This document provides an overview of Docker, ECS, and how they can be used together on AWS. It defines Docker as application virtualization using containers that package code, runtime, and dependencies. ECS is AWS's container orchestration service that allows running Docker containers across a cluster, providing scheduling, networking, scaling, and reliability. The document outlines key aspects of using ECS including task definitions that specify container configurations, services that maintain a desired number of tasks, and load balancers for exposing applications. It also provides details on how ECS leverages underlying AWS resources and orchestrates tasks and services behind the scenes.
Automating Application over OpenStack using WorkflowsYaron Parasol
This document discusses automating DevOps processes through orchestration and workflows. It introduces Petsy, a pet art company that needs to automate deployments. Common DevOps workflows like deployment, infrastructure upgrades, and scaling are described. The document then introduces the Cloudify project which uses TOSCA-inspired building blocks like nodes, relationships, and workflows to automate application topology deployment and management across clouds. A live demo of automatically deploying a Mezzanine application is shown. The document concludes by discussing how Cloudify integrates with the OpenStack ecosystem through the Solum project.
This document discusses Amazon Web Services and the different types of cloud computing services it offers including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It provides examples of specific Amazon Web Services offerings within each category such as Elastic Compute Cloud (EC2), Simple Storage Service (S3), and Elastic MapReduce. It also outlines some key advantages of using Amazon Web Services like strong capacity, big storage, high reliability, low cost, high portability, strong scalability, and that it offers a complete set of services.
Design Patterns for Distributed Systems in Azure Kubernetes ServiceStefano Tempesta
Containers and container orchestrators have fundamentally changed the way we look at distributed systems. When in the past developers had to build these systems nearly from scratch, resulting in each architecture be sort of unique and not repeatable, we now have infrastructure and interface elements for designing and deploying services and application on distributed systems using reusable patterns for micro-service architectures and containerized components. This session describes implementation design patterns for deployment of containerized applications on Azure Kubernetes Service (AKS) that meet requirements for availability, reliability and scalability. Among the patterns presented, along with practical code samples for AKS, there is Replicated Load-Balanced Services, Sharded Services and the Scatter Gather pattern.
This document discusses using k-means clustering in Spark to detect device anomalies based on device feature data. It provides an example of device data with attributes like battery percentage and RAM usage. It also shows example Scala code to perform k-means clustering on this data, including normalizing the data first before clustering. The results show data points clustered and predictions assigned.
The document discusses running distributed applications like Spark on Kubernetes. It provides an overview of Kubernetes concepts like nodes, services and pods. It then demonstrates running Spark on Kubernetes by submitting a Spark job and shows how the application scales by increasing the number of pods. It explains that the Kubernetes submission client translates spark-submit options to Kubernetes API resources and that the CoarseGrainedSchedulerBackend is used to schedule executors on pods.
This document outlines steps to configure a Lambda function to send logs and events to Splunk Cloud in real-time. It involves setting up a Splunk index and HTTP Event Collector (HEC), creating an HEC token, and modifying the source type. A standalone Splunk Lambda function is created that can be invoked by other application Lambda functions to log events to Splunk Cloud. The application Lambda is modified to invoke the Splunk Lambda after starting EC2 instances to log instance details.
This document provides an overview of Docker, ECS, and how they can be used together on AWS. It defines Docker as application virtualization using containers that package code, runtime, and dependencies. ECS is AWS's container orchestration service that allows running Docker containers across a cluster, providing scheduling, networking, scaling, and reliability. The document outlines key aspects of using ECS including task definitions that specify container configurations, services that maintain a desired number of tasks, and load balancers for exposing applications. It also provides details on how ECS leverages underlying AWS resources and orchestrates tasks and services behind the scenes.
Automating Application over OpenStack using WorkflowsYaron Parasol
This document discusses automating DevOps processes through orchestration and workflows. It introduces Petsy, a pet art company that needs to automate deployments. Common DevOps workflows like deployment, infrastructure upgrades, and scaling are described. The document then introduces the Cloudify project which uses TOSCA-inspired building blocks like nodes, relationships, and workflows to automate application topology deployment and management across clouds. A live demo of automatically deploying a Mezzanine application is shown. The document concludes by discussing how Cloudify integrates with the OpenStack ecosystem through the Solum project.
This document discusses Amazon Web Services and the different types of cloud computing services it offers including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It provides examples of specific Amazon Web Services offerings within each category such as Elastic Compute Cloud (EC2), Simple Storage Service (S3), and Elastic MapReduce. It also outlines some key advantages of using Amazon Web Services like strong capacity, big storage, high reliability, low cost, high portability, strong scalability, and that it offers a complete set of services.
Design Patterns for Distributed Systems in Azure Kubernetes ServiceStefano Tempesta
Containers and container orchestrators have fundamentally changed the way we look at distributed systems. When in the past developers had to build these systems nearly from scratch, resulting in each architecture be sort of unique and not repeatable, we now have infrastructure and interface elements for designing and deploying services and application on distributed systems using reusable patterns for micro-service architectures and containerized components. This session describes implementation design patterns for deployment of containerized applications on Azure Kubernetes Service (AKS) that meet requirements for availability, reliability and scalability. Among the patterns presented, along with practical code samples for AKS, there is Replicated Load-Balanced Services, Sharded Services and the Scatter Gather pattern.
This document discusses using k-means clustering in Spark to detect device anomalies based on device feature data. It provides an example of device data with attributes like battery percentage and RAM usage. It also shows example Scala code to perform k-means clustering on this data, including normalizing the data first before clustering. The results show data points clustered and predictions assigned.
The document discusses running distributed applications like Spark on Kubernetes. It provides an overview of Kubernetes concepts like nodes, services and pods. It then demonstrates running Spark on Kubernetes by submitting a Spark job and shows how the application scales by increasing the number of pods. It explains that the Kubernetes submission client translates spark-submit options to Kubernetes API resources and that the CoarseGrainedSchedulerBackend is used to schedule executors on pods.
This document outlines steps to configure a Lambda function to send logs and events to Splunk Cloud in real-time. It involves setting up a Splunk index and HTTP Event Collector (HEC), creating an HEC token, and modifying the source type. A standalone Splunk Lambda function is created that can be invoked by other application Lambda functions to log events to Splunk Cloud. The application Lambda is modified to invoke the Splunk Lambda after starting EC2 instances to log instance details.
Major Managed Kubernetes Platforms Comparison (AWS, GCP, Azure)GlobalLogic Ukraine
This presentation by Andriy Kopachevskyy (Engineering Consultant, GlobalLogic) was delivered at GlobalLogic Kharkiv DevOps TechTalk #1 on October 8, 2019.
Andriy talked about basic Kubernetes services available "out of the box", conducted a comparative analysis of the cost of Kubernetes infrastructure. He drew the audience's attention to the ease of deployment, use and support of Kubernetes, talked about its integration with other cloud service providers, as well as monitoring and logging in to Kubernetes.
Event materials: https://www.globallogic.com/ua/events/kharkiv-devops-techtalk-1/
This weekly project update provides information on:
1) Server rack installations and expansions across multiple availability zones.
2) Approval of a first expansion and progress on an AMS/SG expansion.
3) Receipt of shipments and installation of expansion servers.
4) Accomplishments including AWS link balancing and new file download services.
5) Tasks in progress like additional link installations and cable work.
6) Upcoming tasks including additional AWS links through multiple providers.
Nils Mohr & Jake Pearce - 100 years of flight data at British Airways. Past, ...AWSCOMSUM
This document discusses British Airways' use of flight data and analytics over the past 100 years and their plans to expand use of cloud technologies like AWS. It outlines their transition from on-premise storage and processing to using AWS services like S3, Lambda, Fargate, API Gateway, and Aurora. It discusses challenges faced with infrastructure management and the benefits of serverless architectures. The document envisions fully integrating legacy systems with cloud platforms and expanding use of machine learning through services like SageMaker to power business decisions using their vast trove of operational flight data.
Big data Lambda Architecture - Batch Layer Hands Onhkbhadraa
Big Data Batch Layer implementation with Amazon Web Services Cloud Platform, Apache Spark, Hadoop, Apache Cassandra, AngularJS, Java Restful Web Services. This can be extended to implement real world use cases.
The document discusses using Terraform and Atlantis for infrastructure as code on AWS. It provides an overview of Terraform and how it can be used to define AWS resources like S3 buckets. It then introduces Atlantis as a tool that integrates with version control systems like GitHub to run Terraform plans on pull requests and apply changes once approved. Key features of Atlantis mentioned are that it allows pull request based planning and applying of Terraform code.
Scale search powered apps with Elastisearch, k8s and go - Maxime BoisvertWeb à Québec
This document discusses the speaker's experience scaling Elasticsearch clusters on Kubernetes. It provides an overview of:
1) The speaker's background working with search infrastructure at Shopify, moving applications to Google Cloud and growing Elasticsearch clusters.
2) Concepts for running Elasticsearch on Kubernetes including StatefulSets, Deployments, Services and configuration.
3) Automating Elasticsearch operations like rolling updates, scaling, and snapshots using custom Kubernetes components.
4) How the speaker developed a "search operator" product to manage Elasticsearch clusters internally at Shopify in a standardized way with good practices.
This document discusses Spotify's use of Puppet infrastructure to manage over 3,500 nodes across multiple data centers. Spotify has been using Puppet for over 2.5 years, with more than 300 changes per month contributed by 137 committers. Puppet is used to deploy over 240 modules and backend services built as Debian packages. Node classification is currently done using different external node classifiers and environments in different Puppet installations, but will be switching to using Hiera. Service discovery utilizes SRV records looked up via a Puppet module, and a system called WASD ingests information about hosts and services across multiple sites via a REST API.
This document introduces and summarizes Amazon S3 and EC2 cloud computing services. S3 provides scalable cloud storage, while EC2 allows users to launch virtual Linux servers on demand. Both services offer competitive pricing and ease of use through web interfaces and APIs. However, EC2 instances have limitations such as non-persistent local storage and hourly billing.
This document provides instructions for using Ansible to manage Google Compute Engine (GCE) instances. It outlines the steps to set up GCE instances, generate service account keys, configure Ansible inventory files to connect to GCE, and run playbooks to manage hosts on GCE. Additional resources are also referenced for more details on integrating Ansible with GCE, managing SSH keys, service accounts, and the demo GitHub repository.
Orchestrating workflows Apache Airflow on GCP & AWSDerrick Qin
Working in a cloud or on-premises environment, we all somehow move data from A to B on-demand or on schedule. It is essential to have a tool that can automate recurring workflows. This can be anything from an ETL(Extract, Transform, and Load) job for a regular analytics report all the way to automatically re-training a machine learning model.
In this talk, we will introduce Apache Airflow and how it can help orchestrate your workflows. We will cover key concepts, features, and use cases of Apache Airflow, as well as how you can enjoy Apache Airflow on GCP and AWS by demo-ing a few practical workflows.
This document discusses Elasticsearch and compares it to Keboola. It mentions that Keboola Connection provides storage, API, console, extractors, and event handling for MySQL and Elasticsearch. It also discusses Keboola's provisioning of AWS resources using Cloudformation templates and Chef for installing software and configuring instances.
Google Cloud Platform - Eric Johnson, Joe Selman - ManageIQ Design Summit 2016ManageIQ
This document summarizes a presentation given by Joe Selman and Eric Johnson of Google Cloud Platform to the ManageIQ Design Summit in June 2016. It introduces Joe and Eric, discusses Cloud Graphite and Google's support for open source software. It then details Joe's journey learning Ruby and contributing to ManageIQ, including adding support for the Google Cloud Platform. The document concludes with an overview of the Google Cloud Platform and features of Google Compute Engine.
The document discusses a talk titled "Go With the Flow, Understanding Windows Workflow Foundation" which will explain what workflows are, the features of Windows Workflow Foundation, and demonstrate how it can be used to model common business processes like an estate agent managing property sales. It will cover the core concepts of workflows and activities, how to design workflows with Visual Studio and XAML, and some of the advanced features like rules engines and state machines. The talk is intended to provide enough information for attendees to understand if Workflow Foundation could benefit their projects.
Reason meets OCaml
- Reason is a syntax extension to OCaml that compiles to efficient JavaScript. It has a rock solid type system and access to the OCaml ecosystem.
- ReasonReact provides React bindings for Reason. Components are defined in a similar way to TypeScript but use Reason's type system and compiler.
- Examples show how stateless and stateful components are defined in ReasonReact compared to TypeScript. ReasonReact uses OCaml's type system and reducer pattern while TypeScript uses interfaces and state.
- Interoperability with JavaScript is supported through modules, raw JavaScript blocks, and using the platform. Reason aims to leverage the strengths of both OCaml and React.
Google Cloud Computing compares GCE, GAE and GKESimon Su
Google Cloud Computing compares GCE, GKE and GAE. GCE provides raw compute, storage and networking resources and requires more management overhead. GAE focuses on application logic and requires less management. GKE offers managed Kubernetes infrastructure and services. Each option has different strengths for workloads like microservices, containerized services, or large-scale applications requiring quick scaling. Monitoring and management features like Stackdriver are also compared.
Plaλ (pronounced 'plambda') is an approach to make unmodified Play apps work using AWS lambda. Judged from the outset as a comedic attempt destined to fail, the results are surprisingly good - though far from perfect and some way from usable. No matter - it's a great lesson in AWS lambda's limitations and how this technique could be applied to web frameworks in order to accelerate the move to serverless.
Intro to the Google Cloud for DevelopersLynn Langit
This document provides an introduction to developing applications on Google Cloud. It discusses Google's cloud infrastructure and services like Compute Engine and App Engine. It demonstrates how to use the Google Cloud SDK and APIs to manage resources and build applications using various languages and tools. Specifically, it shows how to create instances in Compute Engine and Big Query, deploy applications to App Engine from Eclipse, and use command line tools to manage storage, databases and other services.
This document provides a step-by-step guide to using Docker with data including installing boot2docker, using Dockerfiles and docker-compose files, and discusses Docker concepts from 10,000 feet including containers, union filesystems, and deploying with Kubernetes. It also discusses using Docker for data including loading data from databases into Docker, potential issues like slow performance if not configured carefully, and the tradeoffs of using Docker including reduced flexibility for added convenience.
Getting Predictable - Pragmatic Approach for Mobile Development - Devday.lk ...Anjana Somathilake
Getting Predictable - Pragmatic Approach for Mobile Development by Anjana Somathilake
Are you been challenged by…
Aggressive timelines, increasing team size, and a QA team that demands stability?
An ever growing defect count that impacts the release date?
A "Fix one thing, break another" syndrome?
A UX that is still unknown while the product team is asking for stats.
Stakeholders that instantly need to see beta versions installed on their phones.
Random app crashes (in front of the CEO &/or Paying Clients) and no one knows why?
3rd party API changes and the team doesn’t find out till after the app submission
Achieve Predictable Delivery in Mobile Application Development by
Automating the When & How using…
Acceptance Testing with Calabash
Builds, Unit Tests and Code Quality with Jenkins CI, OCUnit and Clang Static Analyzer
App Distribution with TestFlight
And Implementing Insight and Analytics with…
Real-time Crash Reports and Analytics through Crashlytics
Actions and Events Tracking with Mixpanel
Major Managed Kubernetes Platforms Comparison (AWS, GCP, Azure)GlobalLogic Ukraine
This presentation by Andriy Kopachevskyy (Engineering Consultant, GlobalLogic) was delivered at GlobalLogic Kharkiv DevOps TechTalk #1 on October 8, 2019.
Andriy talked about basic Kubernetes services available "out of the box", conducted a comparative analysis of the cost of Kubernetes infrastructure. He drew the audience's attention to the ease of deployment, use and support of Kubernetes, talked about its integration with other cloud service providers, as well as monitoring and logging in to Kubernetes.
Event materials: https://www.globallogic.com/ua/events/kharkiv-devops-techtalk-1/
This weekly project update provides information on:
1) Server rack installations and expansions across multiple availability zones.
2) Approval of a first expansion and progress on an AMS/SG expansion.
3) Receipt of shipments and installation of expansion servers.
4) Accomplishments including AWS link balancing and new file download services.
5) Tasks in progress like additional link installations and cable work.
6) Upcoming tasks including additional AWS links through multiple providers.
Nils Mohr & Jake Pearce - 100 years of flight data at British Airways. Past, ...AWSCOMSUM
This document discusses British Airways' use of flight data and analytics over the past 100 years and their plans to expand use of cloud technologies like AWS. It outlines their transition from on-premise storage and processing to using AWS services like S3, Lambda, Fargate, API Gateway, and Aurora. It discusses challenges faced with infrastructure management and the benefits of serverless architectures. The document envisions fully integrating legacy systems with cloud platforms and expanding use of machine learning through services like SageMaker to power business decisions using their vast trove of operational flight data.
Big data Lambda Architecture - Batch Layer Hands Onhkbhadraa
Big Data Batch Layer implementation with Amazon Web Services Cloud Platform, Apache Spark, Hadoop, Apache Cassandra, AngularJS, Java Restful Web Services. This can be extended to implement real world use cases.
The document discusses using Terraform and Atlantis for infrastructure as code on AWS. It provides an overview of Terraform and how it can be used to define AWS resources like S3 buckets. It then introduces Atlantis as a tool that integrates with version control systems like GitHub to run Terraform plans on pull requests and apply changes once approved. Key features of Atlantis mentioned are that it allows pull request based planning and applying of Terraform code.
Scale search powered apps with Elastisearch, k8s and go - Maxime BoisvertWeb à Québec
This document discusses the speaker's experience scaling Elasticsearch clusters on Kubernetes. It provides an overview of:
1) The speaker's background working with search infrastructure at Shopify, moving applications to Google Cloud and growing Elasticsearch clusters.
2) Concepts for running Elasticsearch on Kubernetes including StatefulSets, Deployments, Services and configuration.
3) Automating Elasticsearch operations like rolling updates, scaling, and snapshots using custom Kubernetes components.
4) How the speaker developed a "search operator" product to manage Elasticsearch clusters internally at Shopify in a standardized way with good practices.
This document discusses Spotify's use of Puppet infrastructure to manage over 3,500 nodes across multiple data centers. Spotify has been using Puppet for over 2.5 years, with more than 300 changes per month contributed by 137 committers. Puppet is used to deploy over 240 modules and backend services built as Debian packages. Node classification is currently done using different external node classifiers and environments in different Puppet installations, but will be switching to using Hiera. Service discovery utilizes SRV records looked up via a Puppet module, and a system called WASD ingests information about hosts and services across multiple sites via a REST API.
This document introduces and summarizes Amazon S3 and EC2 cloud computing services. S3 provides scalable cloud storage, while EC2 allows users to launch virtual Linux servers on demand. Both services offer competitive pricing and ease of use through web interfaces and APIs. However, EC2 instances have limitations such as non-persistent local storage and hourly billing.
This document provides instructions for using Ansible to manage Google Compute Engine (GCE) instances. It outlines the steps to set up GCE instances, generate service account keys, configure Ansible inventory files to connect to GCE, and run playbooks to manage hosts on GCE. Additional resources are also referenced for more details on integrating Ansible with GCE, managing SSH keys, service accounts, and the demo GitHub repository.
Orchestrating workflows Apache Airflow on GCP & AWSDerrick Qin
Working in a cloud or on-premises environment, we all somehow move data from A to B on-demand or on schedule. It is essential to have a tool that can automate recurring workflows. This can be anything from an ETL(Extract, Transform, and Load) job for a regular analytics report all the way to automatically re-training a machine learning model.
In this talk, we will introduce Apache Airflow and how it can help orchestrate your workflows. We will cover key concepts, features, and use cases of Apache Airflow, as well as how you can enjoy Apache Airflow on GCP and AWS by demo-ing a few practical workflows.
This document discusses Elasticsearch and compares it to Keboola. It mentions that Keboola Connection provides storage, API, console, extractors, and event handling for MySQL and Elasticsearch. It also discusses Keboola's provisioning of AWS resources using Cloudformation templates and Chef for installing software and configuring instances.
Google Cloud Platform - Eric Johnson, Joe Selman - ManageIQ Design Summit 2016ManageIQ
This document summarizes a presentation given by Joe Selman and Eric Johnson of Google Cloud Platform to the ManageIQ Design Summit in June 2016. It introduces Joe and Eric, discusses Cloud Graphite and Google's support for open source software. It then details Joe's journey learning Ruby and contributing to ManageIQ, including adding support for the Google Cloud Platform. The document concludes with an overview of the Google Cloud Platform and features of Google Compute Engine.
The document discusses a talk titled "Go With the Flow, Understanding Windows Workflow Foundation" which will explain what workflows are, the features of Windows Workflow Foundation, and demonstrate how it can be used to model common business processes like an estate agent managing property sales. It will cover the core concepts of workflows and activities, how to design workflows with Visual Studio and XAML, and some of the advanced features like rules engines and state machines. The talk is intended to provide enough information for attendees to understand if Workflow Foundation could benefit their projects.
Reason meets OCaml
- Reason is a syntax extension to OCaml that compiles to efficient JavaScript. It has a rock solid type system and access to the OCaml ecosystem.
- ReasonReact provides React bindings for Reason. Components are defined in a similar way to TypeScript but use Reason's type system and compiler.
- Examples show how stateless and stateful components are defined in ReasonReact compared to TypeScript. ReasonReact uses OCaml's type system and reducer pattern while TypeScript uses interfaces and state.
- Interoperability with JavaScript is supported through modules, raw JavaScript blocks, and using the platform. Reason aims to leverage the strengths of both OCaml and React.
Google Cloud Computing compares GCE, GAE and GKESimon Su
Google Cloud Computing compares GCE, GKE and GAE. GCE provides raw compute, storage and networking resources and requires more management overhead. GAE focuses on application logic and requires less management. GKE offers managed Kubernetes infrastructure and services. Each option has different strengths for workloads like microservices, containerized services, or large-scale applications requiring quick scaling. Monitoring and management features like Stackdriver are also compared.
Plaλ (pronounced 'plambda') is an approach to make unmodified Play apps work using AWS lambda. Judged from the outset as a comedic attempt destined to fail, the results are surprisingly good - though far from perfect and some way from usable. No matter - it's a great lesson in AWS lambda's limitations and how this technique could be applied to web frameworks in order to accelerate the move to serverless.
Intro to the Google Cloud for DevelopersLynn Langit
This document provides an introduction to developing applications on Google Cloud. It discusses Google's cloud infrastructure and services like Compute Engine and App Engine. It demonstrates how to use the Google Cloud SDK and APIs to manage resources and build applications using various languages and tools. Specifically, it shows how to create instances in Compute Engine and Big Query, deploy applications to App Engine from Eclipse, and use command line tools to manage storage, databases and other services.
This document provides a step-by-step guide to using Docker with data including installing boot2docker, using Dockerfiles and docker-compose files, and discusses Docker concepts from 10,000 feet including containers, union filesystems, and deploying with Kubernetes. It also discusses using Docker for data including loading data from databases into Docker, potential issues like slow performance if not configured carefully, and the tradeoffs of using Docker including reduced flexibility for added convenience.
Getting Predictable - Pragmatic Approach for Mobile Development - Devday.lk ...Anjana Somathilake
Getting Predictable - Pragmatic Approach for Mobile Development by Anjana Somathilake
Are you been challenged by…
Aggressive timelines, increasing team size, and a QA team that demands stability?
An ever growing defect count that impacts the release date?
A "Fix one thing, break another" syndrome?
A UX that is still unknown while the product team is asking for stats.
Stakeholders that instantly need to see beta versions installed on their phones.
Random app crashes (in front of the CEO &/or Paying Clients) and no one knows why?
3rd party API changes and the team doesn’t find out till after the app submission
Achieve Predictable Delivery in Mobile Application Development by
Automating the When & How using…
Acceptance Testing with Calabash
Builds, Unit Tests and Code Quality with Jenkins CI, OCUnit and Clang Static Analyzer
App Distribution with TestFlight
And Implementing Insight and Analytics with…
Real-time Crash Reports and Analytics through Crashlytics
Actions and Events Tracking with Mixpanel
Implementing improved and consistent arbitrary event tracking company-wide us...yalisassoon
Talk on the role Snowplow plays as part of the larger project to make data accessible to product marketing and other data-driven teams at StumbleUpon. Touches on technical and organizational challenges
With analytics tools, you get a window into how people use your product and where they drop off, among many other things. But knowing which data to track with these analytics tools – and which data to skip, and which to detail – is a tricky problem. Your analytics tools are only as good as the data you send them. We will teach you what information you should track.
In this webinar, Jake Peterson, head of customer success at Segment, will take us through a framework for deciding which analytics data to track. He’ll suggest best practices for companies and advocate that less is more (i.e., concentrate on the most important parts of your funnel, and then iterate as you learn from your data). Jake will also reveal Segment’s tracking schema and share which information Segment considers key to its sign-up and activation funnels.
Mixpanel is an analytics tool that allows users to track actions, create funnels and retention charts, and analyze notifications and revenue. It can be integrated into websites and mobile apps using JavaScript libraries or SDKs to track events like page views. Examples shown include tracking paths through content from Google to a main page to order, as well as trends and retention. Notifications are also demonstrated as reminders in an email workflow from signup to multiple reminders. Revenue charts show increasing revenue over time.
Comment utiliser Mixpanel - Julien Le Coupanec, Growth Hacker chez TheFamilyTheFamily
Mixpanel est un outil important pour comprendre le comportement des utilisateurs sur un site, ainsi que les comptes rendus de toutes les campagnes entreprises. Mixpanel te donne ainsi des informations déterminantes pour mener une stratégie pertinente et obtenir . Julien le Coupanec, Growth Hacher chez TheFamily, explique dans cette vidéo les forces de Mixpanel et comment maîtriser cet outil.
La video : https://www.youtube.com/watch?v=BZxL7cNznb4
Par Julien Le Coupanec, Growth Hacker chez TheFamily - twitter.com/@lecoupa
At TheFamily, we believe that anyone can become a great entrepreneur. Find more info here: http://www.thefamily.co/
5 Common Startup Growth F-ups - Aliisa Hodges, MixpanelTraction Conf
Aliisa Hodges is the Growth Manager and 1st Business Hire at Mixpanel, the fastest growing mobile and web analytics company. Mixpanel has raised $77M from major investors including Andreessen Horowitz, Sequoia Capital, Y Combinator, David Sacks, Marc Benioff, Max Levchin, and Keith Rabois, and is valued at almost a billion dollars. Aliisa shares some of their early growth mistakes and what you can learn from them. Traction Conf Vancouver 2015 - http://tractionconf.io.
This document discusses VPC by default and AWS services. It provides examples of how EC2 and S3 can communicate through a VPC using security groups and NACLs. It also discusses using VPC for hybrid use cases with on-premise systems through IPsec VPN or Direct Connect. Finally, it highlights how VPC allows integration between different AWS services.
AWS re:Invent re:Cap 행사에서 발표된 강연 자료입니다. 아마존 웹서비스의 김일호 솔루션스 아키텍트가 발표한 내용입니다.
내용 요약: Hadoop과 Elastic MapReduce, Redshift, Kinesis, Data Pipeline, S3 등 다양한 서비스들을 활용하는 데이터 분석의 모범사례 및 아키텍처 설계 패턴에 대해 말씀드리고, re:Invent에서 새로 추가된 Amazon EC2 컴퓨팅 최적화 인스턴스 C4와 새로 발표된 Amazon EBS 볼륨 확장 및 성능 향상에 대해 함께 살펴볼 예정입니다.
StartPad Countdown 8 - Amazon Web Services and YouStart Pad
This document provides an overview of Amazon Web Services (AWS) and cloud computing. It introduces Jeff Barr who works as a Senior Web Services Evangelist at AWS. It describes the key attributes of cloud computing like abstracted resources, cost-effective scaling, and pay-as-you-go usage. The document outlines several AWS services like S3, EC2, SQS, and explains their purpose and pricing models. It also provides examples of how companies have used AWS services to scale their applications.
Building a Scalable Digital Asset Management Platform in the Cloud (MED402) |...Amazon Web Services
With the breadth of AWS services available that are relevant to digital media, organizations can readily build out complete content/asset management (DAM/MAM/CMS) solutions in the cloud. This session provides a detailed walkthrough for implementing a scalable, rich-media asset management platform capable of supporting a variety of industry use cases. The session includes code-level walkthrough, AWS architecture strategies, and integration best practices for content storage, metadata processing, discovery, and overall library management functionality—with particular focus on the use of Amazon S3, Amazon Elastic Transcoder, Amazon DynamoDB and Amazon CloudSearch. Customer case study will highlight successful usage of Amazon CloudSearch by PBS to enable rich discovery of programming content across the breadth of their network catalog.
This document provides an overview of Amazon Kinesis and how it can be used to build a real-time big data application on AWS. Key points discussed include using Kinesis to collect streaming data from sources, processing the data in real-time using services like Kinesis, EMR and Redshift, and storing and analyzing the results. Examples are provided of ingesting log data from sources into Kinesis, analyzing the data with Hive on EMR, and loading results into Redshift for interactive querying and business intelligence.
This document discusses setting up a LAMP stack on an Ubuntu server using SSH and various commands like apt-get. It installs Apache, PHP, MySQL, Redis, and Varnish. It then discusses using rsync to copy files to the server and configuring the various components like enabling PHP modules and Apache rewrite rules.
The document discusses running memcached clusters on Amazon EC2. It covers key concepts like caching, clusters, and infrastructure as a service (AWS). It then provides step-by-step instructions for setting up a memcached cluster on EC2, including creating security groups, launching EC2 instances from AMIs, and configuring the memcached servers and clients. The summary concludes that setting up and running memcached clusters on infrastructure as a service environments like EC2 is straightforward.
Building a Scalable Asset Management (DAM) Platform in the AWSRahul Shukla
This document outlines the architecture for building a scalable digital asset management (DAM) platform in the cloud. Key components include Amazon S3 for storage, auto scaling EC2 instances for processing metadata and generating renditions, DynamoDB for the catalog, CloudSearch for search, and Elastic Transcoder for transcoding. The architecture provides ingest of assets from S3, metadata extraction using EC2 workers, generation of renditions, building the catalog in DynamoDB and CloudSearch, and delivery of assets through CloudFront.
1. The document demonstrates how to use various AWS services like Kinesis, Redshift, Elasticsearch to analyze streaming game log data.
2. It shows setting up an EC2 instance to generate logs, creating a Kinesis stream to ingest the logs, and building Redshift tables to run queries on the logs.
3. The document also explores loading the logs from Kinesis into Elasticsearch for search and linking Kinesis and Redshift with Kinesis Analytics for real-time SQL queries on streams.
Monitoring Containers at Scale - September Webinar SeriesAmazon Web Services
Containers come and go rapidly, which is great for scalable or fast-evolving infrastructure. However, the short life of containers make it more challenging to monitor, leaving many with questions such as: How many containers can you run on a given Amazon EC2 instance type? Which metric should you look at to measure contention? How do you manage fleets of containers at scale? In this session, we'll present the challenges and benefits of running containers at scale, how to use quantitative performance patterns to monitor your infrastructure at this magnitude and complexity, and we'll discuss proven strategies for monitoring your containerized infrastructure on AWS and ECS.
Learning Objectives:
- Set up the infrastructure to monitor your containers running on AWS
- Understand the metrics available and what they mean
- Define a strategy to monitor your containers
This document discusses various options for migrating data and workloads between on-premises environments and AWS. It covers tools like AWS Database Migration Service for database migration, VM Import/Export for virtual machine migration, copying files between S3 buckets, and using services like Route53 for transitioning traffic during a migration. Specific techniques discussed include copying AMIs, EBS snapshots, security groups, and database parameters between regions; using the AWS Schema Conversion Tool; and DynamoDB cross-region replication.
You’re interested in the cloud, and you want to start learning more. In this webcast we will answer the following questions:
• What is Cloud Computing?
• What are the benefits of Cloud Computing?
• What are AWS’s products and what workloads can I run with them?
• Who is using the cloud and what are they using it for?
The slide deck used in the Apache Camel / Syndesis Seminar at Red Hat, K.K., Ebisu --
https://jcug-oss.connpass.com/event/99168/
Uploaded with permission of Christina Lin
In this talk I will show you how to build a CI/CD pipeline in AWS with, static code analysis in Sonar, tests and continuous deployment of a dockerized service through several environments by using pure AWS services like CodeStar, CodeCommit, CodeBuild, CodeDeploy and CodePipline. I will do a demo of such CI/CD to reveal all guts of tools and services integration and implementation. So you will see how a commit will be going through all those steps and tools to get production environment.
The document discusses new features in Amazon RDS for SQL Server. It provides an overview of infrastructure improvements including new instance types and storage options. It also covers security features like SQL Server audit and SQLAgentOperatorRole. Monitoring capabilities through CloudWatch Logs integration and Performance Insights are reviewed. Migration methods using native backups and restores and Change Data Capture are presented. Additional features such as increased database limits and S3 integration are also summarized.
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
Want to get ramped up on how to use Amazon's big data web services and launch your first big data application on AWS? Join us on our journey as we build a big data application in real-time using Amazon EMR, Amazon Redshift, Amazon Kinesis, Amazon DynamoDB, and Amazon S3. We review architecture design patterns for big data solutions on AWS, and give you access to a take-home lab so that you can rebuild and customize the application yourself.
(CMP405) Containerizing Video: The Next Gen Video Transcoding PipelineAmazon Web Services
1) The document discusses designing a container-based video transcoding pipeline using AWS services to address challenges around speed, cost, and rapid changes in video coding standards.
2) It proposes an architecture using Amazon ECS, EFS, S3, Lambda, and EC2 to parallelize transcoding workloads across containers for improved speed while reducing costs through on-demand scaling.
3) The design was validated through testing video processing performance and quality metrics like PSNR to ensure the containerized solution met objectives around speed, cost and quality of transcoded video outputs.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...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 integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
An event is a happening inside the SPiD core.
Ie: Signup, Login, Logout, Verify email, Purchase, etc.
Events are triggered to be able to
Get insight into the user behavior.
Measure conversion of our processes.
We need events to be able to improve our software.
Events are sent from the SPiD Core to our UDP logger.
The UDP logger inserts all events into a SQS queue.
DataPiper retrieves events from the main SQS queue.
Events are filtered and inserted into Redshift, Mixpanel and other SQS queues.
The UDP logger is written in Node.js for performance.
We have an admin interface that monitors the server to be able to detect issues in an early stage.
Why UDP?
We use UDP to prevent latency inside the SPiD Core. This way we can send as many events as we like without having to think about latency.
Package loss is no issue as long as the UDP server is located inside the same network as the core application.
The DataPiper is also written in Node.js.
We use Amazon CloudWatch to monitor the DataPiper performance.
Incoming messages, messages in queue, messages in flight, etc.
Based on these number we can fine tune the DataPiper.
DataPiper flow:
Retrieve data from the main SQS queue
Filter data.
Insert data into Mixpanel, Redshift or another SQS queue.
Deployment of our servers on the Amazon cloud platform. How do we do it?
What do we need to think about?
Scalability
We need to make sure our queues never pile up.
Uptime
Our queues needs to be up at all times. Luckily Amazon provides that with SQS.
Our DataPiper needs to up all the time to keep our data flowing.
Auto Scaling provides the key to this solution.
Auto Scaling:
Makes sure we always have the desired amount of servers running.
Makes it possible to scale when traffic increases and then down scale afterwards.
An Auto Scaling is actually an Auto Scaling Group:
It provides the desired amount of EC2 instances based on your scaling policy.
An Auto Scaling Group is tied to a Launch Configuration:
AMI type : Type of predefined server image.
Instance type : Hardware type.
Storage : Storage size and type.
Security group : Firewalls around this group of servers.
User data : bash script run at launch, used to automate installation.
When the Auto Scaling Group fires up a new server it’s done like this:
AMI is booted with the desired instance type, storage and security group.
User data script installs:
S3cmd config from public S3 bucket.
S3cmd tools.
Puppet via npm.
When the first step is done and you are able to connect to the private S3 bucket:
User data script then downloads:
The standalone puppet config (node less) from private S3 bucket.
Then it executes the Puppet client (node less, no master server needed):
Installing required packages (node.js, ppm, etc)
Preparing software install
Creating dirs and setting ownership
Installing DataPiper
Software, config, upstart, logrotate
Starting DataPiper service.
No ssh login.
No manual labor.
All is automated - Look, no hands :)
How do we deploy new versions of our software?
Software deployment can be a tedious process.
We’re working hard to simplify this and minimize the risk of down time due to deployment.
This is how it’s done:
The deployment master prepares
A new release of our software.
A new config file.
All is uploaded to our private S3 bucket.
Before proceeding please wait a few minutes and enjoy a good cup of coffee. It can be a replication delay inside the S3 platform.
Start the deployment of new instances:
Number of desired instances are increased by the number of new instances you want to deploy with the new software version.
One and one is good to be sure everything works smoothly.
Auto Scaling fires up new instances with our new software and config files. This usually takes a couple of minutes.
When these new instances are up, then you decrease the numbers of desired instances back to the original number.
Auto scaling will destroy the old instances and you’re good to go with your new version.