Activeeon is a key technology provider and actor in the cloud migration. Activeeon offers software and middleware solutions for Big Compute, workload automation and HPC. The company also provides workflows solutions for Machine Learning & IA.
Activeeon use cases for cloud, digital transformation, IoT and big data autom...Activeeon
Activeeon is a company founded in 2007 that provides cloud automation, workflow scheduling, and big data automation software. It addresses the $80 billion hybrid cloud market and has offices in France, the UK, US, and Senegal. The company's patented ProActive Solution automates workload scheduling, service lifecycles, and accelerates tasks like R, Spark, and Hadoop across various cloud and on-premise environments. ProActive Workflows improves return on investment by optimizing resource usage and prioritizing tasks. Activeeon has large enterprise customers and its technology helps companies accelerate calculations for risk management, banking, and other industries.
ActiveEon is a spin-off from INRIA, French Institute for Computer Science. The core technology of ActiveEon was initially developed by a team of about 40 developers and researchers, and has been heavily improved since by ActiveEon R&D team. ActiveEon is also a Docker member and was laureate of the IT Forum for Innovation prize in 2016.
ActiveEon has recently raised significant fund (1M euros) from local and foreign investors: PACA Investissement, BA06, Nestadio Capital, and Kima Ventures, Xavier Niel’s fund).
ActiveEon is now an Open Source ISV (Independent Software Vendor) providing innovative solutions for IT automation, acceleration and scalability, Big Data, Internet of Things, Distributed and parallel applications. ActiveEon offers ProActive, a software available in SaaS mode, both in the Cloud and on premises:
• ProActive Workflows & Scheduling: a complete workload scheduler that distributes and simplifies the execution of applications, featuring a workflow orchestrator and a resources manager.
• ProActive Parallel Scientific Toolbox: toolboxes that allow the distribution and the acceleration of Matlab, Scilab and R Language on Clusters, Grids or Clouds, also featuring data transfer and License cost optimization.
• ProActive Cloud Automation: automates the deployment and management of complex multi-VMs applications, manages heterogeneous and hybrid environments (multi-vendor private, public and hybrid clouds).
Infinite power at your fingertips with Microsoft Azure Cloud & ActiveEonActiveeon
Joint talk Microsoft-ActiveEon at Cloud Expo Europe, Big Data Analytics and Cloud management theater. Presenters: Christopher Plieger, Microsoft Azure Product Marketing Manager, and Denis Caromel , CEO - ActiveEon
Spark and Hadoop at Production Scale-(Anil Gadre, MapR)Spark Summit
This document discusses how MapR helps companies take Spark to production scale. It provides examples of companies using Spark and MapR for real-time security analytics, genomics research, and customer analytics. MapR offers high performance, reliability, and support to ensure Spark runs successfully in production environments. The document also announces new Spark-based quick start solutions from MapR and promotes free MapR training.
Regulatory Reporting of Asset Trading Using Apache Spark-(Sudipto Shankar Das...Spark Summit
1) Financial institutions must report asset trades within a short regulatory window based on changing rules.
2) A leading financial institution executes around 6 million trades daily and must report qualifying trades in a specified format within 15 minutes.
3) The institution transforms, enriches, de-duplicates trades and applies business rules to identify reportable trades for generation within 35 seconds for 30,000 records using Spark on a 10 node cluster.
Hadoop and Spark-Perfect Together-(Arun C. Murthy, Hortonworks)Spark Summit
Hadoop and Spark work well together to enable various types of data processing and analytics. Spark can run interactively on Hadoop's YARN for real-time and batch processing simultaneously. A use case example is a real-time application that processes sensor data from trucks on YARN and also builds a predictive model using Spark for features like anticipating driver violations. A data scientist can use a notebook like Zeppelin in the cloud to explore data, build the model, and integrate it into the real-time application running on YARN.
Successful AI/ML Projects with End-to-End Cloud Data EngineeringDatabricks
Trusted, high-quality data and efficient use of data engineers’ time are critical success factors for AI/ML projects. Enterprise data is complex—it comes from several sources, in a variety of formats, and at varied speeds. For your machine learning projects on Apache Spark, you need a holistic approach to data engineering: finding & discovering, ingesting & integrating, server-less processing at scale, and data governance. Stop by this session for an overview on how to set up AI/ML projects for success while Informatica takes the heavy lifting out of your data engineering.
Power Your Delta Lake with Streaming Transactional ChangesDatabricks
Organizations are adopting data digitization and data-driven decision making is at the heart of this transformation. Cloud Data Lakes and Datawarehouses provide great flexibility to proto-type and roll out applications continuously at much lower costs.
Transactional databases are optimized for processing huge volumes of transactions in real-time, whereas the cloud data lake needs to be optimized for analyzing huge volumes of data quickly. This brings about a challenge in creating a streamlined data flow process from capturing realtime transactions into a cloud datawarehouse to drive realtime insights in a scalable and cost effective manner.
In this session, we’ll show how organizations can easily overcome that challenge by adopting a robust platform with StreamSets and Delta Lake. StreamSets provides a no-code framework to automate ingestion of transactional data and data processing on Spark, while Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Activeeon use cases for cloud, digital transformation, IoT and big data autom...Activeeon
Activeeon is a company founded in 2007 that provides cloud automation, workflow scheduling, and big data automation software. It addresses the $80 billion hybrid cloud market and has offices in France, the UK, US, and Senegal. The company's patented ProActive Solution automates workload scheduling, service lifecycles, and accelerates tasks like R, Spark, and Hadoop across various cloud and on-premise environments. ProActive Workflows improves return on investment by optimizing resource usage and prioritizing tasks. Activeeon has large enterprise customers and its technology helps companies accelerate calculations for risk management, banking, and other industries.
ActiveEon is a spin-off from INRIA, French Institute for Computer Science. The core technology of ActiveEon was initially developed by a team of about 40 developers and researchers, and has been heavily improved since by ActiveEon R&D team. ActiveEon is also a Docker member and was laureate of the IT Forum for Innovation prize in 2016.
ActiveEon has recently raised significant fund (1M euros) from local and foreign investors: PACA Investissement, BA06, Nestadio Capital, and Kima Ventures, Xavier Niel’s fund).
ActiveEon is now an Open Source ISV (Independent Software Vendor) providing innovative solutions for IT automation, acceleration and scalability, Big Data, Internet of Things, Distributed and parallel applications. ActiveEon offers ProActive, a software available in SaaS mode, both in the Cloud and on premises:
• ProActive Workflows & Scheduling: a complete workload scheduler that distributes and simplifies the execution of applications, featuring a workflow orchestrator and a resources manager.
• ProActive Parallel Scientific Toolbox: toolboxes that allow the distribution and the acceleration of Matlab, Scilab and R Language on Clusters, Grids or Clouds, also featuring data transfer and License cost optimization.
• ProActive Cloud Automation: automates the deployment and management of complex multi-VMs applications, manages heterogeneous and hybrid environments (multi-vendor private, public and hybrid clouds).
Infinite power at your fingertips with Microsoft Azure Cloud & ActiveEonActiveeon
Joint talk Microsoft-ActiveEon at Cloud Expo Europe, Big Data Analytics and Cloud management theater. Presenters: Christopher Plieger, Microsoft Azure Product Marketing Manager, and Denis Caromel , CEO - ActiveEon
Spark and Hadoop at Production Scale-(Anil Gadre, MapR)Spark Summit
This document discusses how MapR helps companies take Spark to production scale. It provides examples of companies using Spark and MapR for real-time security analytics, genomics research, and customer analytics. MapR offers high performance, reliability, and support to ensure Spark runs successfully in production environments. The document also announces new Spark-based quick start solutions from MapR and promotes free MapR training.
Regulatory Reporting of Asset Trading Using Apache Spark-(Sudipto Shankar Das...Spark Summit
1) Financial institutions must report asset trades within a short regulatory window based on changing rules.
2) A leading financial institution executes around 6 million trades daily and must report qualifying trades in a specified format within 15 minutes.
3) The institution transforms, enriches, de-duplicates trades and applies business rules to identify reportable trades for generation within 35 seconds for 30,000 records using Spark on a 10 node cluster.
Hadoop and Spark-Perfect Together-(Arun C. Murthy, Hortonworks)Spark Summit
Hadoop and Spark work well together to enable various types of data processing and analytics. Spark can run interactively on Hadoop's YARN for real-time and batch processing simultaneously. A use case example is a real-time application that processes sensor data from trucks on YARN and also builds a predictive model using Spark for features like anticipating driver violations. A data scientist can use a notebook like Zeppelin in the cloud to explore data, build the model, and integrate it into the real-time application running on YARN.
Successful AI/ML Projects with End-to-End Cloud Data EngineeringDatabricks
Trusted, high-quality data and efficient use of data engineers’ time are critical success factors for AI/ML projects. Enterprise data is complex—it comes from several sources, in a variety of formats, and at varied speeds. For your machine learning projects on Apache Spark, you need a holistic approach to data engineering: finding & discovering, ingesting & integrating, server-less processing at scale, and data governance. Stop by this session for an overview on how to set up AI/ML projects for success while Informatica takes the heavy lifting out of your data engineering.
Power Your Delta Lake with Streaming Transactional ChangesDatabricks
Organizations are adopting data digitization and data-driven decision making is at the heart of this transformation. Cloud Data Lakes and Datawarehouses provide great flexibility to proto-type and roll out applications continuously at much lower costs.
Transactional databases are optimized for processing huge volumes of transactions in real-time, whereas the cloud data lake needs to be optimized for analyzing huge volumes of data quickly. This brings about a challenge in creating a streamlined data flow process from capturing realtime transactions into a cloud datawarehouse to drive realtime insights in a scalable and cost effective manner.
In this session, we’ll show how organizations can easily overcome that challenge by adopting a robust platform with StreamSets and Delta Lake. StreamSets provides a no-code framework to automate ingestion of transactional data and data processing on Spark, while Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
This is a brief technology introduction to Oracle Stream Analytics, and how to use the platform to develop streaming data pipelines that support a wide variety of industry use cases
1 Introduction to Microsoft data platform analytics for releaseJen Stirrup
Part 1 of a conference workshop. This forms the morning session, which looks at moving from Business Intelligence to Analytics.
Topics Covered: Azure Data Explorer, Azure Data Factory, Azure Synapse Analytics, Event Hubs, HDInsight, Big Data
Building custom coding integrations requires ongoing maintenance from specialized IT resources as application APIs are frequently updated. This ongoing maintenance workload can soon require a full-time team. Workato's pre-built integrations are maintained as part of the platform, avoiding the need for ongoing custom development and resources when applications or APIs change. Customizations and error reporting are also built into the Workato platform, rather than requiring additional development.
Reference architecture for Internet of ThingsSujee Maniyam
What kind of a data infrastructure is needed, to support Internet of Things?
This talk presents a reference architecture.
We are actually building this architecture as open source project. See here : bit.ly / iotxyz
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Consolidate your data marts for fast, flexible analytics 5.24.18Cloudera, Inc.
In this webinar, Cloudera and AtScale will showcase:
How a company can modernize their analytic architecture to deliver flexibility and agility to more end-users.
How using AtScale’s Universal Semantic layer can end the data chaos and allow business users to use the data in the modern platform.
Highlight the performance of AtScale and Cloudera’s analytic database with newly completed TPC-DS standard benchmarking.
Best practices for migrating from legacy appliances.
Machine Learning in the Enterprise 2019 Timothy Spann
Machine Learning in the Enterprise 2019. These are the slides for my upcoming demo on integrating Machine Learning and Streaming with Apache NiFi and Cloudera Data Science Workbench. This is for the February 12th, 2019 Future of Data Princeton meetup.
Spark Summit East 2015 Keynote -- Databricks CEO Ion StoicaDatabricks
This document discusses Databricks Cloud, a platform for running Apache Spark workloads that aims to accelerate time-to-results from months to days. It provides a unified platform with notebooks, dashboards, and jobs running on Spark clusters managed by Databricks. Key benefits include zero management of clusters, interactive queries and streaming for real-time insights, and the ability to develop models and visualizations in notebooks and deploy them as production jobs or dashboards without code changes. The platform is open source with no vendor lock-in and supports various data sources and third party applications. It is being used by over 3,500 organizations for applications like data preparation, analytics, and machine learning.
Cloudera Altus: Big Data in the Cloud Made EasyCloudera, Inc.
Cloudera Altus makes it easier for data engineers, ETL developers, and anyone who regularly works with raw data to process that data in the cloud efficiently and cost effectively. In this webinar we introduce our new platform-as-a-service offering and explore challenges associated with data processing in the cloud today, how Altus abstracts cluster overhead to deliver easy, efficient data processing, and unique features and benefits of Cloudera Altus.
Real-Time Analytics and Actions Across Large Data Sets with Apache SparkDatabricks
Around the world, businesses are turning to AI to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address the roadblock of moving from batch analytics to making real-time decisions by rapidly accessing and analyzing the relevant information amidst a sea of data. Yaron will explain how to make Spark handle multivariate real-time, historical and event data simultaneously to provide immediate and intelligent responses. He will present several time sensitive use-cases including fraud detection, prevention of outages and customer recommendations to demonstrate how to perform predictive analytics and real-time actions with Spark.
Speaker: Yaron Ekshtein
The Vision & Challenge of Applied Machine LearningCloudera, Inc.
Learn how Cloudera provides a unified platform that breaks down data silos commonly seen in organizations. By unifying the data needed for applied machine learning, organizations are better equipped to gather valuable insights from their data.
Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.
Preparing data for analysis and insights is the foundation of any data-driven exercise. Moving workloads to a PaaS, be it data engineering, analytic database, or data science requires a two step leap of faith - in trusting the public cloud, and then your PaaS vendor. In this webinar we will discuss the architecture of a PaaS solution for data management and understand the nitty gritty details of what exactly this involves with the following:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
3 things to learn:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
Glassbeam: Ad-hoc Analytics on Internet of Complex Things with Apache Cassand...DataStax Academy
This document discusses using Spark and Cassandra for ad hoc analytics on Internet of Complex Things (IoCT) data. It describes modeling data in Cassandra, limitations of ad hoc queries in Cassandra, and how the Spark Cassandra connector enables running ad hoc queries in Spark by treating Cassandra tables as DataFrames that can be queried using SQL. It also covers running Spark SQL queries on Cassandra data using the JDBC server.
Time Series Analysis Using an Event Streaming PlatformDr. Mirko Kämpf
Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats.
In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks.
The first case is relevant for anomaly detection and to protect safety.
Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research.
With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud.
Accelerate, Simplify, and Be Future-Ready with NetApp for SAPNetApp
NetApp solutions for SAP can accelerate performance, simplify operations, and improve flexibility for customers. NetApp's all-flash storage and ONTAP operating system allow customers to accelerate SAP workloads, create system copies in under 10 minutes, and use disaster recovery sites for testing purposes efficiently. NetApp has 18 years of experience co-innovating with SAP as a cloud-connected HANA supplier to help customers future proof their systems.
Data Science lifecycle with Apache Zeppelin and Spark by Moonsoo LeeSpark Summit
This document discusses Apache Zeppelin, an open-source notebook for interactive data analytics. It provides an overview of Zeppelin's features, including interactive notebooks, multiple backends, interpreters, and a display system. The document also covers Zeppelin's adoption timeline, from its origins as a commercial product in 2012 to becoming an Apache Incubator project in 2014. Future projects involving Zeppelin like Helium and Z-Manager are also briefly described.
Workload Automation for Cloud Migration and Machine Learning PlatformActiveeon
Activeeon has developed two Innovative Solutions based on workflows for:
1. Workload Automation for Cloud Migration
2. Data Science and Machine Learning Platform
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
This is a brief technology introduction to Oracle Stream Analytics, and how to use the platform to develop streaming data pipelines that support a wide variety of industry use cases
1 Introduction to Microsoft data platform analytics for releaseJen Stirrup
Part 1 of a conference workshop. This forms the morning session, which looks at moving from Business Intelligence to Analytics.
Topics Covered: Azure Data Explorer, Azure Data Factory, Azure Synapse Analytics, Event Hubs, HDInsight, Big Data
Building custom coding integrations requires ongoing maintenance from specialized IT resources as application APIs are frequently updated. This ongoing maintenance workload can soon require a full-time team. Workato's pre-built integrations are maintained as part of the platform, avoiding the need for ongoing custom development and resources when applications or APIs change. Customizations and error reporting are also built into the Workato platform, rather than requiring additional development.
Reference architecture for Internet of ThingsSujee Maniyam
What kind of a data infrastructure is needed, to support Internet of Things?
This talk presents a reference architecture.
We are actually building this architecture as open source project. See here : bit.ly / iotxyz
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Consolidate your data marts for fast, flexible analytics 5.24.18Cloudera, Inc.
In this webinar, Cloudera and AtScale will showcase:
How a company can modernize their analytic architecture to deliver flexibility and agility to more end-users.
How using AtScale’s Universal Semantic layer can end the data chaos and allow business users to use the data in the modern platform.
Highlight the performance of AtScale and Cloudera’s analytic database with newly completed TPC-DS standard benchmarking.
Best practices for migrating from legacy appliances.
Machine Learning in the Enterprise 2019 Timothy Spann
Machine Learning in the Enterprise 2019. These are the slides for my upcoming demo on integrating Machine Learning and Streaming with Apache NiFi and Cloudera Data Science Workbench. This is for the February 12th, 2019 Future of Data Princeton meetup.
Spark Summit East 2015 Keynote -- Databricks CEO Ion StoicaDatabricks
This document discusses Databricks Cloud, a platform for running Apache Spark workloads that aims to accelerate time-to-results from months to days. It provides a unified platform with notebooks, dashboards, and jobs running on Spark clusters managed by Databricks. Key benefits include zero management of clusters, interactive queries and streaming for real-time insights, and the ability to develop models and visualizations in notebooks and deploy them as production jobs or dashboards without code changes. The platform is open source with no vendor lock-in and supports various data sources and third party applications. It is being used by over 3,500 organizations for applications like data preparation, analytics, and machine learning.
Cloudera Altus: Big Data in the Cloud Made EasyCloudera, Inc.
Cloudera Altus makes it easier for data engineers, ETL developers, and anyone who regularly works with raw data to process that data in the cloud efficiently and cost effectively. In this webinar we introduce our new platform-as-a-service offering and explore challenges associated with data processing in the cloud today, how Altus abstracts cluster overhead to deliver easy, efficient data processing, and unique features and benefits of Cloudera Altus.
Real-Time Analytics and Actions Across Large Data Sets with Apache SparkDatabricks
Around the world, businesses are turning to AI to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address the roadblock of moving from batch analytics to making real-time decisions by rapidly accessing and analyzing the relevant information amidst a sea of data. Yaron will explain how to make Spark handle multivariate real-time, historical and event data simultaneously to provide immediate and intelligent responses. He will present several time sensitive use-cases including fraud detection, prevention of outages and customer recommendations to demonstrate how to perform predictive analytics and real-time actions with Spark.
Speaker: Yaron Ekshtein
The Vision & Challenge of Applied Machine LearningCloudera, Inc.
Learn how Cloudera provides a unified platform that breaks down data silos commonly seen in organizations. By unifying the data needed for applied machine learning, organizations are better equipped to gather valuable insights from their data.
Topics including: The transformative value of real-time data and analytics, and current barriers to adoption. The importance of an end-to-end solution for data-in-motion that includes ingestion, processing, and serving. Apache Kudu’s role in simplifying real-time architectures.
Preparing data for analysis and insights is the foundation of any data-driven exercise. Moving workloads to a PaaS, be it data engineering, analytic database, or data science requires a two step leap of faith - in trusting the public cloud, and then your PaaS vendor. In this webinar we will discuss the architecture of a PaaS solution for data management and understand the nitty gritty details of what exactly this involves with the following:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
3 things to learn:
An exploration of the architecture of Cloudera Altus PaaS - the industry’s first multi-function, multi-cloud data and analytic platform-as-a-service
A dive into use cases and a demo of Altus
The synergy between AWS and Altus to help you securely standardize on a combination of public cloud and data management
Glassbeam: Ad-hoc Analytics on Internet of Complex Things with Apache Cassand...DataStax Academy
This document discusses using Spark and Cassandra for ad hoc analytics on Internet of Complex Things (IoCT) data. It describes modeling data in Cassandra, limitations of ad hoc queries in Cassandra, and how the Spark Cassandra connector enables running ad hoc queries in Spark by treating Cassandra tables as DataFrames that can be queried using SQL. It also covers running Spark SQL queries on Cassandra data using the JDBC server.
Time Series Analysis Using an Event Streaming PlatformDr. Mirko Kämpf
Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats.
In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks.
The first case is relevant for anomaly detection and to protect safety.
Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research.
With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud.
Accelerate, Simplify, and Be Future-Ready with NetApp for SAPNetApp
NetApp solutions for SAP can accelerate performance, simplify operations, and improve flexibility for customers. NetApp's all-flash storage and ONTAP operating system allow customers to accelerate SAP workloads, create system copies in under 10 minutes, and use disaster recovery sites for testing purposes efficiently. NetApp has 18 years of experience co-innovating with SAP as a cloud-connected HANA supplier to help customers future proof their systems.
Data Science lifecycle with Apache Zeppelin and Spark by Moonsoo LeeSpark Summit
This document discusses Apache Zeppelin, an open-source notebook for interactive data analytics. It provides an overview of Zeppelin's features, including interactive notebooks, multiple backends, interpreters, and a display system. The document also covers Zeppelin's adoption timeline, from its origins as a commercial product in 2012 to becoming an Apache Incubator project in 2014. Future projects involving Zeppelin like Helium and Z-Manager are also briefly described.
Workload Automation for Cloud Migration and Machine Learning PlatformActiveeon
Activeeon has developed two Innovative Solutions based on workflows for:
1. Workload Automation for Cloud Migration
2. Data Science and Machine Learning Platform
Red hat's updates on the cloud & infrastructure strategyOrgad Kimchi
Red Hat presented its cloud and infrastructure strategy, focusing on Red Hat Cloud Suite which includes OpenStack for the software platform, OpenShift for DevOps and containers, and CloudForms for cloud management. OpenStack provides massive scalability for infrastructure and removes vendor lock-in. OpenShift enables developers and operations to build, deploy, and manage containerized applications from development to production on any infrastructure including physical, virtual, private and public clouds. CloudForms allows for managing containers and OpenShift deployments across hybrid cloud environments.
Flexible and Scalable Integration in the Automation Industry/Industrial IoTconfluent
Speaker: Kai Waehner, Technology Evangelist, Confluent
Kafka-Native, End-to-End IIoT Data Integration and Processing with Kafka Connect, KSQL, and PLC4X
IIoT / Industry 4.0 with Apache Kafka, Connect, KSQL, Apache PLC4X Kai Wähner
Data integration and processing is a huge challenge in Industrial IoT (IIoT, aka Industry 4.0 or Automation Industry) due to monolithic systems and proprietary protocols. Apache Kafka, its ecosystem (Kafka Connect, KSQL) and Apache PLC4X are a great open source choice to implement this integration end to end in a scalable, reliable and flexible way.
This blog post covers a high level overview about the challenges and a good, flexible architecture. At the end, I share a video recording and the corresponding slide deck. These provide many more details and insights.
Apache Kafka is the De-facto Standard for Real-Time Event Streaming. It provides
Open Source (Apache 2.0 License)
Global-scale
Real-time
Persistent Storage
Stream Processing
PCL4X allows vertical integration and to write software independent of PLCs using JDBC-like adapters for various protocols like Siemens S7, Modbus, Allen Bradley, Beckhoff ADS, OPC-UA, Emerson, Profinet, BACnet, Ethernet.
Github example: https://github.com/kaiwaehner/iiot-integration-apache-plc4x-kafka-connect-ksql-opc-ua-modbus-siemens-s7
More details: http://www.kai-waehner.de/blog/2019/09/02/iiot-data-integr…and-apache-plc4x/
Video Recording: https://youtu.be/RWKggid25ds
There is a huge amount of data out there and a great deal of power and insight that we can gain from it — if we can just bring it all into focus and make it more manageable. Many industrial organizations are accomplishing this by building sophisticated HMI, SCADA, and MES projects with the Ignition Perspective Module.
Miguel Angel Perdiguero - Head of BIG data & analytics Atos Iberia - semanain...COIICV
This document discusses Industry 4.0 and the digital transformation of industry. It describes key technological pillars like the Internet of Things, additive manufacturing, and data analytics. It provides examples of how these technologies can be applied through predictive maintenance and customized products. The document also introduces Atos Codex, an open industrial analytics platform that uses big data, high performance computing, and machine learning to deliver business insights and solutions.
Achhar Kalia has nearly 5 years of experience in application development, production support, and system integration. He has expertise in Linux/Unix administration, virtualization, databases, and networking. Some of his key skills include OpenStack, Oracle, DB2, networking protocols, and Ericsson products. He has worked on projects for clients such as Ericsson, Tata Consultancy Services, and Telstra involving the development, support and enhancement of various applications.
Living objects network performance_management_v2Yoan SMADJA
LivingObjects provides network management software solutions to telecommunications companies. It was originally developed for SFR, a major French telecom provider, and has since been commercialized as generic product. The software suite helps technicians optimize network performance and quality of service for fixed and mobile networks through data collection, processing, and visualization tools. LivingObjects has 35 employees and is headquartered in Toulouse, France.
The document discusses various technologies related to cloud computing and big data including Docker, Kubernetes, OpenStack, and Hitachi's Hyper Scale-Out Platform (HSP). It provides overviews of what each technology is used for, such as Docker for containerization, Kubernetes for container orchestration, OpenStack for building cloud infrastructure, and HSP for hyperconverged scale-out infrastructure. It also includes diagrams illustrating how these technologies can work together in an enterprise environment to provide solutions for areas like data lakes, analytics, and private clouds.
Software AG's Cumulocity IoT platform provides capabilities for device connectivity and management, integration and APIs, data and analytics, and application enablement. It allows customers to remotely manage assets, lower costs through predictive maintenance, and make better decisions using real-time data analytics. The platform supports distributing analytics from cloud to edge to on-premises systems.
Smartscale provides a software defined datacenter that manages shadow IT across public and private clouds. It creates a virtual datacenter and smooths differences between cloud providers, providing quality of service. Smartscale uses matrixed governance with multitenant hierarchy and users to manage compute, storage, networking and security. It also offers self-service configuration, intelligent automation, and analytics capabilities.
How to reinvent your organization in an iterative and pragmatic way? This is the result of using our digital toolbox. It allows you to transform your business model, expand your ecosystem by setting up your digital platform. This reinvention is also supported by the adaptation of your governance allowing you to innovate while guaranteeing the performance of your organization. For any information / suggestion / collaboration - william.poos@nrb.be
Comment réinventer votre organisation de manière itérative et pragmatique ? C'est le résultat de l'utilisation de notre boîte à outils digitale. Elle vous permet de transformer votre modèle métier, d'étendre votre écosystème en mettant en place votre plateforme digitale. Cette réinvention est également supportée par l'adaptation de votre gouvernance vous permettant d'innover tout en garantissant la performance de votre organisation. Pour toute information / suggestion / collaboration - william.poos@nrb.be
Infrastructure as Code in Large Scale OrganizationsXebiaLabs
The adoption of tools for the provisioning and automatic configuration of "Infrastructure as Code" (eg Terraform, Cloudformation or Ansible) reduces cost, time, errors, violations and risks when provisioning and configuring the necessary infrastructure so that our software can run .
However, those who have begun to make intensive use of this technology at the business level agree to identify the emergence of a very critical problem regarding the orchestration and governance needs of supply requests such as security, compliance, scalability, integrity and more.
Learn how The Digital.ai DevOps Platform (formerly XebiaLabs DevOps Platform) responds to all these problems and many more, allowing you to continue working with your favorite tools.
Industrial Internet of Things: Protocols an StandardsJavier Povedano
Presentation for the Distributed Systems Master at the University of Cordoba (Spain). In this presentation we review the state of the art in communication middlewares for Industrial Internet of Things
This document discusses predictive maintenance of robots in the automotive industry using big data analytics. It describes Cisco's Zero Downtime solution which analyzes telemetry data from robots to detect potential failures, saving customers over $40 million by preventing unplanned downtimes. The presentation outlines Cisco's cloud platform and a case study of how robot and plant data is collected and analyzed using streaming and batch processing to predict failures and schedule maintenance. It proposes a next generation predictive platform using machine learning to more accurately detect issues before downtime occurs.
Confluent & GSI Webinars series - Session 3confluent
An in depth look at how Confluent is being used in the financial services industry. Gain an understanding of how organisations are utilising data in motion to solve common problems and gain benefits from their real time data capabilities.
It will look more deeply into some specific use cases and show how Confluent technology is used to manage costs and mitigate risks.
This session is aimed at Solutions Architects, Sales Engineers and Pre Sales, and also the more technically minded business aligned people. Whilst this is not a deeply technical session, a level of knowledge around Kafka would be helpful.
Similar to Activeeon technology for Big Compute and cloud migration (20)
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
What is Master Data Management by PiLog Groupaymanquadri279
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
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!
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
SMS API Integration in Saudi Arabia| Best SMS API ServiceYara Milbes
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
Automated software refactoring with OpenRewrite and Generative AI.pptx.pdf
Activeeon technology for Big Compute and cloud migration
1. Paris, Sophia Antipolis, London, San Jose USA
A Key Technology Provider and Actor
in the Cloud Migration
within all Big Compute verticals and at the heart of IA/Machine Learning
revolution
2. AE Mission
Build and Deliver to the Market the Best
Workflows/Orchestrator Suite for
Automation & Big Compute* in the Cloud
At the core of the Revolution:
Big Data – AI / Machine Learning – IoT – Workload Automation – Cloud Migration
* Big Compute: Business applications in need of high number of Cloud VMs/Containers
3. AE Vision
Workflows
For Application People,
Data Scientists, ML + IA Experts
Scheduling & Meta-Scheduling on all Infra
For Legacy, Cloud Migration & Hybrid
Resource Management
For Hybrid and Effectiveness on the Cloud
4. Express Business Needs with Granular
Workflows
• Distributed & Parallel Computing
• On-premises & Cloud Architectures
A 30+ PhD/Engineer team with focused fields of high expertise:
• Big Data
• IA, ML
• IoT
• Finance
• Gov.
• HPC
• ...
• Data Science, Machine Learning, IA, Matlab, R
• Big Compute and HPC
Visdom
ActiveEon Technology
R&D Investment: 250 M/Y at INRIA + 150 M/Y at AE = 400 Man/Year
5. Process Flow & Operation
Execute &
Monitor
Design
Schedule, Monitor &
Connect the
resources
IT department, Data
Scientist, Business Lines,
Activeeon Services IT department,
Business owner
Operational team
1
2
3
PROACTIVE
STUDIO
PROACTIVE
RESOURCES MANAGER
PROACTIVE SCHEDULER
PROACTIVE
AUTOMATION
PORTAL
6. Next Generation Scheduler/Orchestration
Scheduler and Orchestration
Priority
& Planning
Parallel
Executions
Error
Management
Multi Users
</>OpenRESTAPI
Resource Management and Monitoring
Slurm
SGE
PBS
LSF
Multi-
platform
Local
Machine
Network
Resource
Batch
Scheduler
Cloud
Processing and Automation Workflows
Any
language
Secured
Data
Transfers
Meta-
scheduler
ETL, ERP,
ELT, …
Full
integration
Translator
7. Customer Pains
Context: Digital Transformation, Cloud Migration, DevOps call for Automation
New Scalability Requirements (Big Data, ML, IoT ...)
Pains AE Pain Reliefs
Automation is complex and
time-consuming
Unnecessary VMs in the
Cloud are expensive
Currently using old products
with outdated architecture
Powerful Workflows to Automate and
Optimize
Optimal execution of Workload in the
Cloud
Modern Architecture:
• Micro Services
• REST APIs
• Web Interfaces, available
On-prems and in SaaS
20-50% of cloud resources are unused because of VM over-sizing or bad decommissioning strategies
8. 1. Technology & Product
Strong Product Line:
From Workload to Finance, IoT, Machine Learning/AI Automation
9. AE: The Technology Foundation
Patents:
• Method of locating mobile communicating objects within a communications network,
comprising the transmission of location identifiers by repeaters and server updates
https://www.google.com/patents/EP1652346A1?cl=en
• Asynchronous and automatic device and method for transmission of results between
communicating objects
https://www.google.com/patents/US20070147277
400
Man-Year
R&D Investment since 2005
250 M-Y from INRIA
150 M-Y from AE
110Publications
Articles published in International
Conferences and PhD Thesis
2
Patents
- Method of Locating Mobile Objects
within a Communications Network
- Asynchronous and automatic
communication of results
An Industry Thought
Leader
20
M €
Total € Injected in the Product
Development since the beginning
10. Core Product
Processing & Automation Workflows
Scheduling & Orchestration
+ Meta-Scheduling
Resource
Management
& Monitoring
Workflow
Studio
Job
Console
Resource
Manager
Highly Flexible and Scalable architecture:
Micro Services, REST APIs, On Premise or SaaS
11. Next Generation Scheduler/Orchestration
</>
APIApp-specific Interfaces Integrated Web Portals
ProActive Workflows
Big Data, Data Science, Third Party Software
Scheduler
Resource Manager
Fault Tolerance
Cloud bursting
Resource
agnostic
Micro-service
Etc.
Multi-Cloud Orchestration
Meta-scheduling
Resource AllocationWorkflow Automation
LSF
Clusters Scheduler Cloud Local Big
Compute for
Everyone!
14. Machine Learning Open Studio
Fully Compliant with Docker
Any Machine/Deep Learning
Libraries
Real Time Visualization
with Visdom, etc.
Catalog solution for Sharing &
Production (DevOps)
Scale with Parallel &
Distributed execution
LearningData Prediction
Visdom
Configuration & Pre-Defined Palettes for AI, ML, DML
17. Automation Dashboard
A complete High-Level Portal allowing Users to
Execute, Plan, Monitor Jobs & Deploy PaaS Services from a single central place
18. Job Planner: Schedule Recurring Jobs
DefineCalendars AssociateWorkflowstoCalendars VisualizeExecutionPlanning
Manage recurring Jobs
Forecast and check future
Executions
Control recurring jobs from one
endpoint
Schedule Exceptions through
Exclusion Calendars &
Inclusion Calendars
21. Job Planner
Workflow Trigger
Rest API
Event Based
Manual
Recurring Jobs, Exceptions, Planned Jobs Complex Event Processing
Dashboard, Studio & Scheduler InterfacesPOST call
Get
Post
Put
Delete
</>
22. - Activate the rule through the
Cloud Automation dashboard
- Provide the parameters
required for the condition of the
rule
- Manage the lifecycle of our rule
from the same dashboard
- If the condition is met, the rule
will:
trigger a notification, and/or
send an email,
report in a third-party portal,
launch an action through a
specific Workfow
Monitoring (File System, Host, DB, etc.)
23. 2. Some Typical Customer Cases:
Capabilities & Portfolio Revue
Large Worldwide International Companies
Early Adopters
Using ActiveEon for Critical Business Applications
Finance
IoT
Gov.
Manufacturing
Automotive
Aerospace
Nuclear
RedHat OpenShift
24. Some Major Customers
Telco & IT Bio Tech
& Health
FinanceEngineering Aeronautics Energy
& Space
Some Partners:
Media
Distribution
Government
IoTCosmetics
25. L&G a leading multinational finance and insurance company with headquarters in London
Situation
Comply with new European regulations: Solvency II, Basel III, etc.
Transform legacy system and embrace cloud computing
Solution
Activeeon ProActive and migration to the Cloud have enabled
faster and more reliable execution:
• Cloud bursting
• Error management
• Prioritization
Benefits
From 18 hours to 2 hours for priority reports
Agile development with an objective of 4,000 cores
$1.2m / year committed spent on Cloud
Finance
Time
64VMs,eachwith16vCPUs
26. Komatsu is a Japanese multinational corporation
It manufactures construction, mining, industrial and military equipment.
Situation
ActiveEon Orchestrates on Cloud execution over hot and cold storage for streaming and batch analytics
> 1,200 tasks executed per hour
Solution
Activeeon ProActive has enabled control over and scheduling over execution:
• Error Management – Notification, Automated Recovery
• Job Planner
• Distribution & Parallelization
Benefits
• Reliable execution to orchestrate multiple services and resources
• Provide consistent results and KPIs to end users and BI Tools
IoT
27. PEPs is the French platform that offers access to the products of the Sentinel satellites (S1A and S1B, S2A and S2B, S3A
and S3B) of the European Union Program for Earth observation and monitoring Copernicus
Situation
Make Sentinel data available to the greatest number and
encourage the development of applications using them (agriculture, maritime field...)
1 petabyte (1015 bytes) in 20 years and 7 petabytes in 2 years!
Solution
Proactive Solution provided by ActiveEon to execute on Azure in hybrid mode
allows enhancing PEPS data and making them available to API providers :
• Multi-Cloud Ecosystem Platform
• Remove complexity for Data Scientists
• Provide Cloud performance
Benefits
• Faster execution, Optimisation of On-Prem ressources & Clouds,
• Easier to use by end-users
Space & Image Processing
28. Home Hoffice is the UK Ministry of Interior. They are using ActiveEon for 2 critical
applications:
• Visa Delivery Process, and
• Big Data & Analytics platform for Crime Reduction (HODAC).
Situation
In need to integrate 25 different sources of Data in order to build a consolidated
Data Lake and analytics platform to be used for many Home Land security
applications.
Solution
ActiveEon used as the central Orchestrator to Schedule and Meta-Schedule all the
Big Data, ETL, Analytics, Machine Learnigs software appliance of the platform
(Hadoop, SAS, TIBCO Spotfire, Python, Anaconda, GreenPlum, ElasticSearch, …).
Benefits
• Central Orchestration Tool
• Workflow Expressiveness: universal & comprehensive
• Management of Security for highly sensitive environments
• Management of Resources for all appliances (SAS, TIBCO,… ).
« ActiveEon is the only solution capable
to Schedule any Big Data Analytics,
mono-threaded, multi-threaded, multi-
core, parallel and distributed »
Cap Gemini Lead Engineer for Home
Office
Gov.: UK Ministry of Interior
29. Digital transformation for manufacturing
BENEFITS
Reduce the distance between the virtual and the
manufacturing process
Take advantage of digitalization in the machine tool
field for intelligent manufacturing and more efficient
production
FEATURES
Cloud-based big data analytics during
machining
Optimization of machining parameters using
workflows
Process simulation and optimization tools
Physical measurements and monitoring
Virtual / real part model correction
Use of AI
TARGETED SECTORS
Manufacturing, automotive, aerospace
Cloud processing services in manufacturing
END USERS
30. Workflows for HPC multi-physics engineering
simulations in automotive and aerospace
BENEFITS
Thermal resistance for engine partsFEATURES
Parallel evaluation of optimal mesh size for
the best tradeoff between execution time
and result accuracy
Complex workflow management: monitoring,
scheduling and orchestration
Infrastructure management: on-premises and
cloud HPC
Data collection and processing
END USERS
Pollution levels in a district
Workflow for exploration of tradeoff
between execution time and result accuracy
DOMAIN: COMPUTATIONAL FLUID DYNAMICS (CFD) AND POST-PROCESSING TOOLS
Acceleration and Automation of
Design Analysis and Optimizations
31. Deep Learning forAnomaly Detection in
Satellite Manufacturing
FEATURES
Detection of wires defect on a set of images
from production line using Deep Learning
Deep Learning on images of wires: occlusion,
variation, noise, grayscale, semantic analysis
Detection of defaults using a pre-defined wire
model and computing a distance measure
Workflows for model training and prediction for
parallel execution
BENEFITS
Automatic detection of defaults in hybrid
circuits manufacturing
Higher precision of Machine Learning results
Faster results with parallel execution of
machine learning workflows
Workflows can be used for other applications
Faulty wires come out in red
32. Big DataAnalysis forAutomatedAnomaly
Tracking in Satellite Communication
FEATURES
Data analysis: checking packets number of service
telemetries, order and type
Incident evolution forecasts
Big data workflows for automation of Test Scenarios
Automatic detection of remote controls that didn’t
receive expected telemetries
Data visualization in browser
BENEFITS
Automatic and early detection of defaults via trends
analysis of test results
Engineering process improvement: margin assessment,
robustness analysis, model elaboration based on actual
behaviors
Workflows allowing to accelerate treatments of fast-
growing test data amounts
Data fetching from many sources
ProActive workflow for service
telemetries verification
Visualisation of anomalies
33. Acceleration of Non-Destructive Evaluation (NDE)
for Nuclear Energy, Oil & Gas,Aerospace
FEATURES
NDE batch processing, parametric studies, non-
regression tests on multiple clusters
Transfer Input and Output data
Event programming to follow executions
Workflow process definition
Activeeon guidance and support
Cloud version: Execution on Microsoft Azure with
50 VMs/day per CIVA user 25K nodes/year
A potential of $1M$ Azure spending per Year
BENEFITS
Flexibility and enabler of interoperability between
heterogeneous infrastructures
Ability to run large POD (Probability of Detection)
computations, which were taking months on a
single computer
Large-scale simulations with Microsoft Azure cloud
Radiography – Pipes weld inspection
ABOUT CIVA NDE SOLUTION:
Multi-technique (Ultrasound, Eddy current,
Radiography) software platform developed by
the CEA LIST and its partners
The software is distributed by EXTENDE and
its distributors
Eddy current - Simulations
END USERS
Nuclear Energy, Oil & Gas, Aeronautics, Transportation
34. Orchestration of RedHat OpenShift On-Prem & OnAzure
Orchestrate & Manage all layers: IaaS, PaaS, SaaS.
Multi-Cloud, Hybrid, Scalable,
35. Platform for Cosmetic Formulation for 2000 persons around the world and
for Innovation Team. (Statistic, Machine Learning, Use of Language R)
2 000 persons
around the World
Innovation Team
(Statistics, ML, R)
Workflows OrchestrationMonitoring
Data
Compute
Data
Mining
Private
Network
+
HTTPS
36. Resource Manager
Scheduler Calendar
Sync
200 to 300 jobs
planned per week
72 000 patient diagnostics
delivered to nurses
Main Benefits
Job Visualization within Calendar
Edit job planning from both
interfaces
Visualize parallel tasks
Visualize task information in one
view
Usage of customer’s external database:
Oracle 11g Database
using Red Hat Hibernate ORM
(Object – Relational – Mapping)
Formerly part of
Task-Centric View Used
37. Scheduler
Passive
Mediametrie:
TV Audience
Measurement
Scheduler
Active
EC2 Spot Instances
Low costs
EC2 Instances
Regular costs
IaaS
On-Prem
Main Benefits
Deployed On Premise (Capex) or
on a Hosting Service (Opex)
Auto-scaling on infrastructure to
match capacity and demand
Huge costs optimization using only
the VMs needed and interruptible
low cost instances (e.g. EC2 Spot
instances)
38. CHALLENGES
Process 500 terabytes per year
Flexibility and enabler of interoperability
between heterogeneous services
Job affinity with data location
Transfer sensitive data to the cloud for
processing
RESULTS
Efficient metagenomics pipeline
Granular compute management
User friendly system for maximum utilization
Secure transfers
Simple workflow process definition
Workflow model and data management
Compute migration from on-prem to the cloud
MAIN DRIVER
REQUIREMENTS
Guidance and support to achieve high
performances
Fit in hybrid architecture multiplatform
Integration with R
FlexLM support (licenses manager)
Remote Visualization for interactive tasks
COMPANY PROFILE
Industry: BioTech
Product: Metagenomics
39. Quantitative Metagenomics Platform
for gene profiling and statistical analysis
Domain-specific
Users
Windows
Cluster 1
192 cores
Linux
Cluster 2
366 cores
Scheduler
Web Portal
Total
DNA
QC/Library preparation
SoLiD/Illumina
Sequencing
1TB /
Sequence
Analysis
40TB
Parallel DataBase
Pre, Post Processing of Data Analysis
Flexibility, Speed of Analysis
Granular execution
Fast
Architecture Overview
40. ProActive
Cloud Watch
Environment Environment
MachineLearninginITLogAnalysisforErrorDetection&PredictioninFinancialMarket
Analysis &
Classification
• Machine Learning
• Artificial Intelligence
• Probabilistic Analysis
Resources /
Applications /
Services
Resources /
Applications /
Services
Resources /
Applications /
Services
Business Users
11 1
1
2
3
Collect data from
any sources
Update model
Update event
driven system
Events
Monitoring
Complex Event
Processing
• Rule based
• Actions triggering
3
Alert
Predictive
Incident
Request for incident
analysis
2
Automated
Preventive
Action
Incidents
Incidents
Finance Domain: Deep ML for IT Infrastructure
Main Benefits
Openness and diversity of ML
frameworks to be used (vs.
Splunk)
Both Batch and Streaming
Workflow Expressiveness:
universal & comprehensive
IT Users
41. 3. Company / Team
400 man / year of R&D
2 patents
30 highly qualified engineers out of which 17 are PhD’s
References in all Industries in the US and EMEA
42. Global Locations
Partnerships
Key information
Management
Denis
Caromel,
CEO
François
Tournesac, CSO
Brian
Amedro,
CTO
Company
ISV Founded in 2007 by Denis Caromel in Sophia-Antipolis, Spin-off of INRIA
Addressing $80 Billion Hybrid Cloud Market with 27% CAGR
Disruptive Patented Technology w/ Exceptional Business Outcomes
60% of the revenue from international
Sophia-Antipolis (France)
Paris (France)
London (United Kingdom)
San-Jose (United States)
Montreal (Canada)
Fribourg (Switzerland)
Dakar (Senegal)
ProActive Solution
Job Scheduling, Workload Automation
Orchestration & Meta-Scheduling
On-premises and on all clouds
Open Source
43. 2005 An R&D Team of 45 persons headed by Denis Caromel developing a
Core Kernel for Distributed, Parallel & Cloud at INRIA (largest EU Computer
Science Research Institute, 6 000 persons).
Foundation of ActiveEon
Co-development between INRIA Team & ActiveEon
IP Technology Transfer from INRIA to ActiveEon
2007
2009 Scheduler added to the Core
2011 Resource Manager added
2013
2014
2016
2017
Orchestration with Powerful Workflows added
First very large customer references in Production
International Expansions in UK, USA, Africa
ActiveEon Story
R&D Investment
250 M/Y at INRIA + 150 M/Y at AE = 400 Man/Year
44. Paris, Sophia Antipolis, London, San Jose USA @activeeon
contact@activeeon.com
+33 988 777 660
Automate Accelerate & Scale
10K Nodes, 20K Tasks, 1M Jobs