This session was delivered as part of the Oracle Ground Breakers EMEA tour in Romania. What does "autonomous" really mean, and what makes the database autonomous? If you're looking for the answers to these questions, this is the session for you! In this session, we invite you to take a peek under the hood of the Oracle Autonomous Database, so you can get a clear understanding of how our unique Autonomous Database works. We’ll share our exclusive combination of database features, best practices and machine learning algorithms that make up this family of cloud services. With the use of live demos, we’ll illustrates how it can simplify your approach to data management and accelerate your transition to the cloud.
This session was delivered as part of the Oracle Ground Breakers EMEA tour in Romania. What does "autonomous" really mean, and what makes the database autonomous? If you're looking for the answers to these questions, this is the session for you! In this session, we invite you to take a peek under the hood of the Oracle Autonomous Database, so you can get a clear understanding of how our unique Autonomous Database works. We’ll share our exclusive combination of database features, best practices and machine learning algorithms that make up this family of cloud services. With the use of live demos, we’ll illustrates how it can simplify your approach to data management and accelerate your transition to the cloud.
DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.
Approaches for WebLogic Server in the Cloud (OpenWorld, September 2014)jeckels
With Oracle WebLogic Server now available "as a service" both on premise working in conjunction with Oracle Enterprise Manager Cloud Pack and also in several Public Clouds beyond Oracle Public Cloud, customers have a clear set of architectural alternatives when looking at building out an enterprise cloud strategy for WebLogic Server. Cloud with WebLogic Server is not just core server but the layered and related products needed in a cloud infrastructure -Web Tier, WebLogic Server, Database and Fusion Middleware - as well as the full lifecycle capabilities needed: development, administration and operations. Navigate a pragmatic cloud approach for your organization and WebLogic Server.
The Power of Java and Oracle WebLogic Server in the Public Cloud (OpenWorld, ...jeckels
Enjoy all the productivity of developing and deploying Java applications on Oracle's standards-based Java platform---without the headache of IT. Powered by Oracle WebLogic Server, the industry's #1 application server, Oracle's Java Platform is purpose-built for deploying standard Java applications as well as Software-as-a-Service (SaaS) extensions. Learn how you can easily get started and securely deploy your applications in the cloud using the proven developer productivity tools, and a robust database persistence layer.
With the rise of IoT and the increasing complexity of applications, clouds, networks and infrastructure, the battle to keep your data and your infrastructure safe from attackers is getting harder. As groups of bad actors collaborate, sharing information and offering illegal access, and botnets as a service, terabits of attack can be launched cheaply. Meanwhile, it’s hard to find enough security analysts to catch and prevent these attacks.
This is where community collaboration and open source efforts like Apache Metron come in. Metron presents a comprehensive framework for application and network, security built on Apache Hadoop and open source Streaming Analytics(ie Apache Nifi, Apache Kafka) tool’s highly scalable data management and processing stacks. Advanced features like profiling, machine learning, and visualization work with real-time streaming detection to make your SOC analysts more efficient, while the intrinsic extensibility of open source helps your data scientists get security insights out of the lab and into production fast.
We will discuss and demonstrate how some real-world businesses and managed service providers are using Apache Metron to identify and solve security threats at scale, and some approaches and ideas for how the platform can fit into your security architecture.
Speaker: Laurence Da Luz, Senior Solutions Architect, Hortonworks
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
In my talk I will discuss and show examples of using Apache Hadoop, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications. This is the follow up to last years Apache Deep Learning 101 that was done at Dataworks Summit and ApacheCon.
As part of my talk I will walk through using Apache NXNet Pre-Built Models, MXNet's New Model Server with Apache NiFi, executing MXNet with Apache NiFi and running Apache MXNet on edge nodes utilizing Python and Apache MiniFi.
This talk is geared towards Data Engineers interested in the basics of Deep Learning with open source Apache tools in a Big Data environment. I will walk through source code examples available in github and run the code live on an Apache Hadoop / YARN / Apache Spark cluster.
This will be an introduction to executing Deep Learning Pipelines in an Apache Big Data environment.
My talk at Data Works Summit Sydney was listed in top 7 -> https://hortonworks.com/blog/7-sessions-dataworks-summit-sydney-see/
Also have speak at and run Future of Data Princeton and at Oracle Code NYC.
https://www.slideshare.net/oom65/hadoop-security-architecture?next_slideshow=1
https://community.hortonworks.com/articles/83100/deep-learning-iot-workflows-with-raspberry-pi-mqtt.html
https://community.hortonworks.com/articles/146704/edge-analytics-with-nvidia-jetson-tx1-running-apac.html
https://dzone.com/refcardz/introduction-to-tensorflow
Apache Hadoop YARN is the latest distributed operating system for HDSF for big data applications and storage. YARN has transformed the Hadoop Compute Layer into a general resource management platform capable of hosting a wide variety of applications.
This lecture begins with the current state how Apache Hadoop YARN is currently used in large scale deployment. The next topic will cover about strengthening YARN 's current and future - like YARN' s excitement - as a top-notch resource management platform for data centers running enterprise Hadoop. Discuss the current state and future of the following functions and initiatives: support of machine learning through strong container placement, global scheduling, GPU and FPGA support and deep learning workload, large scale of YARN federation, on YARN Containerized applications, natural support that does not change to long-running services (along with applications), seamless application upgrades, powerful scheduling functions, operational improvements and better queue management.
The second part of the lecture focuses on the latest enhancement of HDFS. HDFS has several advantages: horizontal scale of IO bandwidth, storage scaled to petabyte storage. In addition, it provides extremely low latency metadata operations and coordinates for over 60,000 concurrent clients. Hadoop 3.0 recently introduced Erasure Coding. One limitation of HDFS is the scaling of multiple files and blocks in the system. I will explain the fundamental change of Hadoop's storage infrastructure using Ozone technology which will be announced soon. This will allow Hadoop to scale billions of files and blocks in the future to a larger number of smaller objects than before.
Oracle усиливает свои позиции на рынке Cloud Computing, приобретая компанию Ravello Systems - лидера на рынке nested virtualization (вложенная виртуализация) и стремительно развивая решения по переносу on-premise мощностей в облако.
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.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations 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 start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, 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.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Learn how Hortonworks Data Flow (HDF), powered by Apache Nifi, enables organizations to harness IoAT data streams to drive business and operational insights. We will use the session to provide an overview of HDF, including detailed hands-on lab to build HDF pipelines for capture and analysis of streaming data.
Recording and labs available at:
http://hortonworks.com/partners/learn/#hdf
Unlocking a fully integrated Spark experience within your enterprise Hadoop environment that is manageable, secure and deployable anywhere.
Presented at the Spark Summit by Arun C Murthy (co-Founder, Hortonworks) on Monday, June 15, 2015.
Running Chrome/Android OS on Raspberry PiLouis Shue
Presented at GDG Singapore DevFest 2013 on 12/10/2013, by Arun Kumar, technical consultant, Malaysia.
Summary:
1. Introduction to Raspberry Pi, Chrome/Android OS
2. Building Chrome/Android for Raspberry Pi
3. Why Chrome/Android on Raspberry Pi
4. Advantages and Dis-advantages of running Chrome/Android on Raspberry Pi
DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.
Approaches for WebLogic Server in the Cloud (OpenWorld, September 2014)jeckels
With Oracle WebLogic Server now available "as a service" both on premise working in conjunction with Oracle Enterprise Manager Cloud Pack and also in several Public Clouds beyond Oracle Public Cloud, customers have a clear set of architectural alternatives when looking at building out an enterprise cloud strategy for WebLogic Server. Cloud with WebLogic Server is not just core server but the layered and related products needed in a cloud infrastructure -Web Tier, WebLogic Server, Database and Fusion Middleware - as well as the full lifecycle capabilities needed: development, administration and operations. Navigate a pragmatic cloud approach for your organization and WebLogic Server.
The Power of Java and Oracle WebLogic Server in the Public Cloud (OpenWorld, ...jeckels
Enjoy all the productivity of developing and deploying Java applications on Oracle's standards-based Java platform---without the headache of IT. Powered by Oracle WebLogic Server, the industry's #1 application server, Oracle's Java Platform is purpose-built for deploying standard Java applications as well as Software-as-a-Service (SaaS) extensions. Learn how you can easily get started and securely deploy your applications in the cloud using the proven developer productivity tools, and a robust database persistence layer.
With the rise of IoT and the increasing complexity of applications, clouds, networks and infrastructure, the battle to keep your data and your infrastructure safe from attackers is getting harder. As groups of bad actors collaborate, sharing information and offering illegal access, and botnets as a service, terabits of attack can be launched cheaply. Meanwhile, it’s hard to find enough security analysts to catch and prevent these attacks.
This is where community collaboration and open source efforts like Apache Metron come in. Metron presents a comprehensive framework for application and network, security built on Apache Hadoop and open source Streaming Analytics(ie Apache Nifi, Apache Kafka) tool’s highly scalable data management and processing stacks. Advanced features like profiling, machine learning, and visualization work with real-time streaming detection to make your SOC analysts more efficient, while the intrinsic extensibility of open source helps your data scientists get security insights out of the lab and into production fast.
We will discuss and demonstrate how some real-world businesses and managed service providers are using Apache Metron to identify and solve security threats at scale, and some approaches and ideas for how the platform can fit into your security architecture.
Speaker: Laurence Da Luz, Senior Solutions Architect, Hortonworks
As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
In my talk I will discuss and show examples of using Apache Hadoop, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications. This is the follow up to last years Apache Deep Learning 101 that was done at Dataworks Summit and ApacheCon.
As part of my talk I will walk through using Apache NXNet Pre-Built Models, MXNet's New Model Server with Apache NiFi, executing MXNet with Apache NiFi and running Apache MXNet on edge nodes utilizing Python and Apache MiniFi.
This talk is geared towards Data Engineers interested in the basics of Deep Learning with open source Apache tools in a Big Data environment. I will walk through source code examples available in github and run the code live on an Apache Hadoop / YARN / Apache Spark cluster.
This will be an introduction to executing Deep Learning Pipelines in an Apache Big Data environment.
My talk at Data Works Summit Sydney was listed in top 7 -> https://hortonworks.com/blog/7-sessions-dataworks-summit-sydney-see/
Also have speak at and run Future of Data Princeton and at Oracle Code NYC.
https://www.slideshare.net/oom65/hadoop-security-architecture?next_slideshow=1
https://community.hortonworks.com/articles/83100/deep-learning-iot-workflows-with-raspberry-pi-mqtt.html
https://community.hortonworks.com/articles/146704/edge-analytics-with-nvidia-jetson-tx1-running-apac.html
https://dzone.com/refcardz/introduction-to-tensorflow
Apache Hadoop YARN is the latest distributed operating system for HDSF for big data applications and storage. YARN has transformed the Hadoop Compute Layer into a general resource management platform capable of hosting a wide variety of applications.
This lecture begins with the current state how Apache Hadoop YARN is currently used in large scale deployment. The next topic will cover about strengthening YARN 's current and future - like YARN' s excitement - as a top-notch resource management platform for data centers running enterprise Hadoop. Discuss the current state and future of the following functions and initiatives: support of machine learning through strong container placement, global scheduling, GPU and FPGA support and deep learning workload, large scale of YARN federation, on YARN Containerized applications, natural support that does not change to long-running services (along with applications), seamless application upgrades, powerful scheduling functions, operational improvements and better queue management.
The second part of the lecture focuses on the latest enhancement of HDFS. HDFS has several advantages: horizontal scale of IO bandwidth, storage scaled to petabyte storage. In addition, it provides extremely low latency metadata operations and coordinates for over 60,000 concurrent clients. Hadoop 3.0 recently introduced Erasure Coding. One limitation of HDFS is the scaling of multiple files and blocks in the system. I will explain the fundamental change of Hadoop's storage infrastructure using Ozone technology which will be announced soon. This will allow Hadoop to scale billions of files and blocks in the future to a larger number of smaller objects than before.
Oracle усиливает свои позиции на рынке Cloud Computing, приобретая компанию Ravello Systems - лидера на рынке nested virtualization (вложенная виртуализация) и стремительно развивая решения по переносу on-premise мощностей в облако.
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.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations 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 start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, 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.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Learn how Hortonworks Data Flow (HDF), powered by Apache Nifi, enables organizations to harness IoAT data streams to drive business and operational insights. We will use the session to provide an overview of HDF, including detailed hands-on lab to build HDF pipelines for capture and analysis of streaming data.
Recording and labs available at:
http://hortonworks.com/partners/learn/#hdf
Unlocking a fully integrated Spark experience within your enterprise Hadoop environment that is manageable, secure and deployable anywhere.
Presented at the Spark Summit by Arun C Murthy (co-Founder, Hortonworks) on Monday, June 15, 2015.
Running Chrome/Android OS on Raspberry PiLouis Shue
Presented at GDG Singapore DevFest 2013 on 12/10/2013, by Arun Kumar, technical consultant, Malaysia.
Summary:
1. Introduction to Raspberry Pi, Chrome/Android OS
2. Building Chrome/Android for Raspberry Pi
3. Why Chrome/Android on Raspberry Pi
4. Advantages and Dis-advantages of running Chrome/Android on Raspberry Pi
Messaging becomes Data Distributions gets embedded event processing (not complex, made simple) - bending all the rules one benchmark at a time - Push Technology, Waratek and other things
Smart Wireless Surveillance Monitoring using RASPBERRY PIKrishna Kumar
This is a slide about the smart surveillance monitoring system using raspberry pi.
It includes the full details of the procedure , component description and the screenshots
My Official Hack slides from Dockercon 2016 as demonstrated in the community theatre in the expo area.
In this hack, we secure the data-center through a scaleable network of real-time sensors and microservices running Docker. Each rack in the server-room is filled with thousands of terabytes of priceless customer data, IoT lets us keep one step ahead and keep that data safe. The cluster deploys a set of smart sensors running the Docker Swarm agent to the rack panels.
Java in the Air: A Case Study for Java-based Environment Monitoring StationsEurotech
Eurotech and Oracle Joint presentation at JavaOne 2014 that introduces:
IoT Present and Challenges
Java, OSGi and Eclipse Kura: IoT Gateway Services
Embedded Data Stream: Edge Analytics
Use Case: Environment Monitoring Stations
Slides from the June Oracle Middleware Forum held in Canberra, Australia. Covers some of the new features of WebLogic 12c; including HTML5 support, WebSockets, integrated Maven, managed Coherence servers among others
Technical deep dive on Java Micro Edition (ME) 8 (apologies for the partially messed up colors and slides - SlideShare is doing that during the conversion process)
Sviluppo IoT - Un approccio standard da Nerd ad Impresa, prove pratiche di Me...Codemotion
Codemotion Rome 2015 - Gli anni passati a veder nascere e crescere tecnologie e tendenze ci aiutano a comprendere come l'Internet delle Cose sia diventata matura per il mercato delle imprese. L’intervento, che include una panoramica sulle tendenze attuali e future dell’IoT, è centrato sullo sviluppo di soluzioni basate su standard industriali in ascesa (eg. Z-Wave), mettendo in evidenza gli inevitabili vantaggi e limiti derivanti dall’adozione di una metodologia industriale: solo un approccio industriale può rappresentare il vero e proprio salto di qualità per proporre prodotti efficaci per un mercato a doppia cifra.
Oracle Openworld Presentation with Paul Kent (SAS) on Big Data Appliance and ...jdijcks
Learn about the benefits of Oracle Big Data Appliance and how it can drive business value underneath applications and tools. This includes a section by Paul Kent, VP Big Data SAS describing how SAS runs well on Oracle Engineered Systems and on Oracle Big Data Appliance specifically.
Noc informatyka - co ja wiem o testowaniuTomek Borek
An old preso (in Polish) from May 2013 about testing and TDD for the beginner's crowd. Stara prezentacja z maja 2013 o testowaniu i TDD dla początkujących.
Quite often "new" people are only "new" to Postgres. This is my summary of do's and don'ts when it comes to teaching Postgres, what to take note on, with emphasis on teaching
Katalog architektur, DDD, mikroserwisy, czysta i cebulowa architektura a także kilka słów wprowadzających do tematu, w tym czym jest architektura, jej poziomy, rola w dokumentowaniu rozwiązań i problemy przy próbach egzekwowania.
When performance hits rock-bottom everybody (and their dog) is called upon and all of a sudden developers should have been responsible for last half a year or so and code with performance in mind (and deadlines, but that of course goes unsaid). So, here I'm talking about what can a dev do to meet those unreasonable demands) and what might he do anticipating them.
Strictly JVM, mostly Sun Hotspot impl, but number of points can be used to other JVMs as wel
Java Memory Consistency Model - concepts and contextTomek Borek
Java Memory Consistency Model is a difficult topic.
It's useful in making sure that multi-threaded programs on multi-threaded cores will interact with each other (and through memory) in a consistent manner.
It's specification is damn hard (even according to folks with lots of concurrent experience, like Doug Lea) to read, understand and routinely follow without error.
This presentation talks about some fallacies surrounding memory model, explains it, offers definitions and reasons for it's existence. It ain't deep, it's more entry level stuff.
Presentation I gave at TestWell in Kraków, 21st April 2015. Synthesis of my experiences in 4 years of putting out the fires.
I talk about putting out fires and tools needed for it (mental and software alike):
Where does the smoke come from:
1) YOU - what to focus on - how to recognize urgent from important
2) TOOLS - which tools can help and which cannot
3) OTHER PEOPLE - how to enlist support's and operations' help while you're only a mere developer
Finally, I mention when to let go, how to see there's too much smoke and what then.
Summarizing, topics cover:
- diagnostic methodology for performance problems
- how not to loose your head
- how not to let yourself be pulled into others' pace and fright / urgency
- monitoring tools
- logging 101
- how not to fly solo, for this makes things way harder than necessary
- advice on "when there's too much smoke"
Spróbujmy szczęścia bo zaciskanie pięści nie działaTomek Borek
Szereg studiów dotyczących szczęścia i kontroli pokazuje ciekawe rezultaty. Szczęście to mieszanina genetyki, czynników zewnętrznych i naszych czynów, myśli i nastawienia. Kontrola może być biologicznie wbudowaną potrzebą. Jedno i drugie zatem można zastosować w pracy i na pewno nie raz to już robiliśmy. Spróbujmy dowiedzieć się więcej, by wiedzieć gdzie i kiedy.
Lightning talk on Java Memory Consistency Model Java Day Kiev 2014Tomek Borek
Brief introduction into what Memory Model is about and why it matters and when. Explanation why it was changed in Tiger and how. Also informs where to look for more and offers definition. I've gave this talk at PJUG and Java Day Kiev in 2014.
Few words about happiness (Polish talk) / O szczęściu słów kilkaTomek Borek
Since latest One Beer Talks were rather on the hard side, I went with soft presentation and talked about happiness. I outlined new research and showed what was found and how and provided links for more. It's a short preso, a lightning talk if you will.
Ponieważ ostatne Piwne Gadki były raczej mocno techniczne, zrobiłem miękką prezentację o szczęściu, zarysowującą nowe badania nad nim i dającą ogólny pogląd o kilku rzeczach w tej domenie, wszystko w kwadrans.
It's not always the application's faultTomek Borek
English version of a talk in Polish, myself and Jacek Jagieła gave at Polish JUG during JavaCamp #12.
We presented what we believed would be good to know to design infrastructure fit for given purpose. Virtualization, network topology, monitoring, etc. Of course, since talk lasted one hour, in-depth approach wasn't possible. ;-)
Talk in Polish, I and Jacek Jagieła gave at Polish JUG during JavaCamp #12. We presented what we believed would be good to know to design infrastructure fit for given purpose. Virtualization, network topology, monitoring, etc. Of course, since talk lasted one hour, in-depth approach wasn't possible. ;-)
Wprowadzenie do optymalizacji wielokryterialnej / Intro to multicriteria opti...Tomek Borek
Stara prezentacja moja i Karola opublikowana przez Alicję Pachocki, z czasów studiów.
http://slideplayer.pl/slide/60910/
Old preso done during my studies, introducing multicriteria optimization. Thanks to Alicja Pachocki for digging it out:
http://slideplayer.pl/slide/60910/
Git not for beginners. Confitura talk, in Polish. Describes detached head, commit, repository, SHA1, various ways of ignoring files, interactivity and more.
"Narco" emotions - description of study on whether Twitter can be used to gle...Tomek Borek
for UA class, we had to choose a HCI paper, read it and make a short presentation about it. Paper selection criteria was that it can be used to demonstrate how cognitive science helps UA. I kinda failed in that regard (there were much better papers out there) but the content of that paper hijacked my attention totally.
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
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
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.
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.