Introduction to Chaos Engineering with Microsoft AzureAna Medina
https://www.gremlin.com/webinars/ce-on-azure/
Join us for a walkthrough on how to get started with Chaos Engineering on Azure. Learn the fundamentals of Chaos Engineering and how to build more reliable applications on Azure.
In this live session, we’ll show you how to get started running experiments on Azure’s managed Kubernetes (AKS) and how to implement continuous Chaos Engineering using Azure Pipelines. Then be sure to stay until the end for live Q&A.
AGENDA
- Learn the history, principles and practice of Chaos Engineering
- How to get started with Chaos Engineering on Azure
- Run chaos experiments to simulate common real-world failures on AKS
- How to implement Chaos Engineering Experiments on Azure Pipelines
Amazon EMR enables fast processing of large structured or unstructured datasets, and in this presentation we'll show you how to setup an Amazon EMR job flow to analyse application logs, and perform Hive queries against it. We also review best practices around data file organisation on Amazon Simple Storage Service (S3), how clusters can be started from the AWS web console and command line, and how to monitor the status of a Map/Reduce job.
Finally we take a look at Hadoop ecosystem tools you can use with Amazon EMR and the additional features of the service.
See a recording of the webinar based on this presentation on YouTube here:
Check out the rest of the Masterclass webinars for 2015 here: http://aws.amazon.com/campaigns/emea/masterclass/
See the Journey Through the Cloud webinar series here: http://aws.amazon.com/campaigns/emea/journey/
What is observability and how is it different from traditional monitoring? How do we effectively monitor and debug complex, elastic microservice architectures? In this interactive discussion, we’ll answer these questions. We’ll also introduce the idea of an “observability pipeline” as a way to empower teams following DevOps practices. Lastly, we’ll demo cloud-native observability tools that fit this “observability pipeline” model, including Fluentd, OpenTracing, and Jaeger.
Integration Patterns for Microservices ArchitecturesNATS
NATS was created by Derek Collison, founder and CEO
of Apcera, who has spent 20+ years designing, building, and using publish-subscribe messaging systems.
Unlike traditional enterprise messaging systems, NATS has an always-on dial tone that does whatever it takes to remain available. Learn how end users are building modern, reliable and scalable cloud and distributed systems with NATS.
Talk given by David Williams, Principal, Williams & Garcia
You can learn more about NATS at http://www.nats.io
Prometheus: Monitoring by "Pravin Magdum" from "Crevise". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
Slides for the presentation on BaaS at the Cloud Computing event hosted by GTECH Technology Forum, Technopark.
Covers :
- What is Backend as a Service ?
- How much time can be saved by using BaaS ?
- Current Mobile Ecosystem with BaaS as the new middleware
- Bringing BaaS to Enterprise IT.
- Leading Providers
Introduction to Chaos Engineering with Microsoft AzureAna Medina
https://www.gremlin.com/webinars/ce-on-azure/
Join us for a walkthrough on how to get started with Chaos Engineering on Azure. Learn the fundamentals of Chaos Engineering and how to build more reliable applications on Azure.
In this live session, we’ll show you how to get started running experiments on Azure’s managed Kubernetes (AKS) and how to implement continuous Chaos Engineering using Azure Pipelines. Then be sure to stay until the end for live Q&A.
AGENDA
- Learn the history, principles and practice of Chaos Engineering
- How to get started with Chaos Engineering on Azure
- Run chaos experiments to simulate common real-world failures on AKS
- How to implement Chaos Engineering Experiments on Azure Pipelines
Amazon EMR enables fast processing of large structured or unstructured datasets, and in this presentation we'll show you how to setup an Amazon EMR job flow to analyse application logs, and perform Hive queries against it. We also review best practices around data file organisation on Amazon Simple Storage Service (S3), how clusters can be started from the AWS web console and command line, and how to monitor the status of a Map/Reduce job.
Finally we take a look at Hadoop ecosystem tools you can use with Amazon EMR and the additional features of the service.
See a recording of the webinar based on this presentation on YouTube here:
Check out the rest of the Masterclass webinars for 2015 here: http://aws.amazon.com/campaigns/emea/masterclass/
See the Journey Through the Cloud webinar series here: http://aws.amazon.com/campaigns/emea/journey/
What is observability and how is it different from traditional monitoring? How do we effectively monitor and debug complex, elastic microservice architectures? In this interactive discussion, we’ll answer these questions. We’ll also introduce the idea of an “observability pipeline” as a way to empower teams following DevOps practices. Lastly, we’ll demo cloud-native observability tools that fit this “observability pipeline” model, including Fluentd, OpenTracing, and Jaeger.
Integration Patterns for Microservices ArchitecturesNATS
NATS was created by Derek Collison, founder and CEO
of Apcera, who has spent 20+ years designing, building, and using publish-subscribe messaging systems.
Unlike traditional enterprise messaging systems, NATS has an always-on dial tone that does whatever it takes to remain available. Learn how end users are building modern, reliable and scalable cloud and distributed systems with NATS.
Talk given by David Williams, Principal, Williams & Garcia
You can learn more about NATS at http://www.nats.io
Prometheus: Monitoring by "Pravin Magdum" from "Crevise". The presentation was done at #doppa17 DevOps++ Global Summit 2017. All the copyrights are reserved with the author
Slides for the presentation on BaaS at the Cloud Computing event hosted by GTECH Technology Forum, Technopark.
Covers :
- What is Backend as a Service ?
- How much time can be saved by using BaaS ?
- Current Mobile Ecosystem with BaaS as the new middleware
- Bringing BaaS to Enterprise IT.
- Leading Providers
This presentation is an overview of the "Performance Modelling of Serverless Computing Platforms" paper published in IEEE Transactions on Cloud Computing (TCC). These slides were presented at CASCON 2020 Workshop on Cloud Computing.
Authors: Nima Mahmoudi and Hamzeh Khazaei
Paper: https://doi.org/10.1109/TCC.2020.3033373
Presentation: https://youtu.be/R3-gEd_0TF8
An Introduction to the AWS Well Architected Framework - WebinarAmazon Web Services
The AWS Well-Architected Framework enables customers to understand best practices around security, reliability, performance, cost optimization and operational excellence when building systems on AWS. This approach helps customers make informed decisions and weigh the pros and cons of application design patterns for the cloud.
In this one hour webinar, you'll learn how to use the AWS Well-Architected Framework to follow guidelines and best practices for your architecture on AWS.
While many organizations have started to automate their software development processes, many still engineer their infrastructure largely by hand. Treating your infrastructure just like any other piece of code creates a “programmable infrastructure” that allows you to take full advantage of the scalability and reliability of the AWS cloud. This session will walk through practical examples of how AWS customers have merged infrastructure configuration with application code to create application-specific infrastructure and a truly unified development lifecycle. You will learn how AWS customers have leveraged tools like CloudFormation, orchestration engines, and source control systems to enable their applications to take full advantage of the scalability and reliability of the AWS cloud, create self-reliant applications, and easily recover when things go seriously wrong with their infrastructure.
Mapreduce examples starting from the basic WordCount to a more complex K-means algorithm. The code contained in these slides is available at https://github.com/andreaiacono/MapReduce
Application monitoring is being talked about a lot these days and it helps provide key information that is helpful in developing better software and also in taking some key business decision. Datadog offers monitoring as a service.
A introduction to Microservices Architecture: definition, characterstics, framworks, success stories. It contains a demo about implementation of microservices with Spring Boot, Spring cloud an Eureka.
Apache Hadoop and Spark on AWS: Getting started with Amazon EMR - Pop-up Loft...Amazon Web Services
Amazon EMR is a managed service that makes it easy for customers to use big data frameworks and applications like Apache Hadoop, Spark, and Presto to analyze data stored in HDFS or on Amazon S3, Amazon’s highly scalable object storage service. In this session, we will introduce Amazon EMR and the greater Apache Hadoop ecosystem, and show how customers use them to implement and scale common big data use cases such as batch analytics, real-time data processing, interactive data science, and more. Then, we will walk through a demo to show how you can start processing your data at scale within minutes.
An Introduction to AWS, Why Organizations are choosing AWS, What Workloads are appropriate on AWS, and How Organizations are getting started with AWS. Chris will discuss what many AWS public sector customers and partners are doing with and saying about AWS. Lastly, we will talk about various strategies for how customers and partners can get started with AWS.
by Harrell Stiles, Sr. Consultant, AWS ProServe
Batch computing is a common way to run a series of programs, called batch jobs, on a large pool of shared compute resources, such as servers, virtual machines, and containers. But running batch workloads at scale is a challenging task, configuring and scaling a cluster of virtual machines to process complex batch jobs is difficult and resource intensive. In this session, we’ll discuss options and best practices for running batch jobs on AWS including AWS Batch, a fully managed batch-processing service, and building batch processing architectures with the Amazon EC2 Container Service. We’ll also discuss best practices for ensuring efficient and opportunistic scheduling, fine-grained monitoring, compute resource auto-scaling, and security for batch jobs. Level 200
Java Thread and Process Performance for Parallel Machine Learning on Multicor...Saliya Ekanayake
The growing use of Big Data frameworks on large machines highlights the importance of performance issues and the value of High Performance Computing (HPC) technology. This paper looks carefully at three major frameworks Spark, Flink and Message Passing Interface (MPI) both in scaling across nodes and internally over the many cores inside modern nodes.We focus on the special challenges of the Java Virtual Machine (JVM) using an Intel Haswell HPC cluster with 24 cores per node. Two parallel machine learning algorithms, K-Means clustering and Multidimensional Scaling (MDS) are used in our performance studies. We identify three major issues – thread models, affinity patterns, and communication mechanisms – as factors affecting performance by large factors and show how to optimize them so that Java can match the performance of traditional HPC languages like C. Further we suggest approaches that preserve the user interface and elegant dataflow approach of Flink and Spark but modify the runtime so that these Big Data frameworks can achieve excellent performance and realize the goals of HPCBig Data convergence.
This presentation is an overview of the "Performance Modelling of Serverless Computing Platforms" paper published in IEEE Transactions on Cloud Computing (TCC). These slides were presented at CASCON 2020 Workshop on Cloud Computing.
Authors: Nima Mahmoudi and Hamzeh Khazaei
Paper: https://doi.org/10.1109/TCC.2020.3033373
Presentation: https://youtu.be/R3-gEd_0TF8
An Introduction to the AWS Well Architected Framework - WebinarAmazon Web Services
The AWS Well-Architected Framework enables customers to understand best practices around security, reliability, performance, cost optimization and operational excellence when building systems on AWS. This approach helps customers make informed decisions and weigh the pros and cons of application design patterns for the cloud.
In this one hour webinar, you'll learn how to use the AWS Well-Architected Framework to follow guidelines and best practices for your architecture on AWS.
While many organizations have started to automate their software development processes, many still engineer their infrastructure largely by hand. Treating your infrastructure just like any other piece of code creates a “programmable infrastructure” that allows you to take full advantage of the scalability and reliability of the AWS cloud. This session will walk through practical examples of how AWS customers have merged infrastructure configuration with application code to create application-specific infrastructure and a truly unified development lifecycle. You will learn how AWS customers have leveraged tools like CloudFormation, orchestration engines, and source control systems to enable their applications to take full advantage of the scalability and reliability of the AWS cloud, create self-reliant applications, and easily recover when things go seriously wrong with their infrastructure.
Mapreduce examples starting from the basic WordCount to a more complex K-means algorithm. The code contained in these slides is available at https://github.com/andreaiacono/MapReduce
Application monitoring is being talked about a lot these days and it helps provide key information that is helpful in developing better software and also in taking some key business decision. Datadog offers monitoring as a service.
A introduction to Microservices Architecture: definition, characterstics, framworks, success stories. It contains a demo about implementation of microservices with Spring Boot, Spring cloud an Eureka.
Apache Hadoop and Spark on AWS: Getting started with Amazon EMR - Pop-up Loft...Amazon Web Services
Amazon EMR is a managed service that makes it easy for customers to use big data frameworks and applications like Apache Hadoop, Spark, and Presto to analyze data stored in HDFS or on Amazon S3, Amazon’s highly scalable object storage service. In this session, we will introduce Amazon EMR and the greater Apache Hadoop ecosystem, and show how customers use them to implement and scale common big data use cases such as batch analytics, real-time data processing, interactive data science, and more. Then, we will walk through a demo to show how you can start processing your data at scale within minutes.
An Introduction to AWS, Why Organizations are choosing AWS, What Workloads are appropriate on AWS, and How Organizations are getting started with AWS. Chris will discuss what many AWS public sector customers and partners are doing with and saying about AWS. Lastly, we will talk about various strategies for how customers and partners can get started with AWS.
by Harrell Stiles, Sr. Consultant, AWS ProServe
Batch computing is a common way to run a series of programs, called batch jobs, on a large pool of shared compute resources, such as servers, virtual machines, and containers. But running batch workloads at scale is a challenging task, configuring and scaling a cluster of virtual machines to process complex batch jobs is difficult and resource intensive. In this session, we’ll discuss options and best practices for running batch jobs on AWS including AWS Batch, a fully managed batch-processing service, and building batch processing architectures with the Amazon EC2 Container Service. We’ll also discuss best practices for ensuring efficient and opportunistic scheduling, fine-grained monitoring, compute resource auto-scaling, and security for batch jobs. Level 200
Java Thread and Process Performance for Parallel Machine Learning on Multicor...Saliya Ekanayake
The growing use of Big Data frameworks on large machines highlights the importance of performance issues and the value of High Performance Computing (HPC) technology. This paper looks carefully at three major frameworks Spark, Flink and Message Passing Interface (MPI) both in scaling across nodes and internally over the many cores inside modern nodes.We focus on the special challenges of the Java Virtual Machine (JVM) using an Intel Haswell HPC cluster with 24 cores per node. Two parallel machine learning algorithms, K-Means clustering and Multidimensional Scaling (MDS) are used in our performance studies. We identify three major issues – thread models, affinity patterns, and communication mechanisms – as factors affecting performance by large factors and show how to optimize them so that Java can match the performance of traditional HPC languages like C. Further we suggest approaches that preserve the user interface and elegant dataflow approach of Flink and Spark but modify the runtime so that these Big Data frameworks can achieve excellent performance and realize the goals of HPCBig Data convergence.
High Performance Data Analytics with Java on Large Multicore HPC ClustersSaliya Ekanayake
Within the last few years, there have been significant contributions to Java-based big data frameworks and libraries
such as Apache Hadoop, Spark, and Storm. While these
systems are rich in interoperability and features, developing
high performance big data analytic applications is challenging.
Also, the study of performance characteristics and
high performance optimizations is lacking in the literature for
these applications. By contrast, these features are well documented in the High Performance Computing (HPC) domain and some of the techniques have potential performance benefits in the big data domain as well. This paper presents the implementation of a high performance big data analytics library - SPIDAL Java - with a comprehensive discussion on five performance challenges, solutions, and speedup results. SPIDAL Java captures a class of global machine learning applications with significant computation and communication that can serve as a yardstick in studying performance bottlenecks with Java big data analytics. The five challenges present here are the cost of intra-node messaging, inefficient cache utilization, performance costs with threads, overhead of garbage collection, and the costs of heap allocated objects. SPIDAL Java presents its solutions to these and demonstrates significant performance gains and scalability when running on up to 3072 cores in one of the latest Intel Haswell-based multicore clusters.
This is a survey on HPCS languages, i.e. Chapel, X10, and Fortress comparing their idioms that support parallel programming. Paper on this is available at http://grids.ucs.indiana.edu/ptliupages/publications/Survey_on_HPCS_Languages_formatted_v2.pdf
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!
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
MapReduce in Simple Terms
1. The Story of Sam
Saliya Ekanayake
SALSA HPC Group
http://salsahpc.indiana.edu
Pervasive Technology Institute, Indiana University, Bloomington
2. Believed “an apple a day keeps a doctor away”
Mother
Sam
An Apple
3. Sam thought of “drinking” the apple
He used a to cut
the and a
to make juice.
4. (map ‘( ))
( )
Sam applied his invention to all the
fruits he could find in the fruit basket
(reduce ‘( )) Classical Notion of MapReduce in
Functional Programming
A list of values mapped into
another list of values, which gets
reduced into a single value
5. Sam got his first job in JuiceRUs for his
talent in making juice
Now, it’s not just one basket
but a whole container of fruits
Also, they produce a list of
juice types separately
NOT ENOUGH !!
But, Sam had just ONE
and ONE
Large data and list of values
for output
Wait!
6. Implemented a parallel version of his innovation
(<a, > , <o, > , <p, > , …)
Each input to a map is a list of <key, value> pairs
Each output of a map is a list of <key, value> pairs
(<a’, > , <o’, > , <p’, > , …)
Grouped by key
Each input to a reduce is a <key, value-list>
(possibly a list of these, depending on the
grouping/hashing mechanism)
e.g. <a’, ( …)>
Reduced into a list of values
7. Implemented a parallel version of his innovation
The idea of MapReduce in Data
Intensive Computing
A list of <key, value> pairs mapped
into another list of <key, value> pairs
which gets grouped by the key and
reduced into a list of values
8. Sam realized,
◦ To create his favorite mix fruit juice he can use a combiner after the
reducers
◦ If several <key, value-list> fall into the same group (based on the
grouping/hashing algorithm) then use the blender (reducer) separately on
each of them
◦ The knife (mapper) and blender (reducer) should not contain residue after
use – Side Effect Free
◦ In general reducer should be associative and commutative