – A GigaSpaces Processing Unit Diagram 4. A Processing Unit with 3 services collocated with a space.

Web application Deployment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Remoting ,[object Object],[object Object],The First phase involves the Service invocation The Second phase involves reducing the results retrieved from the Services

Messaging and Processing - Polling Container ,[object Object],[object Object],Write

Messaging and Processing – Notify Container ,[object Object],[object Object],Write

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Today’s products - devices, software and services - are well instrumented to permit users, vendors and service providers to gather maximum insight into how they are used, when they need repair and many other operational insights. Ensuring that products can rapidly adapt to a constantly changing environment and changing customer needs requires that the events they generate are analyzed continuously and in context. Insights can be synthesized from many sources in context - geospatial and proximity, trajectory and even predicted future states.Customers, vendors and service providers need to analyze, learn, and predict directly from streaming events because data volumes are huge and automated responses must often be delivered in milliseconds. To achieve insights quickly, we need to build models on-the-fly whose predictions are accurate and in sync with the real world, often to support automation. Many insights depend on analyzing the joint evolution of data sources whose behavior is correlated in time or space.In this talk we present Swim, an Apache 2.0 licensed platform for continuous intelligence applications. Swim builds a fluid model of data sources and their changing relationships in real-time - Swim applications analyze, learn and predict directly from event data. Swim applications integrate with Apache Kafka for event streaming. Developers need nothing more than Java skills. Swim deploys native or in containers on k8s, with the same code in each instance. Instances link to build an application layer mesh that facilitates distribution and massive scale without sacrificing consistency. We will present several continuous intelligence applications in use today that depend on real-time analysis, learning and prediction to power automation and deliver responses that are in sync with the real-world. We will show how easy it is to build, deploy and run distributed, highly available event streaming applications that analyze data from hundreds of millions of sources - petabytes per day. The architecture is intuitively appealing and blazingly fast.

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MakeMyTrip - India's #1 online travel platform having more than 70% of the traffic from mobile apps embarked on a journey to revolutionize its customer experience by building a scalable, personalized, machine learning based platform which powers onboarding, in-funnel and post-funnel engagement flows, such as ranking, dynamic pricing, persuasions, cross-sell and propensity models.For a company like MakeMyTrip, the next wave of consumer growth is driven and powered by data products for personalization, context-aware mobile experiences. Having a better data architecture to ingest user activity streams (events), processing and data APIs enable a foundation for real-time feature generation for machine learning models.Topics include:* Why common feature-store, removing dataset fragmentation caused by usecase-by-usecase approach!* Productionizing ML via standardization : MetaConfigs & FeatureCatalog | Reducing Data-Tech Debt* Developing Real-Time Serving store over Spark Streaming, Kafka, RocksDB, Akka HTTP Data APIs* Lifecycle of feature generation | Online(Near Real-Time) & Historical(Batch) Compute* Consistent Feature Engineering & Model Deployment for DSA: DataScience AutomationAs Technology we leverage Kafka, Spark (Streaming, SQL), Scala, Python, AWS (S3, EMR, Glue and other services), DRUID, Hive, Presto, Cassandra, RocksDB, Redis, Akka HTTP

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This talk offers actionable insights at an executive level for enhancing productivity and refining your portfolio management approach to propel your organization to greater heights. Key Points Covered: 1. Experience Transformation: - The core challenge remains consistent across organizations: converting budget into user-centric designs. - Strategies for deploying design resources effectively in both startups and large enterprises. 2. Strategic Frameworks: - Introduction to the "Ziggurat of Impact" model, detailing layers from basic system interactions to comprehensive customer experiences. - Practical insights on creating frameworks that scale with organizational complexity. 3. Organizational Impact: - Real-world examples of navigating design in large settings, focusing on the synthesis of consumer products and customer experiences. - Emphasis on the importance of designing systems that directly influence customer interactions. 4. Design Execution: - Detailed walkthrough of organizational layers affecting design execution, from touchpoints and customer activities to shared capabilities. - How to ensure design influences both the micro and macro aspects of customer interactions. 5. Measurement and Adaptation: - Techniques for measuring the impact of design decisions and adapting strategies based on data-driven insights. - The critical role of continuous improvement and feedback in refining customer experiences.

Structuring Teams and Portfolios for Success

UXDXConf

Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to: Create a campaign using Mailchimp with merge tags/fields Send an interactive Slack channel message (using buttons) Have the message received by managers and peers along with a test email for review But there’s more: In a second workflow supporting the same use case, you’ll see: Your campaign sent to target colleagues for approval If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team But—if the “Reject” button is pushed, colleagues will be alerted via Slack message Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors. And... Speakers: Akshay Agnihotri, Product Manager Charlie Greenberg, Host

Connector Corner: Automate dynamic content and events by pushing a button

DianaGray10

In this session, we will showcase how to revolutionize automated testing for your software, automation, and QA teams with UiPath Test Suite. In part 1 of UiPath test automation using UiPath Test Suite – developer series, we will cover, Software testing overview What is software testing Why software testing is required Typical test types and levels Continuous testing and challenges Introduction to UiPath Test Suite UiPath Test Suite family of products Speaker: Atul Trikha, Chief Technologist & Solutions Architect, Peraton and UiPath MVP Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

UiPath Test Automation using UiPath Test Suite series, part 1

DianaGray10

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

Julian Hyde

PLAI - Acceleration Program for Generative A.I. Startups

Stefano

This presentation dives into the practical applications of machine learning within Google's operations, providing a comprehensive overview of how to leverage AI technologies to solve real-world business challenges. Key Points Covered: - Introduction to Machine Learning at Google: Discussion on the role of ML and its evolution in enhancing Google's operational efficiency. - Experience Sharing: Insights into the team's long-term engagement with machine learning projects and the impacts on Google’s operational strategies. - Practical Applications: Real-world examples of ML applications within Google’s daily operations, providing a blueprint to adapt similar strategies. - Challenges and Solutions: Discussion on the challenges faced during the implementation of ML projects and the strategic solutions employed to overcome them. - Future of ML at Google: Insights into future trends in machine learning at Google and how they plan to continue integrating AI into their ecosystem.

Strategic AI Integration in Engineering Teams

UXDXConf

Intro in Product Management - Коротко про професію продакт менеджера

Mark Opanasiuk

IESVE for Early Stage Design and Planning

IES VE

This presentation discusses the complexities of aligning teams and ensuring consistent product experiences across various platforms, proposing Server Driven UI (SDUI) as a solution. Key Points Covered: - The challenge of maintaining consistency in product experiences across web and app interfaces, highlighted by discrepancies in user experience features like comment sections. - Introduction of Server Driven UI (SDUI) to manage uniformity and streamline updates across different platforms. - The importance of adapting design systems to accommodate SDUI, ensuring uniform naming conventions, and component functionalities. - Technical discussions on overcoming framework differences and the operational load on developers due to continuous OS updates.

Server-Driven User Interface (SDUI) at Priceline

UXDXConf

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Paul Groth

IoT Analytics Company Presentation May 2024

IoTAnalytics

In an era where artificial intelligence (AI) is becoming increasingly integrated into our daily lives, the need for empathetic technology is paramount. This talk explores the role of motion design in enhancing AI interfaces to foster empathy and improve user experiences. Key Topics Covered: 1. Introduction to Empathetic AI - The importance of creating empathy in AI interfaces - Personal journey from character animation to AI and robotics design 2. The Role of Motion in User Interaction - Historical context of motion in design, from early Disney animations to modern interfaces - The blinking cursor: An example of effective motion use since 1960 3. Designing for Empathy - How motion can enhance user satisfaction and trust in AI systems - Examples from human-robot interaction and AI interfaces 4. Creating Human-like Interactions - Using motion to provide transparency and context in AI interactions - The importance of natural conversation flows and the ability to interrupt AI responses 5. Avoiding the Uncanny Valley - Recognizing and mitigating the uncanny valley in AI-generated content - Examples of effective and ineffective uses of motion in AI 6. Systemizing Motion Design - Strategies for integrating motion into AI systems at scale - Salesforce’s approach to creating and scaling a motion system for AI

Motion for AI: Creating Empathy in Technology

UXDXConf

The standard Salesforce Approval process can be limiting in many ways, especially in complex scenarios. What if there was a way to implement very flexible approvals where one can use Apex code to make data updates in unrelated records, dynamically generate next steps details, and compute assignees on the fly? And still use UI-based configurations to implement concrete approval processes. In this session, we will share ideas behind such a solution and show a few lines of code to get you started.

Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder

CzechDreamin

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Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality

Connecting the Dots in Product Design at KAYAK

Structuring Teams and Portfolios for Success

Connector Corner: Automate dynamic content and events by pushing a button

UiPath Test Automation using UiPath Test Suite series, part 1

Measures in SQL (a talk at SF Distributed Systems meetup, 2024-05-22)

PLAI - Acceleration Program for Generative A.I. Startups

Strategic AI Integration in Engineering Teams

Intro in Product Management - Коротко про професію продакт менеджера

IESVE for Early Stage Design and Planning

Server-Driven User Interface (SDUI) at Priceline

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

IoT Analytics Company Presentation May 2024

Motion for AI: Creating Empathy in Technology

Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder

GigaSpaces PAAS For Cloud Based Java Applications

1. Gigapaces PAAS For Cloud Based Java Applications Karan Nangru Xebia IT Architects

3. The Tier Based Architecture

4. Cache A Typical Tier Scenario

5. The Tier Based Architecture Tier integration impacts scalability, performance, and reliability

6. The Gigaspaces Architecture A single model for full life cycle enterprise systems

7. From J2EE to XAP Component J2EE XAP Persistency JPA Hibernate, JPA Messaging JMS,MDB JMS, Polling/Notify Container , Native Messaging API Security JAAS, SSL Spring Security, SSL Web Session Management HttpSession HttpSession (via Jetty) Transaction Management JTA Spring Transaction via Jini Transaction Manager Data Access JDBC , Session Bean, Entity Bean JDBC, Space, JPA Remoting EJB, IIOP, RMI Spring remoting over LRMI Web Servlet, JSP Servlet, JSP (via Jetty) Packaging and deployment ear , war jar , war Contexts and Dependency Injection JSR 299 Spring IOC System Management JMX JMX , SNMP, Native Admin API Java Naming and Directory Service JNDI Jini Lookup Service

10. Space Based Architecture – Scaling Diagram 1. Car wash, the tier based way: a different warehouse for each step in the process.

11. Space Based Architecture – The Unit System Diagram 2. An all-in-one "washing unit" containing soaping, watering and drying services.

12. Space Based Architecture – Multiple Units Diagram 3. Scaling the system by adding more "car washing units"

13. Space Based Architecture – A GigaSpaces Processing Unit Diagram 4. A Processing Unit with 3 services collocated with a space.

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16. HTTP Session Management

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25. @references - Gigaspaces

GigaSpaces PAAS For Cloud Based Java Applications

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