This document describes a remote analytical service that provides instrument-level analytical capabilities to clients by wrapping the QuantLib and OpenGamma quantitative libraries. The service handles reference data and calendar management, supports various financial instruments, and can be embedded or accessed remotely. It aims to simplify analytics for end users while hiding the complexity of the underlying libraries.
Enterprise wide publish subscribe with Apache KafkaJohan Louwers
The document discusses enterprise wide publish/subscribe models using Apache Kafka. It describes moving from monolithic architectures to microservice architectures and how Kafka can be used to distribute transactions between microservices. It provides examples of publishing and subscribing using Kafka clients and discusses considerations for deploying Kafka in a highly available manner across multiple data centers.
Machine Learning with Apache Kafka in Pharma and Life SciencesKai Wähner
Blog Post:
https://www.kai-waehner.de/apache-kafka-event-streaming-pharmaceuticals-pharma-life-sciences-use-cases-architecture
Video Recording:
https://youtu.be/t2IH0brwGTg
AI/Machine learning and the Apache Kafka ecosystem are a great combination for training, deploying and monitoring analytic models at scale in real-time. They are showing up more and more in projects but still, feel like buzzwords and hype for science projects.
See how to connect the dots!
--How are Kafka and Machine Learning related?
--How can they be combined to productionize analytic models in mission-critical and scalable real-time applications?
--We will discuss a step-by-step approach to build a scalable and reliable real-time infrastructure for drug discovery doing data integration, feature engineering, image processing, model scoring and processing orchestration.
Use Cases:
R&D Engineering
Sales & Marketing
Manufacturing & Quality Assurance
Supply Chain
Product Monitoring & After Sales Support
VoC (Voice of Customer)
Single View Customer
Yield/Quality Optimization
Improved Drug Yield
Proactive Service Scheduling
Testing & Simulation
Drug Diversion
Process/Quality Monitoring
Inventory & Supply Chain Optimization
Proactive Service Offers
Patent Research and Analytics
Personalized Offers / Ads
EDW Offload
Supply Chain Network Design/Risk Management
Product Predictive Maintenance
Clinical Trials
Customer Segmentation
Smart Products
Serialization & e-Pedigree
Product Usage Tracking
GTM
Global Facilities
Inventory and Logistics Visibility
Warranty & Recall Management
Event-Based Business Architecture: Orchestrating Enterprise Communications confluent
(Gary Samuelson, GarySamuelson) Kafka Summit SF 2018
A business-oriented view, illustrating both process models and in-flight task progress, is critical to understanding organizational health, efficiency and alignment to strategic goals. The intent of this talk is to illustrate the real-time relationship between Kafka-managed events (event driven) and business architecture via actionable models (real-time analytics).
Takeaways:
-Understand how business views technology in terms of capabilities aligned to strategy.
-Introduce process model and performance views into an event-oriented dashboard. This view illustrates the organization in terms of collaborating human and automated services.
-Illustrate how system architecture dovetails into business goals with an aligned business/IT architectures.
Scotas + Cima - Oracle Open World Sao Pablo 2012Julian Arocena
With BLEND, companies can integrate and organize large volumes of structured and unstructured data from across their systems onto a single platform in real time. This consolidated data contains valuable information from various sources like customers, transactions, security risks, and potential fraud. BLEND provides solutions for application performance management, log management, and security and fraud detection by ingesting logs from many sources and allowing users to analyze the data.
Through this presentation, Mr. Vineet Khanna, Director - Practices, SAS India; talks about the key considerations for ALM, need for data management, analytics and optimisation.
Building Event Driven (Micro)services with Apache KafkaGuido Schmutz
What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will start with quick recap of how we created systems over the past 20 years and how different architectures evolved from it. The talk will show how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so.
Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Event-based APIs are becoming more popular, enabling developers to craft new integrations and solutions that go beyond the original design of an API. Yet, there remains a challenge: how can teams design thoughtful event-based APIs that are long-lasting, evolvable, and discoverable? This talk will dive into the design practices of event-based APIs, including tips for determining which protocol(s) you should select, design patterns we should apply, and anti-patterns should we avoid. We will also look at how AI and tools such as ChatGPT are starting to shape the next generation of APIs.
Delivered on May 10, 2023 for the EDA Summit
This document describes a remote analytical service that provides instrument-level analytical capabilities to clients by wrapping the QuantLib and OpenGamma quantitative libraries. The service handles reference data and calendar management, supports various financial instruments, and can be embedded or accessed remotely. It aims to simplify analytics for end users while hiding the complexity of the underlying libraries.
Enterprise wide publish subscribe with Apache KafkaJohan Louwers
The document discusses enterprise wide publish/subscribe models using Apache Kafka. It describes moving from monolithic architectures to microservice architectures and how Kafka can be used to distribute transactions between microservices. It provides examples of publishing and subscribing using Kafka clients and discusses considerations for deploying Kafka in a highly available manner across multiple data centers.
Machine Learning with Apache Kafka in Pharma and Life SciencesKai Wähner
Blog Post:
https://www.kai-waehner.de/apache-kafka-event-streaming-pharmaceuticals-pharma-life-sciences-use-cases-architecture
Video Recording:
https://youtu.be/t2IH0brwGTg
AI/Machine learning and the Apache Kafka ecosystem are a great combination for training, deploying and monitoring analytic models at scale in real-time. They are showing up more and more in projects but still, feel like buzzwords and hype for science projects.
See how to connect the dots!
--How are Kafka and Machine Learning related?
--How can they be combined to productionize analytic models in mission-critical and scalable real-time applications?
--We will discuss a step-by-step approach to build a scalable and reliable real-time infrastructure for drug discovery doing data integration, feature engineering, image processing, model scoring and processing orchestration.
Use Cases:
R&D Engineering
Sales & Marketing
Manufacturing & Quality Assurance
Supply Chain
Product Monitoring & After Sales Support
VoC (Voice of Customer)
Single View Customer
Yield/Quality Optimization
Improved Drug Yield
Proactive Service Scheduling
Testing & Simulation
Drug Diversion
Process/Quality Monitoring
Inventory & Supply Chain Optimization
Proactive Service Offers
Patent Research and Analytics
Personalized Offers / Ads
EDW Offload
Supply Chain Network Design/Risk Management
Product Predictive Maintenance
Clinical Trials
Customer Segmentation
Smart Products
Serialization & e-Pedigree
Product Usage Tracking
GTM
Global Facilities
Inventory and Logistics Visibility
Warranty & Recall Management
Event-Based Business Architecture: Orchestrating Enterprise Communications confluent
(Gary Samuelson, GarySamuelson) Kafka Summit SF 2018
A business-oriented view, illustrating both process models and in-flight task progress, is critical to understanding organizational health, efficiency and alignment to strategic goals. The intent of this talk is to illustrate the real-time relationship between Kafka-managed events (event driven) and business architecture via actionable models (real-time analytics).
Takeaways:
-Understand how business views technology in terms of capabilities aligned to strategy.
-Introduce process model and performance views into an event-oriented dashboard. This view illustrates the organization in terms of collaborating human and automated services.
-Illustrate how system architecture dovetails into business goals with an aligned business/IT architectures.
Scotas + Cima - Oracle Open World Sao Pablo 2012Julian Arocena
With BLEND, companies can integrate and organize large volumes of structured and unstructured data from across their systems onto a single platform in real time. This consolidated data contains valuable information from various sources like customers, transactions, security risks, and potential fraud. BLEND provides solutions for application performance management, log management, and security and fraud detection by ingesting logs from many sources and allowing users to analyze the data.
Through this presentation, Mr. Vineet Khanna, Director - Practices, SAS India; talks about the key considerations for ALM, need for data management, analytics and optimisation.
Building Event Driven (Micro)services with Apache KafkaGuido Schmutz
What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will start with quick recap of how we created systems over the past 20 years and how different architectures evolved from it. The talk will show how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so.
Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Event-based APIs are becoming more popular, enabling developers to craft new integrations and solutions that go beyond the original design of an API. Yet, there remains a challenge: how can teams design thoughtful event-based APIs that are long-lasting, evolvable, and discoverable? This talk will dive into the design practices of event-based APIs, including tips for determining which protocol(s) you should select, design patterns we should apply, and anti-patterns should we avoid. We will also look at how AI and tools such as ChatGPT are starting to shape the next generation of APIs.
Delivered on May 10, 2023 for the EDA Summit
A discussion of the Internet of Things and how I explored the use of an event-based API and microservices inside a unique architecture based on persistent compute objects, or picos, in the connected car platform called Fuse.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solutions and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
Understanding Business APIs through statisticsWSO2
This document discusses using statistics and data analysis to understand API usage. It describes WSO2's tools for offline and real-time analysis of API data. For offline analysis, the API Manager integrates with WSO2 Business Activity Monitor (BAM) which aggregates event streams, stores the data in Cassandra, analyzes it using Hive, and stores summaries in a relational database. For real-time analysis, the API Manager integrates with WSO2 Complex Event Processing (CEP) which executes queries over event streams to identify patterns like excessive requests from a client. It also discusses integrating Google Analytics for additional monitoring and visualization of API usage statistics.
Salesforce's Event-Driven Software Architecture is presented by Tim Taylor, a member of the Jacksonville FL Developer group. For more information see
Platform Events Developer Guide - https://developer.salesforce.com/docs/atlas.en-us.platform_events.meta/platform_events/platform_events_intro.htm
Platform Events Basics - https://trailhead.salesforce.com/en/content/learn/modules/platform_events_basics
Watch the live demo of Apigee's API platform to learn how to:
- easily configure and manage new APIs and enforce security with minimal impact to backend services
- create, manage and monetize API products
- extend API Services to increase flexibility and tailor to business requirements with JavaScript, Java, Python, and Node.js
- provide developers easy, yet secure access to explore, test, and deploy APIs
- use end-to-end visibility across the digital value chain to monitor, measure, and manage success
The document discusses three blueprints for streaming visualization:
1. Using a fast datastore with regular polling from consumers, which introduces some delay but allows using data stores' full capabilities. Example technologies are Elasticsearch/Kibana and InfluxDB/Grafana.
2. Directly streaming data to consumers with minimal latency but more complex client-side processing. Examples are Kafka Connect to Slack and WebSockets/SSE apps.
3. Streaming SQL results to consumers, providing SQL query capabilities with minimal latency but limiting historical data access. KSQL and Spark Streaming are discussed.
Serverless ML Workshop with Hopsworks at PyData SeattleJim Dowling
1. The document discusses building a minimal viable prediction service (MVP) to predict air quality using only Python and free serverless services in 90 minutes.
2. It describes creating feature, training, and inference pipelines to build an air quality prediction service using Hopsworks, Modal, and Streamlit/Gradio.
3. The pipelines would extract features from weather and air quality data, train a model, and deploy an inference pipeline to make predictions on new data.
Scaling Experimentation & Data Capture at GrabRoman
This is the slides from the presentation I gave at the Data Science Meetup Hamburg. This talks about how we build and scaled our online experimentation platform and associated event capture system.
Notification Services 2005 is a platform that enables developers to easily create rich, scalable notification applications. It uses a declarative programming model based on XML and T-SQL running on SQL Server 2005. Developers can manage subscriptions, define events and notifications, and integrate delivery channels like email or mobile alerts.
Building event-driven Microservices with Kafka EcosystemGuido Schmutz
This session will begin with a short recap of how we created systems over the past 20 years, up to the current idea of building systems, using a Microservices architecture. What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to integrate services with each each other in a Microservices Architecture? Or is it better to use a more loosely-coupled protocol? Answers to these and many other questions are provided. The talk will show how a distributed log (event hub) can help to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk shows the difference between a request-driven and event-driven communication and answers when to use which. It highlights how a modern stream processing system can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Building Event-Driven (Micro)Services with Apache KafkaGuido Schmutz
Should we use traditional REST APIs to bind services together? Or is it better to use a more loosely-coupled protocol? This talk will dive into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which.
Subscription based control system to automate management of events for robotsdbpublications
This document proposes a subscription-based control system using websockets to automate event management for robots. The current polling method used in human-machine interfaces restricts automation capabilities. The proposed system allows clients to subscribe to events and receive asynchronous updates from the server, making responses more reliable. It also explores using semantic web technologies to enable coordinated multi-robot activities through ontologies and web services.
This document discusses serverless applications and event management. It compares events to messages and different event streaming services like Event Grid, Event Hubs and Service Bus. It also provides examples of using GraphQL with serverless functions to handle events and real-time updates through subscriptions.
The internet of things requires a different architectural model than what we've used to build Web 2.0. This presentation makes a proposal for what that architecture could look like and presents a working example based on the connected car platform Fuse (http://joinfuse.com)
Pros and Cons of a MicroServices Architecture talk at AWS ReInventSudhir Tonse
Netflix morphed from a private datacenter based monolithic application into a cloud based Microservices architecture. This talk highlights the pros and cons of building software applications as suites of independently deployable services, as well as practical approaches for overcoming challenges - especially in the context of an elastic but ephemeral cloud ecosystem. What were the lessons learned while building and managing these services? What are the best practices and anti-patterns?
The new Process Events Monitoring feature set makes it possible for the first time to import process data into Optimize from a range of external sources and carry out monitoring, reporting, and continuous improvement for end-to-end processes even in cases where the entire process isn’t yet automated by Camunda BPM.
Enhancement in Optimize 3.0 include:
- New capabilities for efficient End-To-End Monitoring and Reporting
- New User Task Reporting and Monitoring capabilities which allow you to analyse performance trends for your user tasks
- New Flexible Alerting capabilities which allow you to send Alerts to any system of your choice
- New Dashboarding capabilities which simplify creating and modifying dashboards to a large extend
- Support for Elasticsearch 7
These new capabilities expand the scope of Optimize from a process analytics platform that’s entirely Camunda-centric to one that enables you to visualize, monitor, and improve processes anywhere in your organization–even the processes you haven’t yet gotten around to fully automating with Camunda.
In this webinar, Optimize Product Manager Felix Müller will be joined by Camunda Optimize Tech Lead Sebastian Bathke to share more on Process Events Monitoring and to show you step-by-step how to start using it.
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dig into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to
hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
This document discusses the importance of setting goals and provides tips for doing so effectively. It defines the differences between dreams and goals, noting that goals are more specific targets with accompanying plans of action. The key steps for setting goals are to decide what you want to accomplish, devise a plan to work towards it, and work on the plan to achieve your desired result. Setting goals helps people focus their time and energy, stay positive, and gain a sense of control over the direction of their lives. Tips for effective goal-setting include choosing worthwhile and achievable goals, making goals specific with deadlines, prioritizing, and rewarding accomplishments.
Customer Relationship Management (CRM) helps to develop and retain more profitable customer relationships through our broad range of capabilities that address every aspect of the customer experience. We help our clients accelerate growth, improve sales productivity and reduce customer-care costs to increase the value of their customer relationships and enhance the economic value of their brands.
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Similar to Bloomberg API Open Source Development and Solution Providers India
A discussion of the Internet of Things and how I explored the use of an event-based API and microservices inside a unique architecture based on persistent compute objects, or picos, in the connected car platform called Fuse.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solutions and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
Understanding Business APIs through statisticsWSO2
This document discusses using statistics and data analysis to understand API usage. It describes WSO2's tools for offline and real-time analysis of API data. For offline analysis, the API Manager integrates with WSO2 Business Activity Monitor (BAM) which aggregates event streams, stores the data in Cassandra, analyzes it using Hive, and stores summaries in a relational database. For real-time analysis, the API Manager integrates with WSO2 Complex Event Processing (CEP) which executes queries over event streams to identify patterns like excessive requests from a client. It also discusses integrating Google Analytics for additional monitoring and visualization of API usage statistics.
Salesforce's Event-Driven Software Architecture is presented by Tim Taylor, a member of the Jacksonville FL Developer group. For more information see
Platform Events Developer Guide - https://developer.salesforce.com/docs/atlas.en-us.platform_events.meta/platform_events/platform_events_intro.htm
Platform Events Basics - https://trailhead.salesforce.com/en/content/learn/modules/platform_events_basics
Watch the live demo of Apigee's API platform to learn how to:
- easily configure and manage new APIs and enforce security with minimal impact to backend services
- create, manage and monetize API products
- extend API Services to increase flexibility and tailor to business requirements with JavaScript, Java, Python, and Node.js
- provide developers easy, yet secure access to explore, test, and deploy APIs
- use end-to-end visibility across the digital value chain to monitor, measure, and manage success
The document discusses three blueprints for streaming visualization:
1. Using a fast datastore with regular polling from consumers, which introduces some delay but allows using data stores' full capabilities. Example technologies are Elasticsearch/Kibana and InfluxDB/Grafana.
2. Directly streaming data to consumers with minimal latency but more complex client-side processing. Examples are Kafka Connect to Slack and WebSockets/SSE apps.
3. Streaming SQL results to consumers, providing SQL query capabilities with minimal latency but limiting historical data access. KSQL and Spark Streaming are discussed.
Serverless ML Workshop with Hopsworks at PyData SeattleJim Dowling
1. The document discusses building a minimal viable prediction service (MVP) to predict air quality using only Python and free serverless services in 90 minutes.
2. It describes creating feature, training, and inference pipelines to build an air quality prediction service using Hopsworks, Modal, and Streamlit/Gradio.
3. The pipelines would extract features from weather and air quality data, train a model, and deploy an inference pipeline to make predictions on new data.
Scaling Experimentation & Data Capture at GrabRoman
This is the slides from the presentation I gave at the Data Science Meetup Hamburg. This talks about how we build and scaled our online experimentation platform and associated event capture system.
Notification Services 2005 is a platform that enables developers to easily create rich, scalable notification applications. It uses a declarative programming model based on XML and T-SQL running on SQL Server 2005. Developers can manage subscriptions, define events and notifications, and integrate delivery channels like email or mobile alerts.
Building event-driven Microservices with Kafka EcosystemGuido Schmutz
This session will begin with a short recap of how we created systems over the past 20 years, up to the current idea of building systems, using a Microservices architecture. What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to integrate services with each each other in a Microservices Architecture? Or is it better to use a more loosely-coupled protocol? Answers to these and many other questions are provided. The talk will show how a distributed log (event hub) can help to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk shows the difference between a request-driven and event-driven communication and answers when to use which. It highlights how a modern stream processing system can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Building Event-Driven (Micro)Services with Apache KafkaGuido Schmutz
Should we use traditional REST APIs to bind services together? Or is it better to use a more loosely-coupled protocol? This talk will dive into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which.
Subscription based control system to automate management of events for robotsdbpublications
This document proposes a subscription-based control system using websockets to automate event management for robots. The current polling method used in human-machine interfaces restricts automation capabilities. The proposed system allows clients to subscribe to events and receive asynchronous updates from the server, making responses more reliable. It also explores using semantic web technologies to enable coordinated multi-robot activities through ontologies and web services.
This document discusses serverless applications and event management. It compares events to messages and different event streaming services like Event Grid, Event Hubs and Service Bus. It also provides examples of using GraphQL with serverless functions to handle events and real-time updates through subscriptions.
The internet of things requires a different architectural model than what we've used to build Web 2.0. This presentation makes a proposal for what that architecture could look like and presents a working example based on the connected car platform Fuse (http://joinfuse.com)
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Netflix morphed from a private datacenter based monolithic application into a cloud based Microservices architecture. This talk highlights the pros and cons of building software applications as suites of independently deployable services, as well as practical approaches for overcoming challenges - especially in the context of an elastic but ephemeral cloud ecosystem. What were the lessons learned while building and managing these services? What are the best practices and anti-patterns?
The new Process Events Monitoring feature set makes it possible for the first time to import process data into Optimize from a range of external sources and carry out monitoring, reporting, and continuous improvement for end-to-end processes even in cases where the entire process isn’t yet automated by Camunda BPM.
Enhancement in Optimize 3.0 include:
- New capabilities for efficient End-To-End Monitoring and Reporting
- New User Task Reporting and Monitoring capabilities which allow you to analyse performance trends for your user tasks
- New Flexible Alerting capabilities which allow you to send Alerts to any system of your choice
- New Dashboarding capabilities which simplify creating and modifying dashboards to a large extend
- Support for Elasticsearch 7
These new capabilities expand the scope of Optimize from a process analytics platform that’s entirely Camunda-centric to one that enables you to visualize, monitor, and improve processes anywhere in your organization–even the processes you haven’t yet gotten around to fully automating with Camunda.
In this webinar, Optimize Product Manager Felix Müller will be joined by Camunda Optimize Tech Lead Sebastian Bathke to share more on Process Events Monitoring and to show you step-by-step how to start using it.
Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will dig into how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to
hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
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This document discusses the importance of setting goals and provides tips for doing so effectively. It defines the differences between dreams and goals, noting that goals are more specific targets with accompanying plans of action. The key steps for setting goals are to decide what you want to accomplish, devise a plan to work towards it, and work on the plan to achieve your desired result. Setting goals helps people focus their time and energy, stay positive, and gain a sense of control over the direction of their lives. Tips for effective goal-setting include choosing worthwhile and achievable goals, making goals specific with deadlines, prioritizing, and rewarding accomplishments.
Customer Relationship Management (CRM) helps to develop and retain more profitable customer relationships through our broad range of capabilities that address every aspect of the customer experience. We help our clients accelerate growth, improve sales productivity and reduce customer-care costs to increase the value of their customer relationships and enhance the economic value of their brands.
Android is a remarkable platform for smartphone devices that supports compelling and intuitive application developments. This flexible and feature-rich operating system is one of the most popularly used platforms today to create ground-breaking and dynamic third party mobile applications, leveraging the popularity of mobile computing.
This document provides 10 marketing tips for small businesses, including giving away products or services, attending or creating networking events, volunteering to lead an organization, starting a podcast, being helpful to others, sending a weekly email, supporting causes, sponsoring organizations, and creating a cool giveaway to promote the business.
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Our Internet marketing practice revolves around Enhancing brand value, attracting more Clients and building quality customer relationship by increasing traffic. Team of experts at PPTS is well acquainted with paid inclusion and contextual advertising. We ensure that the client gets guaranteed traffic by following both SEO and SEM. We ensure that the client gets maximum return on investment. Developing customized Internet marketing Strategies for companies are part of our services. We ensure in cost effective and customized solutions to clients with an Ethical approach.
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My slides at Nordic Testing Days 6.6.2024
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Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
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UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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* Live demos with code snippets
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#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
2. Overview of the Bloomberg API
The Bloomberg API provides developers with 24x7 programmatic access to data
from the Bloomberg Data Centre for use in customer applications.
The established service provides free, unrestricted access to raw data for customers
for its financial market information.
The Bloomberg API lets you integrate streaming real-time and delayed data,
reference data, historical data, intraday data, and Bloomberg derived data into
your own custom and third-party applications.
3. What kind of data?
Infrastructure for high-performance worldwide delivery of arbitrary structured data
from multiple distributed sources
Many different services, with "market data" most heavily used
Prices, trades, volumes, etc. delivered directly from exchanges
Real-time subscriptions to live data
Query interface to database of historical data
4. Features of the Bloomberg API
Interfaces in C, C++, Java, .NET, Perl, and Python
Linux, Windows, Solaris, Mac OS X
Full set of example applications—easy starting point
http://openbloomberg.com/open-api/
Lightweight Interfaces
32- and 64-bit Programming Support
Pure Java Implementation: The Java API is implemented entirely in Java. Bloomberg
did not use JNI to wrap either our existing C library or the new C++ library.
5. Real-time prices in Python
session = blpapi.Session()
session.start()
subscriptions = blpapi.SubscriptionList()
subscriptions.add("IBM US Equity", "LAST_PRICE,BID,ASK", "", blpapi::CorrelationId(1))
session.subscribe(subscriptions)
while (True):
event = session.nextEvent()
for msg in event:
print("IBM: ", msg)
6. Application Structure
The Bloomberg API object model contains a small number of key objects which
applications use to request, receive and interpret data.
Session: An application creates a Session object to manage its connection with the
Bloomberg infrastructure.
Service: Using the Session object, an application creates a Service object and then
‘opens’ each Bloomberg service that it will use.
Request: The client can make individual requests for data.
Subscription: the client can start a subscription with the service for on-going data
updates.
Event: Programmatically, the customer application obtains Event objects for the
Session and then extracts from those Event objects one or more Message objects
containing the Bloomberg data.
8. Using the Request/Response
Paradigm - 1
public static void main(String[] args) throws Exception {
SessionOptions sessionOptions = new SessionOptions();
sessionOptions.setServerHost("localhost"); // default value
sessionOptions.setServerPort(8194); // default value
Session session = new Session(sessionOptions);
if (!session.start()) {
System.out.println("Could not start session.");
System.exit(1);
}
if (!session.openService("//blp/refdata")) {
System.out.println("Could not open service " +
"//blp/refdata");
System.exit(1);
}
9. Using the Request/Response
Paradigm - 2
CorrelationID requestID = new CorrelationID(1);
Service refDataSvc = session.getService("//blp/refdata");
Request request =
refDataSvc.createRequest("ReferenceDataRequest");
request.append("securities", "IBM US Equity");
request.append("fields", "PX_LAST");
session.sendRequest(request, requestID);
boolean continueToLoop = true;
10. Using the Request/Response
Paradigm - 3
while (continueToLoop) {
Event event = session.nextEvent();
switch (event.eventType().intValue()) {
case Event.EventType.Constants.RESPONSE: // final event
continueToLoop = false; // fall through
case Event.EventType.Constants.PARTIAL_RESPONSE:
handleResponseEvent(event);
break;
default:
handleOtherEvent(event);
break;
}
}
}
11. Services
Core: Reference Data Service "//blp/refdata"
Market Data Service "//blp/mktdata"
Additional: Custom VWAP Service "//blp/mktvwap"
Market Bar Subscription Service "//blp/mktbar"
API Field Information Service "//blp/apiflds"
Page Data Service "//blp/pagedata"
Technical Analysis Service "//blp/tasvc"
API Authorization "//blp/apiauth"
13. Publishing
The Bloomberg API allows customer applications to publish data as well as consume
it. Customer data can be published for distribution within the customer’s enterprise,
contributed to the Bloomberg infrastructure, distributed to others, or used for
warehousing.
Publishing applications might simply broadcast data or they can be “interactive”,
responding to feedback from the infrastructure about the currently active
subscriptions from data consumers.
14. Simple Broadcast
Creating a session.
Obtaining authorization.
Creating the topic.
Publishing events for the topic to the designated service.
15. Interactive Publication
Creating a session.
Obtaining authorization.
Registering for subscription start and stop messages.
Handling subscription start and stop events, which add and remove topics to the
active publication set.
Creating a topic.
Publishing events for the active topics of the designated service.