A quick introduction to log aggregation in a local Docker development environment using Fluentd followed by a demonstration using a publicly available GitHub repo.
Presented at GDG Devfest Ukraine 2018.
Prometheus has become the defacto monitoring system for cloud native applications, with systems like Kubernetes and Etcd natively exposing Prometheus metrics. In this talk Tom will explore all the moving part for a working Prometheus-on-Kubernetes monitoring system, including kube-state-metrics, node-exporter, cAdvisor and Grafana. You will learn about the various methods for getting to a working setup: the manual approach, using CoreOS’s Prometheus Operator, or using Prometheus Ksonnet Mixin. Tom will also share some little tips and tricks for getting the most out of your Prometheus monitoring, including the common pitfalls and what you should be alerting on.
PerconaLive 2016 Santa Clara presentation on Hashicorp Vault with CTO Armon Dadger
https://www.percona.com/live/data-performance-conference-2016/sessions/using-vault-decouple-secrets-applications
Best Practices of Infrastructure as Code with TerraformDevOps.com
When your organization is moving to cloud, the infrastructure layer transitions from running dedicated servers at limited scale to a dynamic environment, where you can easily adjust to growing demand by spinning up thousands of servers and scaling them down when not in use.
The future of DevOps is infrastructure as code. Infrastructure as code supports the growth of infrastructure and provisioning requests. It treats infrastructure as software: code that can be re-used, tested, automated and version controlled. HashiCorp Terraform adopts infrastructure as code throughout its tool to prevent configuration drift, manage immutable infrastructure and much more!
Join this webinar to learn why Infrastructure as Code is the answer to managing large scale, distributed systems and service-oriented architectures. We will cover key use cases, a demo of how to use Infrastructure as Code to provision your infrastructure and more:
Agenda:
Intro to Infrastructure as Code: Challenges & Use cases
Writing Infrastructure as Code with Terraform
Collaborating with Teams on Infrastructure
Grafana Loki: like Prometheus, but for LogsMarco Pracucci
Loki is a horizontally-scalable, highly-available log aggregation system inspired by Prometheus. It is designed to be very cost-effective and easy to operate, as it does not index the contents of the logs, but rather labels for each log stream.
In this talk, we will introduce Loki, its architecture and the design trade-offs in an approachable way. We’ll both cover Loki and Promtail, the agent used to scrape local logs to push to Loki, including the Prometheus-style service discovery used to dynamically discover logs and attach metadata from applications running in a Kubernetes cluster.
Finally, we’ll show how to query logs with Grafana using LogQL - the Loki query language - and the latest Grafana features to easily build dashboards mixing metrics and logs.
This is a talk on how you can monitor your microservices architecture using Prometheus and Grafana. This has easy to execute steps to get a local monitoring stack running on your local machine using docker.
Presented at GDG Devfest Ukraine 2018.
Prometheus has become the defacto monitoring system for cloud native applications, with systems like Kubernetes and Etcd natively exposing Prometheus metrics. In this talk Tom will explore all the moving part for a working Prometheus-on-Kubernetes monitoring system, including kube-state-metrics, node-exporter, cAdvisor and Grafana. You will learn about the various methods for getting to a working setup: the manual approach, using CoreOS’s Prometheus Operator, or using Prometheus Ksonnet Mixin. Tom will also share some little tips and tricks for getting the most out of your Prometheus monitoring, including the common pitfalls and what you should be alerting on.
PerconaLive 2016 Santa Clara presentation on Hashicorp Vault with CTO Armon Dadger
https://www.percona.com/live/data-performance-conference-2016/sessions/using-vault-decouple-secrets-applications
Best Practices of Infrastructure as Code with TerraformDevOps.com
When your organization is moving to cloud, the infrastructure layer transitions from running dedicated servers at limited scale to a dynamic environment, where you can easily adjust to growing demand by spinning up thousands of servers and scaling them down when not in use.
The future of DevOps is infrastructure as code. Infrastructure as code supports the growth of infrastructure and provisioning requests. It treats infrastructure as software: code that can be re-used, tested, automated and version controlled. HashiCorp Terraform adopts infrastructure as code throughout its tool to prevent configuration drift, manage immutable infrastructure and much more!
Join this webinar to learn why Infrastructure as Code is the answer to managing large scale, distributed systems and service-oriented architectures. We will cover key use cases, a demo of how to use Infrastructure as Code to provision your infrastructure and more:
Agenda:
Intro to Infrastructure as Code: Challenges & Use cases
Writing Infrastructure as Code with Terraform
Collaborating with Teams on Infrastructure
Grafana Loki: like Prometheus, but for LogsMarco Pracucci
Loki is a horizontally-scalable, highly-available log aggregation system inspired by Prometheus. It is designed to be very cost-effective and easy to operate, as it does not index the contents of the logs, but rather labels for each log stream.
In this talk, we will introduce Loki, its architecture and the design trade-offs in an approachable way. We’ll both cover Loki and Promtail, the agent used to scrape local logs to push to Loki, including the Prometheus-style service discovery used to dynamically discover logs and attach metadata from applications running in a Kubernetes cluster.
Finally, we’ll show how to query logs with Grafana using LogQL - the Loki query language - and the latest Grafana features to easily build dashboards mixing metrics and logs.
This is a talk on how you can monitor your microservices architecture using Prometheus and Grafana. This has easy to execute steps to get a local monitoring stack running on your local machine using docker.
Prometheus - Intro, CNCF, TSDB,PromQL,GrafanaSridhar Kumar N
https://www.youtube.com/playlist?list=PLAiEy9H6ItrKC5PbH7KiELiSEIKv3tuov
-What is Prometheus?
-Difference Between Nagios vs Prometheus
-Architecture
-Alertmanager
-Time series DB
-PromQL (Prometheus Query Language)
-Live Demo
-Grafana
An introduction to Linux Container, Namespace & Cgroup.
Virtual Machine, Linux operating principles. Application constraint execution environment. Isolate application working environment.
VictoriaLogs: Open Source Log Management System - PreviewVictoriaMetrics
VictoriaLogs Preview - Aliaksandr Valialkin
* Existing open source log management systems
- ELK (ElasticSearch) stack: Pros & Cons
- Grafana Loki: Pros & Cons
* What is VictoriaLogs
- Open source log management system from VictoriaMetrics
- Easy to setup and operate
- Scales vertically and horizontally
- Optimized for low resource usage (CPU, RAM, disk space)
- Accepts data from Logstash and Fluentbit in Elasticsearch format
- Accepts data from Promtail in Loki format
- Supports stream concept from Loki
- Provides easy to use yet powerful query language - LogsQL
* LogsQL Examples
- Search by time
- Full-text search
- Combining search queries
- Searching arbitrary labels
* Log Streams
- What is a log stream?
- LogsQL examples: querying log streams
- Stream labels vs log labels
* LogsQL: stats over access logs
* VictoriaLogs: CLI Integration
* VictoriaLogs Recap
Github Actions enables you to create custom software development lifecycle workflows directly in your Github repository. These workflows are made out of different tasks so-called actions that can be run automatically on certain events.
Prometheus Design and Philosophy by Julius Volz at Docker Distributed System Summit
Prometheus - https://github.com/Prometheus
Liveblogging: http://canopy.mirage.io/Liveblog/MonitoringDDS2016
Using HashiCorp’s Terraform to build your infrastructure on AWS - Pop-up Loft...Amazon Web Services
Using Terraform to automate your infrastructure on AWS. What is Terraform and how is it different from Ansible. How to control cloud deployments using Terraform.
Data Warehouses in Kubernetes Visualized: the ClickHouse Kubernetes Operator UIAltinity Ltd
Graham Mainwaring and Robert Hodges summarize management of ClickHouse on Kubernetes using the ClickHouse Kubernetes Operator and introduce a new UI for it. Presented at the 15 Dec '22 SF Bay Area ClickHouse Meetup.
MeetUp Monitoring with Prometheus and Grafana (September 2018)Lucas Jellema
This presentation introduces the concept of monitoring - focusing on why and how and finally on the tools to use. It introduces Prometheus (metrics gathering, processing, alerting), application instrumentation and Prometheus exporters and finally it introduces Grafana as a common companion for dashboarding, alerting and notifications. This presentations also introduces the handson workshop - for which materials are available from https://github.com/lucasjellema/monitoring-workshop-prometheus-grafana
Evolving to serverless
How the applications are transforming
A note on CI/CD
Architecture of Docker
Setting up a docker environment
Deep dive into DockerFile and containers
Tagging and publishing an image to docker hub
A glimpse from session one
Services: scale our application and enable load-balancing
Swarm: Deploying application onto a cluster, running it on multiple machines
Stack: A stack is a group of interrelated services that share dependencies, and can be orchestrated and scaled together.
Deploy your app: Compose file works just as well in production as it does on your machine.
Extras: Containers and VMs together
No production system is complete without a way to monitor it. In software, we define observability as the ability to understand how our system is performing. This talk dives into capabilities and tools that are recommended for implementing observability when running K8s in production as the main platform today for deploying and maintaining containers with cloud-native solutions.
We start by introducing the concept of observability in the context of distributed systems such as K8s and the difference with monitoring. We continue by reviewing the observability stack in K8s and the main functionalities. Finally, we will review the tools K8s provides for monitoring and logging, and get metrics from applications and infrastructure.
Between the points to be discussed we can highlight:
-Introducing the concept of observability
-Observability stack in K8s
-Tools and apps for implementing Kubernetes observability
-Integrating Prometheus with OpenMetrics
Prometheus - Intro, CNCF, TSDB,PromQL,GrafanaSridhar Kumar N
https://www.youtube.com/playlist?list=PLAiEy9H6ItrKC5PbH7KiELiSEIKv3tuov
-What is Prometheus?
-Difference Between Nagios vs Prometheus
-Architecture
-Alertmanager
-Time series DB
-PromQL (Prometheus Query Language)
-Live Demo
-Grafana
An introduction to Linux Container, Namespace & Cgroup.
Virtual Machine, Linux operating principles. Application constraint execution environment. Isolate application working environment.
VictoriaLogs: Open Source Log Management System - PreviewVictoriaMetrics
VictoriaLogs Preview - Aliaksandr Valialkin
* Existing open source log management systems
- ELK (ElasticSearch) stack: Pros & Cons
- Grafana Loki: Pros & Cons
* What is VictoriaLogs
- Open source log management system from VictoriaMetrics
- Easy to setup and operate
- Scales vertically and horizontally
- Optimized for low resource usage (CPU, RAM, disk space)
- Accepts data from Logstash and Fluentbit in Elasticsearch format
- Accepts data from Promtail in Loki format
- Supports stream concept from Loki
- Provides easy to use yet powerful query language - LogsQL
* LogsQL Examples
- Search by time
- Full-text search
- Combining search queries
- Searching arbitrary labels
* Log Streams
- What is a log stream?
- LogsQL examples: querying log streams
- Stream labels vs log labels
* LogsQL: stats over access logs
* VictoriaLogs: CLI Integration
* VictoriaLogs Recap
Github Actions enables you to create custom software development lifecycle workflows directly in your Github repository. These workflows are made out of different tasks so-called actions that can be run automatically on certain events.
Prometheus Design and Philosophy by Julius Volz at Docker Distributed System Summit
Prometheus - https://github.com/Prometheus
Liveblogging: http://canopy.mirage.io/Liveblog/MonitoringDDS2016
Using HashiCorp’s Terraform to build your infrastructure on AWS - Pop-up Loft...Amazon Web Services
Using Terraform to automate your infrastructure on AWS. What is Terraform and how is it different from Ansible. How to control cloud deployments using Terraform.
Data Warehouses in Kubernetes Visualized: the ClickHouse Kubernetes Operator UIAltinity Ltd
Graham Mainwaring and Robert Hodges summarize management of ClickHouse on Kubernetes using the ClickHouse Kubernetes Operator and introduce a new UI for it. Presented at the 15 Dec '22 SF Bay Area ClickHouse Meetup.
MeetUp Monitoring with Prometheus and Grafana (September 2018)Lucas Jellema
This presentation introduces the concept of monitoring - focusing on why and how and finally on the tools to use. It introduces Prometheus (metrics gathering, processing, alerting), application instrumentation and Prometheus exporters and finally it introduces Grafana as a common companion for dashboarding, alerting and notifications. This presentations also introduces the handson workshop - for which materials are available from https://github.com/lucasjellema/monitoring-workshop-prometheus-grafana
Evolving to serverless
How the applications are transforming
A note on CI/CD
Architecture of Docker
Setting up a docker environment
Deep dive into DockerFile and containers
Tagging and publishing an image to docker hub
A glimpse from session one
Services: scale our application and enable load-balancing
Swarm: Deploying application onto a cluster, running it on multiple machines
Stack: A stack is a group of interrelated services that share dependencies, and can be orchestrated and scaled together.
Deploy your app: Compose file works just as well in production as it does on your machine.
Extras: Containers and VMs together
No production system is complete without a way to monitor it. In software, we define observability as the ability to understand how our system is performing. This talk dives into capabilities and tools that are recommended for implementing observability when running K8s in production as the main platform today for deploying and maintaining containers with cloud-native solutions.
We start by introducing the concept of observability in the context of distributed systems such as K8s and the difference with monitoring. We continue by reviewing the observability stack in K8s and the main functionalities. Finally, we will review the tools K8s provides for monitoring and logging, and get metrics from applications and infrastructure.
Between the points to be discussed we can highlight:
-Introducing the concept of observability
-Observability stack in K8s
-Tools and apps for implementing Kubernetes observability
-Integrating Prometheus with OpenMetrics
Black, Flake8, isort, and Mypy are useful Python linters but it’s challenging to use them effectively at scale in the case of multiple codebases, in a large codebase, or with many developers. Manually managing consistent linter versions and configurations across codebases requires endless effort. Linter analysis on large codebases is slow. Linters may slow down developers by asking them to fix trivial issues. Running linters in distributed CI jobs makes it hard to understand the overall developer experience.
To handle these scale challenges, we developed a reusable linter framework that releases new linter updates automatically, reuses consistent configurations, runs linters on only updated code to speedup runtime, collects logs and metrics to provide observability, and builds auto fixes for common linter issues. Our linter runs are fast and scalable. Every week, they run 10k times on multiple millions of lines of code in over 25 codebases, generating 25k suggestions for more than 200 developers. Its autofixes also save 20 hours of developer time every week.
In this talk, we’ll walk you through popular Python linters and configuration recommendations, and we will discuss common issues and solutions when scaling them out. Using linters more effectively will make it much easier for you to apply best practices and more quickly write better code.
Everyone heard about Kubernetes. Everyone wants to use this tool. However, sometimes we forget about security, which is essential throughout the container lifecycle.
Therefore, our journey with Kubernetes security should begin in the build stage when writing the code becomes the container image.
Kubernetes provides innate security advantages, and together with solid container protection, it will be invincible.
During the sessions, we will review all those features and highlight which are mandatory to use. We will discuss the main vulnerabilities which may cause compromising your system.
Contacts:
LinkedIn - https://www.linkedin.com/in/vshynkar/
GitHub - https://github.com/sqerison
-------------------------------------------------------------------------------------
Materials from the video:
The policies and docker files examples:
https://gist.github.com/sqerison/43365e30ee62298d9757deeab7643a90
The repo with the helm chart used in a demo:
https://github.com/sqerison/argo-rollouts-demo
Tools that showed in the last section:
https://github.com/armosec/kubescape
https://github.com/aquasecurity/kube-bench
https://github.com/controlplaneio/kubectl-kubesec
https://github.com/Shopify/kubeaudit#installation
https://github.com/eldadru/ksniff
Further learning.
A book released by CISA (Cybersecurity and Infrastructure Security Agency):
https://media.defense.gov/2021/Aug/03/2002820425/-1/-1/1/CTR_KUBERNETES%20HARDENING%20GUIDANCE.PDF
O`REILLY Kubernetes Security:
https://kubernetes-security.info/
O`REILLY Container Security:
https://info.aquasec.com/container-security-book
Thanks for watching!
Distributed Logging Architecture in the Container EraGlenn Davis
Presentation given at LinuxCon Japan 2016 by Satoshi "Moris" Tagomori (@tagomoris), Treasure Data. Describes various strategies for aggregating log data in a microservices architecture using containers, e.g. Docker.
Netflix Open Source: Building a Distributed and Automated Open Source Programaspyker
Netflix has been using and contributing to open source for several years. Over the years, Netflix has released over one hundred Netflix Open Source (aka NetflixOSS) libraries, servers, and technologies. Netflix engineers benefit by accepting contributions and gathering feedback with key collaborators around the world. Users of NetflixOSS from many industries benefit from our solutions including Big Data, Build and Delivery Tools, Runtime Services and Libraries, Data Persistence, Insight, Reliability and Performance, Security and User Interface. With such a large and mature open source program, Netflix has worked on approaches and tools that help manage and improve the NetflixOSS source offerings and communities. Netflix has taken a different approach to building support for open source as compared to other Internet scale companies. Come to this session to learn about the unique approaches Netflix has taken to both distribute and automate the responsibilities of building a world-class open source program.
Building a Distributed & Automated Open Source Program at NetflixAll Things Open
Andrew Spyker
Senior Software Engineer for Netflix
Find more by Andrew Spyker: http://www.slideshare.net/aspyker
All Things Open
October 26-27, 2016
Raleigh, North Carolina
This slide was delivered at the Bay Area In-Memory Computing meetup in California on how TiDB, an open source NewSQL distributed database, is deployed and managed on any Kubernetes-enabled cloud environment by applying the Operator pattern.
A TRUE STORY ABOUT DATABASE ORCHESTRATIONInfluxData
During this talk, Gianluca will share the architecture of the project, describe the criticalities of the infrastructure and how the team strives to make this powerful service secure, fast, and reliable for all customers using InfluxCloud.
Systems have fundamentally changed with the introduction and adoption of micro services like Docker, resulting in a shift in how we think about log management and analysis. Logging into a server and grepping logs is no longer a reality when dealing with thousands of container instances. While support of logging has improved on Docker over the past year, there is yet to be a widely agreed upon “standard” for Docker.
Logentries Chief Scientist Trevor Parsons recently co-hosted a webinar with Peter Elger of nearForm and Bright Fulton of Swipely where we explored three different approaches to logging on Docker, including:
Logging from within the container using a Daemon/collector
Logging outside the container, writing logs out and running a collector on the host
Using a dedicated logging container responsible for collecting and forwarding logs
Over the course of the webinar, Trevor, Peter and Bright engaged in a lively discussion, exploring the pros and cons of each option and debating which one truly represents “best practice.”They also shared insights into common events that are valuable to log, monitor and analyze,, useful Docker APIs, and services for analyzing Docker log data for a deeper understanding of your application and environments.
Redundancy and high availability are the basis for all production deployments. Database systems with large data sets or high throughput applications can challenge the capacity of a single server like CPU for high query rates or RAM for large working sets. Adding more CPU and RAM for vertical scaling is limited. Systems need horizontal scaling by distributing data across multiple servers. MongoDB supports horizontal scaling through sharding.
Integrating Existing C++ Libraries into PySpark with Esther KundinDatabricks
Bloomberg’s Machine Learning/Text Analysis team has developed many machine learning libraries for fast real-time sentiment analysis of incoming news stories. These models were developed using smaller training sets, implemented in C++ for minimal latency, and are currently running in production. To facilitate backtesting our production models across our full data set, we needed to be able to parallelize our workloads, while using the actual production code.
We also wanted to integrate the C++ code with PySpark and use it to run our models. In this talk, I will discuss some of the challenges we faced, decisions we made, and other options when dealing with integrating existing C++ code into a Spark system. The techniques we developed have been used successfully by our team multiple times and I am sure others will benefit from the gotchas that we were able to identify.
Luciano Resende - Scaling Big Data Interactive Workloads across Kubernetes Cl...Codemotion
The Jupyter Notebook Stack has become the "de facto" platform used by data scientists to interactively work on big data problems. With the popularity of deep learning, there is also an increasing need for resources to make deep learning effective. In this session, we will discuss how we brought support for Kubernetes into Jupyter Enterprise Gateway and touch on some best practices on how to scale an interactive big data workloads across a Kubernets managed cluster.
Provisioning Windows instances at scale on Azure, AWS and OpenStack - Adrian ...ITCamp
In a cloud based environment, where automation is a primary concern, guest operating systems need to be provisioned at boot time.
There are a lot of actions that need to be performed at this stage, ranging from assigning the admin user’s credentials to creating WinRM listeners, storage configurations, RDP settings, guest agent installation, custom data execution and much more.
The de-facto standard guest provisioning tools are cloud-init on Linux and cloudbase-init on Windows.
I will present how cloudbase-init runs on all the Microsoft supported Windows editions (there are quite a few) and how it supports a plethora of metadata service implementations (EC2, OpenStack, the recently added Azure).
Cloudbase-init is being run thousands of times daily all over the world’s public clouds and data centers and it has reached more than 5 million known runs to date.
We will also take an in-depth look at the Argus integration testing framework, which automates the integration testing of cloudbase-init on real world platforms, to make sure it meets a very strict set of performance, compatibility and security requirements.
At the end I will show you a live demo with a cloudbase-init bootstrapped Windows instance on Azure, and how you can benefit from the provisioning process.
At Opendoor, we do a lot of big data processing, and use Spark and Dask clusters for the computations. Our machine learning platform is written in Dask and we are actively moving data ingestion pipelines and geo computations to PySpark. The biggest challenge is that jobs vary in memory, cpu needs, and the load in not evenly distributed over time, which causes our workers and clusters to be over-provisioned. In addition to this, we need to enable data scientists and engineers run their code without having to upgrade the cluster for every request and deal with the dependency hell.
To solve all of these problems, we introduce a lightweight integration across some popular tools like Kubernetes, Docker, Airflow and Spark. Using a combination of these tools, we are able to spin up on-demand Spark and Dask clusters for our computing jobs, bring down the cost using autoscaling and spot pricing, unify DAGs across many teams with different stacks on the single Airflow instance, and all of it at minimal cost.
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!
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
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
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.
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
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
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.
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.
17. Any Questions?
Project is available publicly on GitHub
github.com/deploymentking/efk
Please feel free to contact me via...
mail@thinkstack.io
twitter.com/thinkstackio
linkedin.com/in/leemyring
