A new version of Data Query is here to make AT Internet’s decision-making platform even more powerful. Discover how the API query generator can considerably improve your reporting and analysis and help drive your decisions.
Крауд маркетинг на практике ( Crowd marketing )Yuriy Titkov
Пример эффективности инструмента крауд маркетинга в SEO. Влияние упоминаний бренда, эффективность использования картинок с сайта в виде контента, упоминание и связка текстовых упоминаний домена. В связи с отказом поисковых систем от активных гиперссылок как элемента влияния на поисковую выдачу crowd marketing на сегодня является самым значимым инструментом.
Operationalizing Machine Learning—Managing Provenance from Raw Data to Predic...Databricks
Our team at Comcast is challenged with operationalizing predictive ML models to improve customer experience. Our goal is to eliminate bottlenecks in the process from model inception to deployment and monitoring.
Traditionally CI/CD manages code and infrastructure artifacts like container definitions. We want to extend it to support granular traceability enabling tracking of ML Models from use-case, to feature/attribute selection, development of versioned datasets, model training code, model evaluation artifacts, model prediction deployment containers, and sinks to which the predictions/outcomes are persisted to. Our framework stack enables us to track models from use-case to deployments, manage and evaluate multiple models simultaneously in the live yet dark mode and continue to monitor models in production against real-world outcomes using configurable policies.
The technologies/components which drive this vision are:
1. FeatureStore – Enables data scientists to reuse versioned features and review feature metrics by models. Self-Service capabilities allow all teams to onboard their events data into the feature store.
2. ModelRepository – Manages meta-data about models including pre-processing parameters (Ex. Scaling parameters for features), mapping to the features needed to execute the model, model discovery mechanisms, etc.
3. Spark on Alluxio – Alluxio provides the universal data plane on top of various under-stores (Ex. S3, HDFS, RDBMS). Apache Spark with its Data Sources API provides a unified query language which Data Scientist use to consume features to create training/validation/test datasets which are versioned and integrated into the full model pipeline using Ground-Context discussed next.
4. Ground-Context – This open-source vendor-neutral data context service enables full traceability from use-case, models, features, model to features mapping, versioned datasets, model training codebase, model deployment containers and prediction/outcome sinks. It integrates with the Feature-Store, Container Repository and Git to integrate data, code and run-time artifacts for CI/CD integration.
The document introduces the APIForce community and provides an agenda for an upcoming event. It introduces the APIForce team members and special guests. It then provides details on upcoming events and recognition for APIForce. The agenda outlines the schedule and topics to be covered at the event, including introductions, presentations on CloudHub 2.0, migrating to an API-led approach, empowering business, the Salesforce data cloud, and reimagining integration with APIs.
CA Spectrum® Just Keeps Getting Better and BetterCA Technologies
The CA Spectrum 10 release represented one of the most substantial releases for this solution in recent years. Join us for this session to explore some of the new features, such as the new web client for operators, software-defined network (SDN) support, wireless LAN controller and access point management, bi-directional integration with CA Unified Infrastructure Management, support for ModSecurity, and simplified reporting. This will be a combination of slides, demos and hands-on practice.
For more information, please visit http://cainc.to/Nv2VOe
OpenAIRE Content Providers Community Call, November 4th, 2020
This call was focused on the PROVIDE future developments, functionalities wishlist and PROVIDE service in EOSC.
Was also an opportunity to share the most recent updates and novelties in the OpenAIRE Content Provider Dashboard, and to get feedback from community.
Recordings: https://youtu.be/wY4fOS767Us
Follow the Community activities at https://www.openaire.eu/provide-community-calls
Industry 4.0: Merging Internet and FactoriesFabernovel
Industrial IoT and connected objects for factories are part of our research at FABERNOVEL OBJET, our activity dedicated to IoT.
The future of industry is at the crossroads of internet and factories. Some call it INDUSTRY 4.0 or FACTORY 4.0 in reference to the upcoming fourth industrial revolution. Governments and private companies in Germany, UK and the USA have acknowledged the importance of industrial IoT and its central role in future industrial transformation.
The adoption of Industrial Internet has both near-term and long-term impacts and will be characterized by the emergence of new models such as the “Outcome Economy” and the “Autonomous, Pull Economy”.
We believe that INDUSTRY 4.0 is a growth opportunity for industrial companies, and have decrypted this very phenomenon in the following presentation.
How to Build and Operate a Global Behavioral Change Platform (Neil Adamson, V...confluent
This talk will focus on the move from a monolithic solution to an event driven microservices architecture to allow each of our partners to offer their clients a customizable and localized Vitality offering that is based on a consistent global experience. It will include details on how we manage client demands and legal and regulatory issues specific to each market, how we can rapidly implement Vitality into a country within the space of a few months and then how we manage and support each market. It will describe wow we moved off proprietary technologies to a cloud agnostic open source, horizontally scalable hosted solution that takes advantage of technologies such as Kafka and Kubernetes etc. and the challenges faced in doing this.
In addition it will provide detail as to how Vitality streams exercise activity data real time from a multitude of device manufacturers (such as Fitbit, Garmin, Suunto, Apple) and routes it to the correct Vitality instance to analyze and allocate points to the member. Globally we process on average 50 -60 million member workouts a week. As well as how we integrate with a number of rewards partners to ensure members can access and utilize their rewards through local partners (gyms, airlines and cinemas) as well as global partners such as Starbucks, Hotels.com and Amazon.
Крауд маркетинг на практике ( Crowd marketing )Yuriy Titkov
Пример эффективности инструмента крауд маркетинга в SEO. Влияние упоминаний бренда, эффективность использования картинок с сайта в виде контента, упоминание и связка текстовых упоминаний домена. В связи с отказом поисковых систем от активных гиперссылок как элемента влияния на поисковую выдачу crowd marketing на сегодня является самым значимым инструментом.
Operationalizing Machine Learning—Managing Provenance from Raw Data to Predic...Databricks
Our team at Comcast is challenged with operationalizing predictive ML models to improve customer experience. Our goal is to eliminate bottlenecks in the process from model inception to deployment and monitoring.
Traditionally CI/CD manages code and infrastructure artifacts like container definitions. We want to extend it to support granular traceability enabling tracking of ML Models from use-case, to feature/attribute selection, development of versioned datasets, model training code, model evaluation artifacts, model prediction deployment containers, and sinks to which the predictions/outcomes are persisted to. Our framework stack enables us to track models from use-case to deployments, manage and evaluate multiple models simultaneously in the live yet dark mode and continue to monitor models in production against real-world outcomes using configurable policies.
The technologies/components which drive this vision are:
1. FeatureStore – Enables data scientists to reuse versioned features and review feature metrics by models. Self-Service capabilities allow all teams to onboard their events data into the feature store.
2. ModelRepository – Manages meta-data about models including pre-processing parameters (Ex. Scaling parameters for features), mapping to the features needed to execute the model, model discovery mechanisms, etc.
3. Spark on Alluxio – Alluxio provides the universal data plane on top of various under-stores (Ex. S3, HDFS, RDBMS). Apache Spark with its Data Sources API provides a unified query language which Data Scientist use to consume features to create training/validation/test datasets which are versioned and integrated into the full model pipeline using Ground-Context discussed next.
4. Ground-Context – This open-source vendor-neutral data context service enables full traceability from use-case, models, features, model to features mapping, versioned datasets, model training codebase, model deployment containers and prediction/outcome sinks. It integrates with the Feature-Store, Container Repository and Git to integrate data, code and run-time artifacts for CI/CD integration.
The document introduces the APIForce community and provides an agenda for an upcoming event. It introduces the APIForce team members and special guests. It then provides details on upcoming events and recognition for APIForce. The agenda outlines the schedule and topics to be covered at the event, including introductions, presentations on CloudHub 2.0, migrating to an API-led approach, empowering business, the Salesforce data cloud, and reimagining integration with APIs.
CA Spectrum® Just Keeps Getting Better and BetterCA Technologies
The CA Spectrum 10 release represented one of the most substantial releases for this solution in recent years. Join us for this session to explore some of the new features, such as the new web client for operators, software-defined network (SDN) support, wireless LAN controller and access point management, bi-directional integration with CA Unified Infrastructure Management, support for ModSecurity, and simplified reporting. This will be a combination of slides, demos and hands-on practice.
For more information, please visit http://cainc.to/Nv2VOe
OpenAIRE Content Providers Community Call, November 4th, 2020
This call was focused on the PROVIDE future developments, functionalities wishlist and PROVIDE service in EOSC.
Was also an opportunity to share the most recent updates and novelties in the OpenAIRE Content Provider Dashboard, and to get feedback from community.
Recordings: https://youtu.be/wY4fOS767Us
Follow the Community activities at https://www.openaire.eu/provide-community-calls
Industry 4.0: Merging Internet and FactoriesFabernovel
Industrial IoT and connected objects for factories are part of our research at FABERNOVEL OBJET, our activity dedicated to IoT.
The future of industry is at the crossroads of internet and factories. Some call it INDUSTRY 4.0 or FACTORY 4.0 in reference to the upcoming fourth industrial revolution. Governments and private companies in Germany, UK and the USA have acknowledged the importance of industrial IoT and its central role in future industrial transformation.
The adoption of Industrial Internet has both near-term and long-term impacts and will be characterized by the emergence of new models such as the “Outcome Economy” and the “Autonomous, Pull Economy”.
We believe that INDUSTRY 4.0 is a growth opportunity for industrial companies, and have decrypted this very phenomenon in the following presentation.
How to Build and Operate a Global Behavioral Change Platform (Neil Adamson, V...confluent
This talk will focus on the move from a monolithic solution to an event driven microservices architecture to allow each of our partners to offer their clients a customizable and localized Vitality offering that is based on a consistent global experience. It will include details on how we manage client demands and legal and regulatory issues specific to each market, how we can rapidly implement Vitality into a country within the space of a few months and then how we manage and support each market. It will describe wow we moved off proprietary technologies to a cloud agnostic open source, horizontally scalable hosted solution that takes advantage of technologies such as Kafka and Kubernetes etc. and the challenges faced in doing this.
In addition it will provide detail as to how Vitality streams exercise activity data real time from a multitude of device manufacturers (such as Fitbit, Garmin, Suunto, Apple) and routes it to the correct Vitality instance to analyze and allocate points to the member. Globally we process on average 50 -60 million member workouts a week. As well as how we integrate with a number of rewards partners to ensure members can access and utilize their rewards through local partners (gyms, airlines and cinemas) as well as global partners such as Starbucks, Hotels.com and Amazon.
1. Investment in automation and technology is replacing both American and Chinese workers, making them more efficient. One robot can replace thousands of human-hours for certain tasks.
2. Toyota invested $1.3 billion to overhaul its factory in Georgetown to build 11 vehicle types quickly through new technology like lasers that weld parts 4x faster.
3. Process choice, technology investment, and layout are critical factors that impact a firm's competitiveness through costs, delivery speed, flexibility, and ability to satisfy customer demands.
Confluent Steaming Webinar - Cape Town - Vitalityconfluent
The V1 Behavioral Change Platform provides the technical infrastructure to implement and manage global behavioral change programs at scale. It has powered programs in 9 markets since 2018 and continues expanding. The platform utilizes a centralized architecture with microservices and Kafka messaging to process high volumes of data securely and reliably across markets. It is configured to allow partners to customize programs while leveraging shared capabilities and accelerating delivery through its open APIs and federated development model.
Snehasish Roy who is a Test Automation Manager & Enterprise Architect at Amdocs along with Rekha Shetty who is a Program Manager at Amdocs and Umashankar Reddy who is a Software Test Manager at Amdocs took a Session on "AIML Driven extensive reusable Automation Asset Management Process (AIM) for "QA/QE/Testing Org"" at Global Testing Retreat #ATAGTR2019
Please refer our following post for session details:
https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-snehasish-roy-as-our-esteemed-speaker/
https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-rekha-shetty-as-our-esteemed-speaker/
https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-umashankar-reddy-as-our-esteemed-speaker/
This presentation deals with the Performance testing implemented in the Project.
We have to use Certain Tools in the POC process.
1) VSTS
2) JMeter
finally, VSTS has been Implemented.
I hope this presentation helps the viewer to get the overview of the tools which Accenture Deals.
This document discusses output-based contract management in an agile world. It covers metrics like productivity rates, cost effectiveness, and product quality that are core for any agile project. The document presents Glencore Agriculture's experience with a modular rebuild of its trading and traffic system, noting that output-based working brings a different partnership approach and metrics support problem resolution. It also provides Cognizant's profile as a global professional services company focused on digital transformation.
Dynamic professional having more than 5.9years of rich experience in Objective-C/COCOA/XCODE/MAC OS development.
Note:
1. I am holding H1B and I need sponsorship to transfer H1B.
2. I have NOT worked on any iOS development.
3. I am only looking for full time opportunity.
This document provides examples of the author's experience with innovation and commercialization from his career working for Honeywell, Cooper, and Eaton. It summarizes several projects he led including: (1) Developing hall effect sensors to replace ignition points in cars, growing that business to $100M+; (2) Developing sensors for intelligent dishwashers that won an award and led to other appliance sensor products; and (3) Commercializing VCSEL technology from research labs for fiber optic networking. It also outlines other projects renovating switchgear platforms and expanding engineered-to-order transformer designs and manufacturing.
This document discusses strategies for testing microservices. It begins with an overview of manual vs automated testing and challenges of testing software. The goal of testing is to develop, test, and deploy without breaking the system. Unit testing is good for complex logic but not enough to ensure functionality. Integration testing tests interfaces and is fast enough on modern computers. Microservices require loose coupling and independent deployability. The document illustrates integration testing between services and discusses issues with mocks. It proposes using consumer driven contracts and API documentation to test between services. Other strategies discussed include canary deployments with service meshes and message brokers to test at scale with real data.
Utsha Guha has over 5 years of experience developing Mac OS applications using Objective-C, Cocoa, and Swift. They have experience building applications from scratch as well as migrating existing applications to new frameworks. Notable projects include migrating several applications to the NUCore framework and developing the Data Browser 2.0 application which integrates with Microsoft Excel using VBA. Utsha Guha leads development teams and is proficient in the full software development lifecycle.
Microservices and Deployment MethodologiesYash Gupta
Presented by Successive Technologies at Nasscom, this presentation explores why Microservice Architecture is preferred over monolithic apps and how to deploy and manage microservices.
This document discusses Service Oriented Architecture (SOA) and how SOA testing differs from traditional testing. It provides examples of how SOA helps businesses scale by breaking processes into reusable services. Governance is important for SOA success to ensure services are reused rather than duplicated. SOA testing also differs in that it tests an architecture with dynamic business requirements and external services.
CloudIndustries.eu - industrial monitoring for hardware, renewables power pla...Evaldas Paliliūnas
Aedilis, UAB is an energy infrastructure company established in 2004 in Lithuania. It provides engineering services for high voltage substations and renewable power plants from 0.4kV to 400kV. The company has 65 employees and its R&D group, ELSETA, developed CloudIndustries.eu, an IoT platform for monitoring renewable energy plants and other industrial facilities. CloudIndustries.eu provides customizable dashboards and reports to visualize data from sensors in real-time. It also offers ODM services to develop customized monitoring solutions for other companies' hardware products.
Monitor everything from physical hardware to application functionalityNicolas Seyvet
The IT industry is a diverse and dynamic world where applications and functions may be spread out - and move between a multitude of providers and technologies such as Amazon AWS, Rackspace, KVM, volatile containers, and your internal traditional IT infrastructure with physical servers.
Monitoring all of these might require one monitoring tool per platform, or at least a few to seamlessly blend metrics, events and logs to get true Observability on your environment. OP5's intention is to address this with Project Omega. Designed from the ground up using cloud-native technologies packaged in a container environment to be running on premise or as SaaS, scaling horizontally with Kubernetes.
Initially the focus is on monitoring OpenStack with the Monasca project and developing the agent in and for the community providing patches and reviews since the Queens release of OpenStack, using modern REST API’s, time series database for metrics, message queues using Kafka and preparing the stack to for real-time analysis using Apache Storm.
agile architecture - two hour presentation - two worked examplesMark Collins-Cope
The document describes an architectural reference model (ARM) for medium to large scale applications that need to undergo frequent changes. The ARM aims to ease refactoring under changing requirements by improving code factoring, separation of concerns, stability, and testability. It divides an application into layers including the interface layer, application layer, domain layer, infrastructure layer, and platform layer. The application layer provides a service-oriented architecture and acts as a coordinator. The domain layer represents domain abstractions and business logic. Examples are provided of how the ARM was implemented for banking and video rental applications.
The document summarizes the author's achievements affecting production, processes, and industrial engineering tasks. Some key achievements include introducing a linear laser pointer to improve assembly line process time, participating in lean projects that increased efficiency rates, creating capacity monitoring applications and sheets to track production line utilization, and introducing a program called "Timer Pro" to automate industrial engineering tasks. The author also cites achievements in creating time catalogs for quoting, gathering data more quickly and accurately, and improving forecasting and efficiency tracking.
Flink SQL & TableAPI in Large Scale Production at AlibabaDataWorks Summit
Search and recommendation system for Alibaba’s e-commerce platform use batch and streaming processing heavily. Flink SQL and Table API (which is a SQL-like DSL) provide simple, flexible, and powerful language to express the data processing logic. More importantly, it opens the door to unify the semantics of batch and streaming jobs.
Blink is a project at Alibaba which improves Apache Flink to make it ready for large scale production use. To support our products, we made lots of improvements to Flink SQL & TableAPI in Alibaba's Blink project. We added the support for User-Defined Table function (UDTF), User-Defined Aggregates (UDAGG), Window Aggregate, and retraction, etc. We are actively working with the Flink community to contribute these improvements back. In this talk, we will present the rationale, semantics, design and implementation of these improvements. We will also share the experience of running large scale Flink SQL and TableAPI jobs at Alibaba.
Maximo and a roadmap for your IoT journeyHelen Fisher
For IBM customers, the Internet of Things (IoT) enables businesses to improve operations, rapidly connect devices and to lower costs. This is why IBM Maximo Asset Management now sits neatly in the Watson IoT portfolio. There are many business cases out there today for linking IoT and Maximo, IBM are not, however, diverting from their core value statements. Maximo is still about understanding asset availability, preventing failures, maximising resources, increasing reliability, understanding inventory needs and costs, and plant safety. Check out the key investment areas for 2016 and beyond.
[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialitéAT Internet
Cela fait maintenant deux ans que le RGPD est entré en vigueur ; aujourd’hui, il est plus important que jamais d’adopter une approche 100 % confidentialité du digital analytics. Voici un petit bilan chiffré des retombées positives de cette démarche et des risques posés par la non-conformité. N’attendez plus : exploitez tout le potentiel de la confidentialité des données !
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...AT Internet
Presented during the Digital Analytics Forum 2019, Etienne GAUTHERON, Director, Product & Operations at Reeport explains the importance of a powerful reporting when launching media campaigns. Watch it to know more about the 5 lessons they have learned from the analysis of 8,000 dashboards, as well as the relevance of the AT Connect Reeport connector.
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Similar to [Webinar] Multiply your efficiency with Data Query
1. Investment in automation and technology is replacing both American and Chinese workers, making them more efficient. One robot can replace thousands of human-hours for certain tasks.
2. Toyota invested $1.3 billion to overhaul its factory in Georgetown to build 11 vehicle types quickly through new technology like lasers that weld parts 4x faster.
3. Process choice, technology investment, and layout are critical factors that impact a firm's competitiveness through costs, delivery speed, flexibility, and ability to satisfy customer demands.
Confluent Steaming Webinar - Cape Town - Vitalityconfluent
The V1 Behavioral Change Platform provides the technical infrastructure to implement and manage global behavioral change programs at scale. It has powered programs in 9 markets since 2018 and continues expanding. The platform utilizes a centralized architecture with microservices and Kafka messaging to process high volumes of data securely and reliably across markets. It is configured to allow partners to customize programs while leveraging shared capabilities and accelerating delivery through its open APIs and federated development model.
Snehasish Roy who is a Test Automation Manager & Enterprise Architect at Amdocs along with Rekha Shetty who is a Program Manager at Amdocs and Umashankar Reddy who is a Software Test Manager at Amdocs took a Session on "AIML Driven extensive reusable Automation Asset Management Process (AIM) for "QA/QE/Testing Org"" at Global Testing Retreat #ATAGTR2019
Please refer our following post for session details:
https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-snehasish-roy-as-our-esteemed-speaker/
https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-rekha-shetty-as-our-esteemed-speaker/
https://atablogs.agiletestingalliance.org/2019/12/04/global-testing-retreat-atagtr2019-welcomes-umashankar-reddy-as-our-esteemed-speaker/
This presentation deals with the Performance testing implemented in the Project.
We have to use Certain Tools in the POC process.
1) VSTS
2) JMeter
finally, VSTS has been Implemented.
I hope this presentation helps the viewer to get the overview of the tools which Accenture Deals.
This document discusses output-based contract management in an agile world. It covers metrics like productivity rates, cost effectiveness, and product quality that are core for any agile project. The document presents Glencore Agriculture's experience with a modular rebuild of its trading and traffic system, noting that output-based working brings a different partnership approach and metrics support problem resolution. It also provides Cognizant's profile as a global professional services company focused on digital transformation.
Dynamic professional having more than 5.9years of rich experience in Objective-C/COCOA/XCODE/MAC OS development.
Note:
1. I am holding H1B and I need sponsorship to transfer H1B.
2. I have NOT worked on any iOS development.
3. I am only looking for full time opportunity.
This document provides examples of the author's experience with innovation and commercialization from his career working for Honeywell, Cooper, and Eaton. It summarizes several projects he led including: (1) Developing hall effect sensors to replace ignition points in cars, growing that business to $100M+; (2) Developing sensors for intelligent dishwashers that won an award and led to other appliance sensor products; and (3) Commercializing VCSEL technology from research labs for fiber optic networking. It also outlines other projects renovating switchgear platforms and expanding engineered-to-order transformer designs and manufacturing.
This document discusses strategies for testing microservices. It begins with an overview of manual vs automated testing and challenges of testing software. The goal of testing is to develop, test, and deploy without breaking the system. Unit testing is good for complex logic but not enough to ensure functionality. Integration testing tests interfaces and is fast enough on modern computers. Microservices require loose coupling and independent deployability. The document illustrates integration testing between services and discusses issues with mocks. It proposes using consumer driven contracts and API documentation to test between services. Other strategies discussed include canary deployments with service meshes and message brokers to test at scale with real data.
Utsha Guha has over 5 years of experience developing Mac OS applications using Objective-C, Cocoa, and Swift. They have experience building applications from scratch as well as migrating existing applications to new frameworks. Notable projects include migrating several applications to the NUCore framework and developing the Data Browser 2.0 application which integrates with Microsoft Excel using VBA. Utsha Guha leads development teams and is proficient in the full software development lifecycle.
Microservices and Deployment MethodologiesYash Gupta
Presented by Successive Technologies at Nasscom, this presentation explores why Microservice Architecture is preferred over monolithic apps and how to deploy and manage microservices.
This document discusses Service Oriented Architecture (SOA) and how SOA testing differs from traditional testing. It provides examples of how SOA helps businesses scale by breaking processes into reusable services. Governance is important for SOA success to ensure services are reused rather than duplicated. SOA testing also differs in that it tests an architecture with dynamic business requirements and external services.
CloudIndustries.eu - industrial monitoring for hardware, renewables power pla...Evaldas Paliliūnas
Aedilis, UAB is an energy infrastructure company established in 2004 in Lithuania. It provides engineering services for high voltage substations and renewable power plants from 0.4kV to 400kV. The company has 65 employees and its R&D group, ELSETA, developed CloudIndustries.eu, an IoT platform for monitoring renewable energy plants and other industrial facilities. CloudIndustries.eu provides customizable dashboards and reports to visualize data from sensors in real-time. It also offers ODM services to develop customized monitoring solutions for other companies' hardware products.
Monitor everything from physical hardware to application functionalityNicolas Seyvet
The IT industry is a diverse and dynamic world where applications and functions may be spread out - and move between a multitude of providers and technologies such as Amazon AWS, Rackspace, KVM, volatile containers, and your internal traditional IT infrastructure with physical servers.
Monitoring all of these might require one monitoring tool per platform, or at least a few to seamlessly blend metrics, events and logs to get true Observability on your environment. OP5's intention is to address this with Project Omega. Designed from the ground up using cloud-native technologies packaged in a container environment to be running on premise or as SaaS, scaling horizontally with Kubernetes.
Initially the focus is on monitoring OpenStack with the Monasca project and developing the agent in and for the community providing patches and reviews since the Queens release of OpenStack, using modern REST API’s, time series database for metrics, message queues using Kafka and preparing the stack to for real-time analysis using Apache Storm.
agile architecture - two hour presentation - two worked examplesMark Collins-Cope
The document describes an architectural reference model (ARM) for medium to large scale applications that need to undergo frequent changes. The ARM aims to ease refactoring under changing requirements by improving code factoring, separation of concerns, stability, and testability. It divides an application into layers including the interface layer, application layer, domain layer, infrastructure layer, and platform layer. The application layer provides a service-oriented architecture and acts as a coordinator. The domain layer represents domain abstractions and business logic. Examples are provided of how the ARM was implemented for banking and video rental applications.
The document summarizes the author's achievements affecting production, processes, and industrial engineering tasks. Some key achievements include introducing a linear laser pointer to improve assembly line process time, participating in lean projects that increased efficiency rates, creating capacity monitoring applications and sheets to track production line utilization, and introducing a program called "Timer Pro" to automate industrial engineering tasks. The author also cites achievements in creating time catalogs for quoting, gathering data more quickly and accurately, and improving forecasting and efficiency tracking.
Flink SQL & TableAPI in Large Scale Production at AlibabaDataWorks Summit
Search and recommendation system for Alibaba’s e-commerce platform use batch and streaming processing heavily. Flink SQL and Table API (which is a SQL-like DSL) provide simple, flexible, and powerful language to express the data processing logic. More importantly, it opens the door to unify the semantics of batch and streaming jobs.
Blink is a project at Alibaba which improves Apache Flink to make it ready for large scale production use. To support our products, we made lots of improvements to Flink SQL & TableAPI in Alibaba's Blink project. We added the support for User-Defined Table function (UDTF), User-Defined Aggregates (UDAGG), Window Aggregate, and retraction, etc. We are actively working with the Flink community to contribute these improvements back. In this talk, we will present the rationale, semantics, design and implementation of these improvements. We will also share the experience of running large scale Flink SQL and TableAPI jobs at Alibaba.
Maximo and a roadmap for your IoT journeyHelen Fisher
For IBM customers, the Internet of Things (IoT) enables businesses to improve operations, rapidly connect devices and to lower costs. This is why IBM Maximo Asset Management now sits neatly in the Watson IoT portfolio. There are many business cases out there today for linking IoT and Maximo, IBM are not, however, diverting from their core value statements. Maximo is still about understanding asset availability, preventing failures, maximising resources, increasing reliability, understanding inventory needs and costs, and plant safety. Check out the key investment areas for 2016 and beyond.
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[INFOGRAPHIE] Une stratégie digital analytics orientée confidentialitéAT Internet
Cela fait maintenant deux ans que le RGPD est entré en vigueur ; aujourd’hui, il est plus important que jamais d’adopter une approche 100 % confidentialité du digital analytics. Voici un petit bilan chiffré des retombées positives de cette démarche et des risques posés par la non-conformité. N’attendez plus : exploitez tout le potentiel de la confidentialité des données !
Reeport Partner presentation - Mixing site- and ad- centric data despite the ...AT Internet
Presented during the Digital Analytics Forum 2019, Etienne GAUTHERON, Director, Product & Operations at Reeport explains the importance of a powerful reporting when launching media campaigns. Watch it to know more about the 5 lessons they have learned from the analysis of 8,000 dashboards, as well as the relevance of the AT Connect Reeport connector.
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Bien que la data soit omniprésente vous n’en exploitez pas forcément tout le potentiel. Or, ceci peut devenir un sérieux problème si vos concurrents y parviennent et qu’ils portent le pilotage de leur activité à un niveau supérieur tout en instillant une culture Data Driven dans leur organisation.
Cet atelier sera l'occasion de découvrir :
• Comment des entreprises telles qu’Airbnb, Netflix ou Spotify définissent et diffusent des indicateurs actionnables.
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Ce webinar présentera les avantages du partenariat AT Internet / Mazeberry, et vous apportera des réponses concrètes :
- Comment choisir les leviers qui contribuent à maximiser vos conversions ?
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Thanks to unified data from AT Internet, and Mazeberry’s ROI-focused KPIs, BforBank now benefits from a clear understanding of its marketing mix. Business risks are minimised and the acquisition strategy optimised.
This webinar is the perfect opportunity to discover the many benefits of combining AT Internet and Mazeberry:
- How to choose marketing channels that are most effective in increasing your conversions?
- How to make impressions part of your customer journey analytical framework?
- How to combine both ROI-driven management of your marketing spen, and an innovative & expansionist strategy?
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[DAF 2017] Analytics Suite 2 - Insights for everyoneAT Internet
Présentation Analytics Suite 2 et roadmap du 9 novembre 2017 lors du Digital Analytics Forum à Paris. Partie 2 : Insights for everyone.
D’une donnée totalement fiable jusqu’aux insights pour tous, découvrez en avant-première l’Analytics Suite II et projetez-vous dans les prochaines innovations de la solution.
[DAF 2017] Analytics Suite 2 - Insights for everyone
[Webinar] Multiply your efficiency with Data Query
1. WEBINAR
INNOVATIONS 2014 / API V2 - DATA QUERY 2
July 2014
Mélanie CLAISSE - Product Manager
Clémence NOIZAT-TALLON - Product Manager
2. TABLE OF CONTENTS
I. Introduction
II. New features
III. Transition to the new version
IV. Q&A
3. I. INTRODUCTION
CONTEXT
- Databesity: a huge volume of data
- Difficulty to find the right information
at the right time
- To build easily, to automatize and
to custom reportings
- To share and to communicate data
4. I. INTRODUCTION
THE API 1 SUCCESS
- 80 000 000 calls in one year
(average)
- 2 600 users Data Query
- 15 000 templates Data Query
- ... and counting!
5. I. INTRODUCTION
WHY A NEW API?
- Faster and more powerful
- Flexibily
- Simplicity
- Evolution: 20 new features
- A new interface Data Query
6. I. INTRODUCTION
DATA QUERY 2
Exploring all the API new functionalities
Insuring an optimal user experience
A new environment, a new workspace
7. 1) NEW CALENDAR
II. NEW FEATURES
- A new calendar, entirely remade from the ergonomic point of view
- Relative periods
Pre-defined & custom periods, including real time
Simplification of calls on regular periods
Saving time: exports automation is easier
Example
- Multi-periods
8. 1) NEW CALENDAR
II. NEW FEATURES
Multi periods
Example: to identify the sales performances of 2 different periods
1st week of June & 1st week of July
Saturday June 14th, 21st and 28th
From the 1st to the 15th of June and July…
9. 1) NEW CALENDAR
II. NEW FEATURES
Product Orders
(May 25 - June 25)
Visits Orders
(June 25 – July 25)
Visits
TV 123 2 398 32 090 4 535 45 312
Tablet YX 1 873 24 350 2 435 25 644
Smartphone Z 2 109 18 724 4 356 24 320
Accessory ER 874 5 643 988 4 532
Multi periods
Example: to identify the sales performances of 2 different periods
10. 1) NEW CALENDAR
II. NEW FEATURES
Product Orders
(May 25 - June 25)
Visits Orders
(June 25 – July 25)
Visits
TV 123 2 398 32 090 4 535 45 312
Tablet YX 1 873 24 350 2 435 25 644
Smartphone Z 2 109 18 724 4 356 24 320
Accessory ER 874 5 643 988 4 532
With the API 1: 1 call per period and necessity to consolidate the products
Multi periods
Example: to identify the sales performances of 2 different periods
11. 1) NEW CALENDAR
II. NEW FEATURES
Product Orders
(May 25 - June 25)
Visits Orders
(June 25 – July 25)
Visits
TV 123 2 398 32 090 4 535 45 312
Tablet YX 1 873 24 350 2 435 25 644
Smartphone Z 2 109 18 724 4 356 24 320
Accessory ER 874 5 643 988 4 532
With the API 2: 1 call for all the table, product consolidation is already done
Multi periods
Example: to identify the sales performances of 2 different periods
12. 1) NEW CALENDAR
- Up to 3 periods
- P1 is the reference to the other ones
- Filters and sorts on P1
- 1 dimension per call (in progress)
- 100 lines per dataset
- 1 period can only contain once the same metric
II. NEW FEATURES
Multi periods
Functional scope of the multiple periods parameters in the API:
13. Example: to follow the evolution of sales per products during the running month
II. NEW FEATURES
Evolution of visits day by day on the period
Evolution hour by hour of the visits of the day, etc.
2) EVOLUTION
14. Functional scope of the EVO mode in the API:
- 1 dimension per call
- The EVO mode can’t be applied to multiple periods
- Maximum number of metrics in the dataset = 30
II. NEW FEATURES
2) EVOLUTION
15. Comparison between metrics over different dates
(variation in %, difference in value)
Example: to compare the sales per products between two periods.
II. NEW FEATURES
3) VARIATION AND DIFFERENCE
16. - You must select at least 2 metrics
- Evolution mode must be disactivated
Functional scope of the VAR / DIF mode in the API:
II. NEW FEATURES
3) VARIATION AND DIFFERENCE
17. Total “Displayed”: total of the metrics displayed in the dataset.
Total “Reference”: total of the reference site’s metrics (soon available).
Example: to get the weight of the 20 best sold products.
II. NEW FEATURES
4) TOTAL
18. Functional scope of the RATIO mode in the API:
- Need to activate the display of the Total
II. NEW FEATURES
5) RATIO
19. 6) META DATA
The Context parameter will transfer meta data information on the global call
scope.
Context for Space: label, currency, time zone, etc.
Context for Periods: period labels, list of dates
Context for Ranges: information linked to the result pagination (ex: Next URL)
Context for Profile: language, first day of week
II. NEW FEATURES
20. 7) FILTERS AND SORTS
II. NEW FEATURES
There are 9 new filters:
“Does not contain”
“Does not start with”
“Does not end by”
“Equals to value 1 or value 2”
“Is different from value 1 or value 2”
“Contains value 1 or value 2”
“Does not contain value 1 or value 2”
“Contains the word value 1 or value 2”
“Does not contain value 1 or value 2”
+ add of multi filter
Example: to isolate products whom title contains the words “Sony” OR “Samsung”
and which have generated a turnover >6,000.00€
21. 8) MAX RESULT
II. NEW FEATURES
With the Max result parameter, possibility to define the number of rows
required in the dataset.
By defaut Max result is 20.
Possibility to change the Max result parameter.
Value between 1 and 10 000.
If more than 10 000 is needed, necessity to make a pagination. Meta Data
are also made for this.
22. II. NEW FEATURES
9) OUTPUT FORMATTING
The output format has no longer to be written in the URL, it has to be
integrated in the header of API call.
Accept: application/json
Accept: application/xml
Accept: application/html
The default format is json
23. 10) FOCUS ON QUOTAS
Goal: to protect our infrastructures and provide an unified quality of service
to all users
- No limitation in number of API calls
- But a limitation of simultaneous calls
- 5 slots per user
- Possibility to have Premium accounts
II. NEW FEATURES
24. III. TRANSITION TO THE NEW VERSION
A NEW WORKSPACE, A NEW API
https://apirest.atinternet-solutions.com/data/v2/getData?querystring
25. III. TRANSITION TO THE NEW VERSION
THE TRANSITION
2 Data Query “ecosystems”:
V1 is still accessible from the DWS 2
V2 is accessible from the new workspace
Custom metrics and segments are the same on both apps
DQ v1 templates can be imported into DQ v2
Warning: once a template is imported in DQ v2, it will be closed in DQ v1.
The API v1 still continue to work