This document provides an overview of Oracle Coherence, an in-memory data grid. It discusses what a data grid is and how Coherence works, including clustering, caching, querying, and aggregating data. It also provides examples of how Coherence can be used and customer use cases, such as for user session management across brands.
Coherence Overview - OFM Canberra July 2014Joelith
Slides from the July Oracle Middleware Forum held in Canberra, Australia. Provides an overview of Coherence. Check out our blog for more details: ofmcanberra.wordpress.com
Oracle Coherence Strategy and Roadmap (OpenWorld, September 2014)jeckels
The Oracle Coherence strategy and roadmap session from OpenWorld 2014. Includes details on the 12.1.3 Cloud Application Foundation release (including WebLogic integration), a road map for the 12.2.1 release, and notable features including JCache (JSR-107) support, Memcached adapters, federated caching, recoverable caching, security enhancements, multitenancy support and more. As usual, all items and statements contained herein are subject to change based on slide 3 of this presentation.
HTTP Session Replication with Oracle Coherence, GlassFish, WebLogicOracle
In this talk we will cover the integration of Coherence and Application Servers like Oracle WebLogic and Oracle GlassFish Server, and touch on the native capabilities of each server for HTTP session state management as well. The integration makes it simpler to access Coherence named caches through resource injection. It also provides an optimized integration of Coherence*Web for HTTP session state management. From a management perspective, it offers Coherence cluster configuration support through the WLS administration domain as well as Runtime monitoring support through the WebLogic console.
10 Tricks to Ensure Your Oracle Coherence Cluster is Not a "Black Box" in Pro...SL Corporation
Configuration of Oracle Coherence can be tricky. While Coherence provides highly valuable in-memory caching and parallel processing features, things don’t always go as planned, and changes can be extremely difficult to make once you’re in production. SL’s Founder and CTO, Tom Lubinski covers 10 things you can do to ensure your Coherence cluster is easy to support in production.
Coherence Overview - OFM Canberra July 2014Joelith
Slides from the July Oracle Middleware Forum held in Canberra, Australia. Provides an overview of Coherence. Check out our blog for more details: ofmcanberra.wordpress.com
Oracle Coherence Strategy and Roadmap (OpenWorld, September 2014)jeckels
The Oracle Coherence strategy and roadmap session from OpenWorld 2014. Includes details on the 12.1.3 Cloud Application Foundation release (including WebLogic integration), a road map for the 12.2.1 release, and notable features including JCache (JSR-107) support, Memcached adapters, federated caching, recoverable caching, security enhancements, multitenancy support and more. As usual, all items and statements contained herein are subject to change based on slide 3 of this presentation.
HTTP Session Replication with Oracle Coherence, GlassFish, WebLogicOracle
In this talk we will cover the integration of Coherence and Application Servers like Oracle WebLogic and Oracle GlassFish Server, and touch on the native capabilities of each server for HTTP session state management as well. The integration makes it simpler to access Coherence named caches through resource injection. It also provides an optimized integration of Coherence*Web for HTTP session state management. From a management perspective, it offers Coherence cluster configuration support through the WLS administration domain as well as Runtime monitoring support through the WebLogic console.
10 Tricks to Ensure Your Oracle Coherence Cluster is Not a "Black Box" in Pro...SL Corporation
Configuration of Oracle Coherence can be tricky. While Coherence provides highly valuable in-memory caching and parallel processing features, things don’t always go as planned, and changes can be extremely difficult to make once you’re in production. SL’s Founder and CTO, Tom Lubinski covers 10 things you can do to ensure your Coherence cluster is easy to support in production.
In this webinar
Hazelcast has pushed the In-Memory Data Grid category further by adding High-Density Caching and making great strides in performance – but what’s next? In this talk Hazelcast CEO Greg Luck will explain the direction of the Hazelcast platform in detail. He’ll share what’s planned for features of High-Density Caching as well for the In-Memory Computing platform at large in the areas of PaaS, IaaS, extensions and integrations along with a detailed list of features planned for 3.6.
We’ll cover these topics:
-High-level platform roadmap
-Detailed list of features planned for 3.6
-Live Q&A
Presenter: Greg Luck, CEO at Hazelcast
Greg has worked with Java for 15 years. He is spec lead of the recently completed JSR107: JCache and the founder of Ehcache. He is a JCP Executive Committee alumni. Prior to Hazelcast, Greg was CTO at Terracotta, Inc which was acquired by Software AG. He was also Chief Architect at Australian travel startup Wotif.com which went to IPO. Earlier roles include consultant at ThoughtWorks and KPMG, and CIO at Virgin Blue, Tempo Services, Stamford Hotels and Resorts, and Australian Resorts. Greg has a Master’s degree in Information Technology from QUT and a Bachelor of Commerce from the University of Queensland.
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr UnternehmenEDB
Dieses Webinar hilft Ihnen, die Unterschiede zwischen den verschiedenen Replikationsansätzen zu verstehen, die Anforderungen der jeweiligen Strategie zu erkennen und sich über die Möglichkeiten klar zu werden, was mit jeder einzelnen zu erreichen ist. Damit werden Sie hoffentlich eher in der Lage sein, herauszufinden, welche PostgreSQL-Replikationsarten Sie wirklich für Ihr System benötigen.
- Wie physische und logische Replikation in PostgreSQL funktionieren
- Unterschiede zwischen synchroner und asynchroner Replikation
- Vorteile, Nachteile und Herausforderungen bei der Multi-Master-Replikation
- Welche Replikationsstrategie für unterschiedliche Use-Cases besser geeignet ist
Referent:
Borys Neselovskyi, Regional Sales Engineer DACH, EDB
------------------------------------------------------------
For more #webinars, visit http://bit.ly/EDB-Webinars
Download free #PostgreSQL whitepapers: http://bit.ly/EDB-Whitepapers
Read our #Postgres Blog http://bit.ly/EDB-Blogs
Follow us on Facebook at http://bit.ly/EDB-FB
Follow us on Twitter at http://bit.ly/EDB-Twitter
Follow us on LinkedIn at http://bit.ly/EDB-LinkedIn
Reach us via email at marketing@enterprisedb.com
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
This presentation:
* covers basics of caching and popular cache types
* explains evolution from simple cache to distributed, and from distributed to IMDG
* not describes usage of NoSQL solutions for caching
* is not intended for products comparison or for promotion of Hazelcast as the best solution
Big-Data-as-a-Service (BDaaS) in an enterprise environment requires meeting the often contradictory goals of (1) providing your data scientists, analysts, and data engineers with a self-service consumption model; (2) delivering agile and scalable on-demand infrastructure for the rapidly evolving ecosystem of big data frameworks and application software; while (3) ensuring enterprise-grade capabilities for isolation, security, monitoring, etc.
In this presentation at our BDaaS meetup in Santa Clara, Tom Phelan (chief architect and co-founder of BlueData) reviewed these goals and how to resolve the potential contradictions. He also discussed the infrastructure, application, user experience, security, and maintainability considerations required before selecting (or designing and building) a Big-Data-as-a-Service platform for an enterprise big data deployment.
More info on this BDaaS meetup can be found at: http://www.meetup.com/Big-Data-as-a-Service/events/233999817
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
Cloud Migration Paths: Kubernetes, IaaS, or DBaaSEDB
Moving to the cloud is hard, and moving Postgres databases to the cloud is even harder. Public cloud or private cloud? Infrastructure as a Service (IaaS), or Platform as a Service (PaaS)? Kubernetes for the application, or for the database and the application? This talk will juxtapose self-managed Kubernetes and container-based database solutions, Postgres deployments on IaaS, and Postgres DBaaS solutions of which EDB’s DBaaS BigAnimal is the latest example.
Running secured Spark job in Kubernetes compute cluster and integrating with ...DataWorks Summit
This presentation will provide technical design and development insights to run a secured Spark job in Kubernetes compute cluster that accesses job data from a Kerberized HDFS cluster. Joy will show how to run a long-running machine learning or ETL Spark job in Kubernetes and to access data from HDFS using Kerberos Principal and Delegation token.
The first part of this presentation will unleash the design and best practices to deploy and run Spark in Kubernetes integrated with HDFS that creates on-demand multi-node Spark cluster during job submission, installing/resolving software dependencies (packages), executing/monitoring the workload, and finally disposing the resources at the end of job completion. The second part of this presentation covers the design and development details to setup a Spark+Kubernetes cluster that supports long-running jobs accessing data from secured HDFS storage by creating and renewing Kerberos delegation tokens seamlessly from end-user's Kerberos Principal.
All the techniques covered in this presentation are essential in order to set up a Spark+Kubernetes compute cluster that accesses data securely from distributed storage cluster such as HDFS in a corporate environment. No prior knowledge of any of these technologies is required to attend this presentation.
Speaker
Joy Chakraborty, Data Architect
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Deep Learning with DL4J on Apache Spark: Yeah it's Cool, but are You Doing it...DataWorks Summit
DeepLearning4J (DL4J) is a powerful Open Source distributed framework that brings Deep Learning to the JVM (it can serve as a DIY tool for Java, Scala, Clojure and Kotlin programmers). It can be used on distributed GPUs and CPUs. It is integrated with Hadoop and Apache Spark. ND4J is a Open Source, distributed and GPU-enabled library that brings the intuitive scientific computing tools of the Python community to the JVM. Training neural network models using DL4J, ND4J and Spark is a powerful combination, but the overall cluster configuration can present some unespected issues that can compromise performances and nullify the benefits of well written code and good model design. In this talk I will walk through some of those problems and will present some best practices to prevent them. The presented use cases will refer to DL4J and ND4J on different Spark deployment modes (standalone, YARN, Kubernetes). The reference programming language for any code example would be Scala, but no preliminary Scala knowledge is mandatory in order to better understanding the presented topics.
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...DataWorks Summit
Hadoop is becoming a standard platform for building critical financial applications such as risk reporting, trading and fraud detection. These applications require high level of SLAs (service-level agreement) in terms of RPO (Recovery Point Objective) and RTO (Recovery Time Objective). To achieve these SLAs, organizations need to build a disaster recovery plan that cover several layers ranging from the infrastructure to the clients going through the platform and the applications. In this talk, we will present the different architecture blueprints for disaster recovery as well as their corresponding SLA objectives. Then, we will focus on the stretch cluster solution that Crédit Agricole CIB is using in production. We will discuss the solution’s advantages, drawbacks and the impact of this approach on the global architecture. Finally, we will explain in detail how to configure and deploy this solution and how to integrate each layer (storage layer, processing layer...) into the architecture.
How to migrate AWS RDS Oracle DBs to OCI using OCI Backup Service. View how you can migrate your Oracle databases on AWS to OCI. View the recording at : https://asktom.oracle.com/pls/apex/asktom.search?oh=7575
Apache Hadoop YARN is the modern Distributed Operating System. It enables the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Multiple organizations are able to leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues etc.
In this talk, we’ll first hit the ground with the current status of Apache Hadoop YARN – how it is faring today in deployments large and small. We will cover different types of YARN deployments, in different environments and scale.
We'll then move on to the exciting present & future of YARN – features that are further strengthening YARN as the first-class resource-management platform for datacenters running enterprise Hadoop. We’ll discuss the current status as well as the future promise of features and initiatives like – 10x scheduler throughput improvements, docker containers support on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, fine-grained isolation for multi-tenancy using CGroups on disk & network resources, powerful scheduling features like application priorities, intra-queue preemption across applications and operational enhancements including insights through Timeline Service V2, a new web UI and better queue management.
We’re living in an era of digital disruption, where the accessibility and adoption of emerging digital technologies are enabling
enterprises to reimagine their businesses in exciting new ways. Data flows from the edge to the core to the cloud while
performing analytics and gaining actionable intelligence at all steps along the way. This connected, automated and data-driven
future enables organizations to rapidly acquire, analyze, and take action on real-time data as well as curate flows for additional
analysis at a later stage. New IoT use cases require enterprises to properly handle data in motion and create newer edge
applications with data flow management, stream processing and analytics while still being governed by existing enterprise
services.
This session highlights the importance of an edge-to-core-to-cloud digital infrastructure that can adapt to your flexing
business needs, capturing expanding data flows at the edge and aligning them to a core infrastructure that can drive insight.
Speakers
Bob Mumford, Hewlett Packard Enterprise, Big Data Solutions Architect
Manage Microservices & Fast Data Systems on One Platform w/ DC/OSMesosphere Inc.
The application landscape inside our data center is changing: Along with the trend of moving toward microservices and containers, there are a number of new distributed data processing frameworks such as Kafka or Cassandra being released on a weekly basis. These changes have implications for the ways we think about infrastructure. With the growing need for computing power and the rise of distributed applications comes the need for a reliable and simple-use cluster manager and programming abstraction.
In this presentation, Mesosphere explains how to use DC/OS to manage microservices and fast data systems on a single platform. We will look at how container orchestration, including resource management and service management, can be streamlined to process fast data in a matter of seconds, allowing for predictive user interfaces, product recommendations, and billing charge back, among other modern app components.
Distributed systems like Cassandra provide significant opportunity for fault tolerance, reliability and continuous uptime. Frequently, what it really takes to ensure this degree of uptime is greatly misunderstood, or under rated. Often, enterprises do not understand or prepare for failure until it happens, which clearly is too late.
Talk to cover the following topics:
-Why do I need to prepare for failure
-What should I be operationally testing
-Understanding failure scenarios
-Understanding time to recovery
-Capacity planning for failure
-Multi-Data Center considerations
-But it's distributed, why do I need to backup?
About the Speaker
Thomas Valley Solutions Engineer, DataStax
Thom Valley is a Solutions Engineer at DataStax Inc. Immediately after graduating with a degree in English and Theatre, Thom went to work for his first software company, installing systems and training users at beverage distribution companies nationwide. From there he moved on to more challenging roles in a range of industries including; Director of R&D for a web publishing startup, CTO of an e-commerce and fulfillment company and COO of an smart cities / IOT provider.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in big data ecosystem. Although, Hive started primarily as batch ingestion and reporting tool, community is hard at work in improving it along many different dimensions and use cases. This talk will provide an overview of latest and greatest features and optimizations which have landed in project over last year. Materialized view, micro managed tables and workload management are some noteworthy features.
I will deep dive into some optimizations which promise to provide major performance gains. Support for ACID tables has also improved considerably. Although some of these features and enhancements are not novel but have existed for years in other DB systems, implementing them on Hive poses some unique challenges and results in lessons which are generally applicable in many other contexts. I will also provide a glimpse of what is expected to come in near future.
Speaker: Ashutosh Chauhan, Engineering Manager, Hortonworks
In this webinar
Hazelcast has pushed the In-Memory Data Grid category further by adding High-Density Caching and making great strides in performance – but what’s next? In this talk Hazelcast CEO Greg Luck will explain the direction of the Hazelcast platform in detail. He’ll share what’s planned for features of High-Density Caching as well for the In-Memory Computing platform at large in the areas of PaaS, IaaS, extensions and integrations along with a detailed list of features planned for 3.6.
We’ll cover these topics:
-High-level platform roadmap
-Detailed list of features planned for 3.6
-Live Q&A
Presenter: Greg Luck, CEO at Hazelcast
Greg has worked with Java for 15 years. He is spec lead of the recently completed JSR107: JCache and the founder of Ehcache. He is a JCP Executive Committee alumni. Prior to Hazelcast, Greg was CTO at Terracotta, Inc which was acquired by Software AG. He was also Chief Architect at Australian travel startup Wotif.com which went to IPO. Earlier roles include consultant at ThoughtWorks and KPMG, and CIO at Virgin Blue, Tempo Services, Stamford Hotels and Resorts, and Australian Resorts. Greg has a Master’s degree in Information Technology from QUT and a Bachelor of Commerce from the University of Queensland.
Die 10 besten PostgreSQL-Replikationsstrategien für Ihr UnternehmenEDB
Dieses Webinar hilft Ihnen, die Unterschiede zwischen den verschiedenen Replikationsansätzen zu verstehen, die Anforderungen der jeweiligen Strategie zu erkennen und sich über die Möglichkeiten klar zu werden, was mit jeder einzelnen zu erreichen ist. Damit werden Sie hoffentlich eher in der Lage sein, herauszufinden, welche PostgreSQL-Replikationsarten Sie wirklich für Ihr System benötigen.
- Wie physische und logische Replikation in PostgreSQL funktionieren
- Unterschiede zwischen synchroner und asynchroner Replikation
- Vorteile, Nachteile und Herausforderungen bei der Multi-Master-Replikation
- Welche Replikationsstrategie für unterschiedliche Use-Cases besser geeignet ist
Referent:
Borys Neselovskyi, Regional Sales Engineer DACH, EDB
------------------------------------------------------------
For more #webinars, visit http://bit.ly/EDB-Webinars
Download free #PostgreSQL whitepapers: http://bit.ly/EDB-Whitepapers
Read our #Postgres Blog http://bit.ly/EDB-Blogs
Follow us on Facebook at http://bit.ly/EDB-FB
Follow us on Twitter at http://bit.ly/EDB-Twitter
Follow us on LinkedIn at http://bit.ly/EDB-LinkedIn
Reach us via email at marketing@enterprisedb.com
From cache to in-memory data grid. Introduction to Hazelcast.Taras Matyashovsky
This presentation:
* covers basics of caching and popular cache types
* explains evolution from simple cache to distributed, and from distributed to IMDG
* not describes usage of NoSQL solutions for caching
* is not intended for products comparison or for promotion of Hazelcast as the best solution
Big-Data-as-a-Service (BDaaS) in an enterprise environment requires meeting the often contradictory goals of (1) providing your data scientists, analysts, and data engineers with a self-service consumption model; (2) delivering agile and scalable on-demand infrastructure for the rapidly evolving ecosystem of big data frameworks and application software; while (3) ensuring enterprise-grade capabilities for isolation, security, monitoring, etc.
In this presentation at our BDaaS meetup in Santa Clara, Tom Phelan (chief architect and co-founder of BlueData) reviewed these goals and how to resolve the potential contradictions. He also discussed the infrastructure, application, user experience, security, and maintainability considerations required before selecting (or designing and building) a Big-Data-as-a-Service platform for an enterprise big data deployment.
More info on this BDaaS meetup can be found at: http://www.meetup.com/Big-Data-as-a-Service/events/233999817
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
Cloud Migration Paths: Kubernetes, IaaS, or DBaaSEDB
Moving to the cloud is hard, and moving Postgres databases to the cloud is even harder. Public cloud or private cloud? Infrastructure as a Service (IaaS), or Platform as a Service (PaaS)? Kubernetes for the application, or for the database and the application? This talk will juxtapose self-managed Kubernetes and container-based database solutions, Postgres deployments on IaaS, and Postgres DBaaS solutions of which EDB’s DBaaS BigAnimal is the latest example.
Running secured Spark job in Kubernetes compute cluster and integrating with ...DataWorks Summit
This presentation will provide technical design and development insights to run a secured Spark job in Kubernetes compute cluster that accesses job data from a Kerberized HDFS cluster. Joy will show how to run a long-running machine learning or ETL Spark job in Kubernetes and to access data from HDFS using Kerberos Principal and Delegation token.
The first part of this presentation will unleash the design and best practices to deploy and run Spark in Kubernetes integrated with HDFS that creates on-demand multi-node Spark cluster during job submission, installing/resolving software dependencies (packages), executing/monitoring the workload, and finally disposing the resources at the end of job completion. The second part of this presentation covers the design and development details to setup a Spark+Kubernetes cluster that supports long-running jobs accessing data from secured HDFS storage by creating and renewing Kerberos delegation tokens seamlessly from end-user's Kerberos Principal.
All the techniques covered in this presentation are essential in order to set up a Spark+Kubernetes compute cluster that accesses data securely from distributed storage cluster such as HDFS in a corporate environment. No prior knowledge of any of these technologies is required to attend this presentation.
Speaker
Joy Chakraborty, Data Architect
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Deep Learning with DL4J on Apache Spark: Yeah it's Cool, but are You Doing it...DataWorks Summit
DeepLearning4J (DL4J) is a powerful Open Source distributed framework that brings Deep Learning to the JVM (it can serve as a DIY tool for Java, Scala, Clojure and Kotlin programmers). It can be used on distributed GPUs and CPUs. It is integrated with Hadoop and Apache Spark. ND4J is a Open Source, distributed and GPU-enabled library that brings the intuitive scientific computing tools of the Python community to the JVM. Training neural network models using DL4J, ND4J and Spark is a powerful combination, but the overall cluster configuration can present some unespected issues that can compromise performances and nullify the benefits of well written code and good model design. In this talk I will walk through some of those problems and will present some best practices to prevent them. The presented use cases will refer to DL4J and ND4J on different Spark deployment modes (standalone, YARN, Kubernetes). The reference programming language for any code example would be Scala, but no preliminary Scala knowledge is mandatory in order to better understanding the presented topics.
Disaster Recovery Experience at CACIB: Hardening Hadoop for Critical Financia...DataWorks Summit
Hadoop is becoming a standard platform for building critical financial applications such as risk reporting, trading and fraud detection. These applications require high level of SLAs (service-level agreement) in terms of RPO (Recovery Point Objective) and RTO (Recovery Time Objective). To achieve these SLAs, organizations need to build a disaster recovery plan that cover several layers ranging from the infrastructure to the clients going through the platform and the applications. In this talk, we will present the different architecture blueprints for disaster recovery as well as their corresponding SLA objectives. Then, we will focus on the stretch cluster solution that Crédit Agricole CIB is using in production. We will discuss the solution’s advantages, drawbacks and the impact of this approach on the global architecture. Finally, we will explain in detail how to configure and deploy this solution and how to integrate each layer (storage layer, processing layer...) into the architecture.
How to migrate AWS RDS Oracle DBs to OCI using OCI Backup Service. View how you can migrate your Oracle databases on AWS to OCI. View the recording at : https://asktom.oracle.com/pls/apex/asktom.search?oh=7575
Apache Hadoop YARN is the modern Distributed Operating System. It enables the Hadoop compute layer to be a common resource-management platform that can host a wide variety of applications. Multiple organizations are able to leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues etc.
In this talk, we’ll first hit the ground with the current status of Apache Hadoop YARN – how it is faring today in deployments large and small. We will cover different types of YARN deployments, in different environments and scale.
We'll then move on to the exciting present & future of YARN – features that are further strengthening YARN as the first-class resource-management platform for datacenters running enterprise Hadoop. We’ll discuss the current status as well as the future promise of features and initiatives like – 10x scheduler throughput improvements, docker containers support on YARN, support for long running services (alongside applications) natively without any changes, seamless application upgrades, fine-grained isolation for multi-tenancy using CGroups on disk & network resources, powerful scheduling features like application priorities, intra-queue preemption across applications and operational enhancements including insights through Timeline Service V2, a new web UI and better queue management.
We’re living in an era of digital disruption, where the accessibility and adoption of emerging digital technologies are enabling
enterprises to reimagine their businesses in exciting new ways. Data flows from the edge to the core to the cloud while
performing analytics and gaining actionable intelligence at all steps along the way. This connected, automated and data-driven
future enables organizations to rapidly acquire, analyze, and take action on real-time data as well as curate flows for additional
analysis at a later stage. New IoT use cases require enterprises to properly handle data in motion and create newer edge
applications with data flow management, stream processing and analytics while still being governed by existing enterprise
services.
This session highlights the importance of an edge-to-core-to-cloud digital infrastructure that can adapt to your flexing
business needs, capturing expanding data flows at the edge and aligning them to a core infrastructure that can drive insight.
Speakers
Bob Mumford, Hewlett Packard Enterprise, Big Data Solutions Architect
Manage Microservices & Fast Data Systems on One Platform w/ DC/OSMesosphere Inc.
The application landscape inside our data center is changing: Along with the trend of moving toward microservices and containers, there are a number of new distributed data processing frameworks such as Kafka or Cassandra being released on a weekly basis. These changes have implications for the ways we think about infrastructure. With the growing need for computing power and the rise of distributed applications comes the need for a reliable and simple-use cluster manager and programming abstraction.
In this presentation, Mesosphere explains how to use DC/OS to manage microservices and fast data systems on a single platform. We will look at how container orchestration, including resource management and service management, can be streamlined to process fast data in a matter of seconds, allowing for predictive user interfaces, product recommendations, and billing charge back, among other modern app components.
Distributed systems like Cassandra provide significant opportunity for fault tolerance, reliability and continuous uptime. Frequently, what it really takes to ensure this degree of uptime is greatly misunderstood, or under rated. Often, enterprises do not understand or prepare for failure until it happens, which clearly is too late.
Talk to cover the following topics:
-Why do I need to prepare for failure
-What should I be operationally testing
-Understanding failure scenarios
-Understanding time to recovery
-Capacity planning for failure
-Multi-Data Center considerations
-But it's distributed, why do I need to backup?
About the Speaker
Thomas Valley Solutions Engineer, DataStax
Thom Valley is a Solutions Engineer at DataStax Inc. Immediately after graduating with a degree in English and Theatre, Thom went to work for his first software company, installing systems and training users at beverage distribution companies nationwide. From there he moved on to more challenging roles in a range of industries including; Director of R&D for a web publishing startup, CTO of an e-commerce and fulfillment company and COO of an smart cities / IOT provider.
Apache Hive is a rapidly evolving project which continues to enjoy great adoption in big data ecosystem. Although, Hive started primarily as batch ingestion and reporting tool, community is hard at work in improving it along many different dimensions and use cases. This talk will provide an overview of latest and greatest features and optimizations which have landed in project over last year. Materialized view, micro managed tables and workload management are some noteworthy features.
I will deep dive into some optimizations which promise to provide major performance gains. Support for ACID tables has also improved considerably. Although some of these features and enhancements are not novel but have existed for years in other DB systems, implementing them on Hive poses some unique challenges and results in lessons which are generally applicable in many other contexts. I will also provide a glimpse of what is expected to come in near future.
Speaker: Ashutosh Chauhan, Engineering Manager, Hortonworks
Escalabilidad horizontal y arquitecturas elásticas en Microsoft azureEnrique Catala Bañuls
En esta sesión veremos problemas y soluciones a la hora de escalar arquitecturas muy exigentes. Veremos opciones para segmentar lecturas-escrituras con Replicación y AlwaysON, utilizar sistemas de cacheo con AppFabric Cache y/o Azure Cache, entornos híbridos para liberar carga con Azure. Orientaremos la sesión para que el asistente entienda las alternativas que hay y sus pros y contras de cara a su evaluación.
7.1 Identify which attribute scopes are thread-safe:
Local variables
Instance variables
Class variables
Request attributes
Session attributes
Context attributes
7.2 Identify correct statements about differences between the multithreaded and single-threaded servlet models.
7.3 Identify the interface used to declare that a servlet must use the single thread model.
2021 04-20 apache arrow and its impact on the database industry.pptxAndrew Lamb
The talk will motivate why Apache Arrow and related projects (e.g. DataFusion) is a good choice for implementing modern analytic database systems. It reviews the major components in most databases and explains where Apache Arrow fits in, and explains additional integration benefits from using Arrow.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
6. <Insert Picture Here> “ A Data Grid is a system composed of multiple servers that work together to manage information and related operations - such as computations - in a distributed environment .” Cameron Purdy VP of Development, Oracle
18. Data Grid Uses Caching Applications request data from the Data Grid rather than backend data sources Analytics Applications ask the Data Grid questions from simple queries to advanced scenario modeling Transactions Data Grid acts as a transactional System of Record, hosting data and business logic Events Automated processing based on event
So why can’t we use database technology to bring high-performance transaction processing to Java applications? The problem is the classic mismatch between object and relational and the huge performance penalty translating back and forth between those two representations of the data. First the object data must be loaded into mid-tier memory from several relational database tables. Then the transaction (object method) is performed. Finally the data is written back to the relational database to commit the transaction and save session state. If another transaction (method call) is performed with the same object, this same process is repeated beginning to end. This performance problem is compounded in modern Event Driven Architectures where one object method call can spawn a whole succession of others.
It is a Development Library. In Java it is jars, dlls etc. We ship with other components Jars to support Spring and Groovy HTTP Session can be used for WLS, OAS. Large online retailer has unified shopping cart across multiple application servers. (WAS, .Net) WebInstaller which replaces default replication
Serialization Options Because serialization is often the most expensive part of clustered data management, Coherence provides the following options for serializing/deserializing data: java.io.Serializable – The simplest, but slowest option. com.tangosol.io.pof.PofSerializer – The Portable Object Format (also referred to as POF) is a language agnostic binary format. POF was designed to be incredibly efficient in both space and time and has become the recommended serialization option in Coherence. java.io.Externalizable – This requires developers to implement serialization manually, but can provide significant performance benefits. Compared to java.io.Serializable, this can cut serialized data size by a factor of two or more (especially helpful with Distributed caches, as they generally cache data in serialized form). Most importantly, CPU usage is dramatically reduced. com.tangosol.io.ExternalizableLite – This is very similar to java.io.Externalizable, but offers better performance and less memory usage by using a more efficient I/O stream implementation. com.tangosol.run.xml.XmlBean– A default implementation of ExternalizableLite (c) Copyright 2007. Oracle Corporation
Coherence provides several cache implementations: Local Cache—Local on-heap caching for non-clustered caching. Replicated Cache Service—Perfect for small, read-heavy caches. Partitioned Cache Service—True linear scalability for both read and write access. Data is automatically, dynamically and transparently partitioned across nodes. The distribution algorithm minimizes network traffic and avoids service pauses by incrementally shifting data. Near Cache—Provides the performance of local caching with the scalability of distributed caching. Several different near-cache strategies provide varying trade-offs between performance and synchronization guarantees. In-process caching provides the highest level of raw performance, since objects are managed within the local JVM. This benefit is most directly realized by the Local, Replicated, Optimistic and Near Cache implementations. Out-of-process (client/server) caching provides the option of using dedicated cache servers. This can be helpful when you want to partition workloads (to avoid stressing the application servers). This is accomplished by using the Partitioned cache implementation and simply disabling local storage on client nodes through a single command-line option or a one-line entry in the XML configuration. Tiered caching (using the Near Cache functionality) enables you to couple local caches on the application server with larger, partitioned caches on the cache servers, combining the raw performance of local caching with the scalability of partitioned caching. This is useful for both dedicated cache servers and co-located caching (cache partitions stored within the application server JVMs). Tech Details Appendix for Cache types/strategies Distributed Cache A distributed, or partitioned, cache is a clustered, fault-tolerant cache that has linear scalability. Data is partitioned among all the machines of the cluster. For fault-tolerance, partitioned caches can be configured to keep each piece of data on one or more unique machines within a cluster. Distributed caches are the most commonly used caches in Coherence. Replicated Cache A replicated cache is a clustered, fault tolerant cache where data is fully replicated to every member in the cluster. This cache offers the fastest read performance with linear performance scalability for reads but poor scalability for writes (as writes must be processed by every member in the cluster). Because data is replicated to all machines, adding servers does not increase aggregate cache capacity. Optimistic Cache An optimistic cache is a clustered cache implementation similar to the replicated cache implementation but without any concurrency control. This implementation offers higher write throughput than a replicated cache. It also allows an alternative underlying store for the cached data (for example, a MRU/MFU-based cache). However, if two cluster members are independently pruning or purging the underlying local stores, it is possible that a cluster member may have a different store content than that held by another cluster member. Near Cache A near cache is a hybrid cache; it typically fronts a distributed cache or a remote cache with a local cache. Near cache invalidates front cache entries, using configurable invalidation strategy, and provides excellent performance and synchronization. Near cache backed by a partitioned cache offers zero-millisecond local access for repeat data access, while enabling concurrency and ensuring coherency and fail-over, effectively combining the best attributes of replicated and partitioned caches. Local Cache A local cache is a cache that is local to (completely contained within) a particular cluster node. While it is not a clustered service, the Coherence local cache implementation is often used in combination with various clustered cache services. Remote Cache A remote cache describes any out of process cache accessed by a Coherence*Extend client. All cache requests are sent to a Coherence proxy where they are delegated to one of the other Coherence cache types (Repilcated, Optimistic, Partitioned).
Data Grids are used for different purposes. These are the four most common uses. Caching Coherence was the first technology to prove reliable distributed caching Helped many organizations alleviate data bottleneck issues and scale out application tier Analytics Enables applications to efficiently run queries across entire data grid Support for heavy query loads, while improving responsiveness of each query Server failures do not impact correctness of “in flight” queries and analytics Transactions Data Grid provides optimal platform for joining data and business logic Greater business agility by moving database stored procedures into the Data Grid Coherence reliability allows not only in-memory data processing, but provides the ability to commit transactions in-memory Reliability is key to conducting in-memory transactions. Coherence provides absolute reliability – every transaction matters. Events Oracle Coherence Data Grid manages processing state, guaranteeing once-and-only-once event processing Data Grid provides scalable management of event processing