Through the looking glass an intro to scalable, distributed counting in data...Geoff Cooney
Lightning talk I gave at GCP Boston meetup for a quick hands on intro to google dataflow. Example based on the public pubsub topic described here: https://github.com/googlecodelabs/cloud-dataflow-nyc-taxi-tycoon
With Cloud Functions you write simple, functions that doing one unit of execution.
Cloud Functions can be written using JavaScript, Python 3, or Go
and you simply deploy a function bound to the event you want and you are all done.
In our case we will leavrage from Cloud Function to manage our K8s clusters based on work times in order to save budget.
Tips and tricks to maximize performance and minimize serverless costs with Firebase and Google Cloud Functions. Live examples and analysis to show that GCF is the cheapest function provider, compared to Azure Functions and AWS Lambda.
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.
Using Kubernetes to deploy Django in GCPWalter Liu
Walter discusses using Kubernetes on Google Cloud Platform to deploy a Django application. He describes how Kubernetes provides infrastructure as code to automate and scale the application. Key Kubernetes components used include pods, deployments, statefulsets, secrets and configmaps. Services are used for internal and external load balancing, with an ingress controller providing a global load balancer. The document also touches on cluster creation steps, load balancing options, and templating with Python Jinja.
This document summarizes a company's two year journey migrating their infrastructure to Kubernetes on AWS. It describes their stack including tools like Terraform, AWS, CoreOS, Kubernetes and Docker. It outlines their architecture with masters, workers and stateful/stateless nodes. It discusses their lifecycles for development, testing and production. It also covers some struggles they faced around node availability and networking issues. Finally, it provides lessons learned around costs, using Terraform with Kubernetes, separating concerns, and prioritizing automation and testing in their workflows.
The Kubeflow control plane includes kfctl and the Kubeflow operator which are used to deploy, manage and monitor Kubeflow applications on Kubernetes clusters. Kfctl is a CLI tool that uses KfDef configuration files to build and apply Kubeflow manifests from a repository. The Kubeflow operator watches for KfDef custom resources and installs Kubeflow by creating the defined applications.
The document provides information about Simon Su and his expertise in Google Dataflow. It includes Simon's contact information and links to his online profiles. It then discusses Simon's areas of specialization including data scientist, data engineer, and frontend engineer. The document proceeds to provide information about preparing for a Google Dataflow workshop, including documents and labs to review. It also discusses Google Cloud services for data processing and analysis like Dataflow, BigQuery, Pub/Sub, and Dataproc. Finally, it outlines the agenda for the workshop, which will include hands-on labs to deploy users' first Dataflow project and create a streaming Dataflow model.
Through the looking glass an intro to scalable, distributed counting in data...Geoff Cooney
Lightning talk I gave at GCP Boston meetup for a quick hands on intro to google dataflow. Example based on the public pubsub topic described here: https://github.com/googlecodelabs/cloud-dataflow-nyc-taxi-tycoon
With Cloud Functions you write simple, functions that doing one unit of execution.
Cloud Functions can be written using JavaScript, Python 3, or Go
and you simply deploy a function bound to the event you want and you are all done.
In our case we will leavrage from Cloud Function to manage our K8s clusters based on work times in order to save budget.
Tips and tricks to maximize performance and minimize serverless costs with Firebase and Google Cloud Functions. Live examples and analysis to show that GCF is the cheapest function provider, compared to Azure Functions and AWS Lambda.
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.
Using Kubernetes to deploy Django in GCPWalter Liu
Walter discusses using Kubernetes on Google Cloud Platform to deploy a Django application. He describes how Kubernetes provides infrastructure as code to automate and scale the application. Key Kubernetes components used include pods, deployments, statefulsets, secrets and configmaps. Services are used for internal and external load balancing, with an ingress controller providing a global load balancer. The document also touches on cluster creation steps, load balancing options, and templating with Python Jinja.
This document summarizes a company's two year journey migrating their infrastructure to Kubernetes on AWS. It describes their stack including tools like Terraform, AWS, CoreOS, Kubernetes and Docker. It outlines their architecture with masters, workers and stateful/stateless nodes. It discusses their lifecycles for development, testing and production. It also covers some struggles they faced around node availability and networking issues. Finally, it provides lessons learned around costs, using Terraform with Kubernetes, separating concerns, and prioritizing automation and testing in their workflows.
The Kubeflow control plane includes kfctl and the Kubeflow operator which are used to deploy, manage and monitor Kubeflow applications on Kubernetes clusters. Kfctl is a CLI tool that uses KfDef configuration files to build and apply Kubeflow manifests from a repository. The Kubeflow operator watches for KfDef custom resources and installs Kubeflow by creating the defined applications.
The document provides information about Simon Su and his expertise in Google Dataflow. It includes Simon's contact information and links to his online profiles. It then discusses Simon's areas of specialization including data scientist, data engineer, and frontend engineer. The document proceeds to provide information about preparing for a Google Dataflow workshop, including documents and labs to review. It also discusses Google Cloud services for data processing and analysis like Dataflow, BigQuery, Pub/Sub, and Dataproc. Finally, it outlines the agenda for the workshop, which will include hands-on labs to deploy users' first Dataflow project and create a streaming Dataflow model.
Serverless with Google Cloud FunctionsJerry Jalava
This document discusses Google Cloud Functions, a serverless platform for running code in response to events. It provides an overview of Google Cloud Functions' features such as triggers from Cloud Pub/Sub and Storage, integration with other Google Cloud services, and use cases including building mobile backends, APIs, data processing, and IoT. The document also discusses using Google Cloud Functions with Firebase and pricing.
Serverless Big Data Architecture on Google Cloud Platform at Credit OKKriangkrai Chaonithi
Serverless Big Data Architecture on Google Cloud Platform was presented by Kriangkrai Chaonithi. The presentation covered Credit OK's use of serverless architecture on GCP for their big data analytics platform. Credit OK processes large amounts of customer data from over 400 sites to perform credit scoring. They use Google Cloud Functions to ingest data from sites, as well as Compute Engine and Google Cloud Storage. This serverless architecture allows them to automatically scale infrastructure as needed, reducing costs since they only pay for resources used. While serverless architectures don't require managing servers, there are still resource limits that must be considered to avoid issues like exhausted worker pools during peak loads.
The document introduces Google Cloud Functions, a serverless computing platform. It discusses how Cloud Functions allows running code without managing servers and paying only for the resources consumed. It provides examples of using Cloud Functions to process events from Cloud Storage and Pub/Sub and chain multiple functions together. Finally, it outlines some use cases and considerations for building serverless applications with Cloud Functions.
Build event driven, low latency, decoupled microservices on the serverless GCP infrastructure with Cloud Functions, PubSub, and Cloud Storage. Don't pay for what you don't use. Don't wait for daemons to sink up. Take computing actions when their needed, rapidly, cheaply, and robustly with microserverless.
Going Microserverless on Google Cloud @ mablJoseph Lust
Build event driven, low latency, decoupled microservices on the serverless GCP infrastructure with Cloud Functions, PubSub, and Cloud Storage. Don't pay for what you don't use. Don't wait for daemons to sink up. Take computing actions when their needed, rapidly, cheaply, and robustly with microserverless.
Serverless Apps on Google Cloud: more dev, less opsJoseph Lust
Serverless on GCP is a perfect match to do more dev and less ops. We discuss the many GCP serverless services used @ mabl and how they reduce both time to market and operating expenses. We focus on the nuances of Google Cloud Functions and many way to optimize your serverless apps.
This document summarizes Google Cloud Platform (GCP) learning resources. It provides an overview of GCP services like Compute Engine, App Engine, and Cloud Storage. It then lists various official training resources from Google including courses, exams, and documentation. Links are also provided to the GCP blog, GitHub, and other sources of information about GCP.
Life of a startup - Sjoerd Mulder - Codemotion Amsterdam 2017Codemotion
Building a minimum viable product in 3 months is easy. Scaling it towards a reactive system that can handle thousands of requests per second and deploying new versions without causing a denial of service is another challenge. Find out how at Crobox we scaled from a single machine (and point of failures) towards the high-available server cluster we are now running. On this journey you can also learn how we solved challenges with monitoring, logging and deployments.
L’evoluzione delle pratiche di sviluppo, delle architetture e delle infrastrutture è un processo che anche Drupal ha abbracciato, trasformandosi da un CMS per community a un framework PHP moderno.
Drupal oggi permette di creare un'esperienza developer-friendly e può essere la base su cui costruire la vostra applicazione cloud-native.
This document provides an introduction to modern DevOps technologies. It discusses DevOps concepts like source code management using Git, different methods of deploying programs including using bare metal servers, virtualization, containers, and cloud functions. Specific container and container orchestration technologies like Docker and Kubernetes are explained. Continuous integration and continuous delivery (CI/CD) practices are also introduced. The presentation includes an agenda with slides on these topics and ends with a question and answer section and announcement of a Docker workshop to deploy an HTTP server container.
Los patrones están en todos lados. Los patrones de diseño han existido desde hace mucho tiempo para las arquitecturas tradicionales (monolíticas). Los patrones nos permiten tener un abanico de opciones de diseño predeterminadas, que se pueden aplicar según cada problema de negocio y tecnológico, dándonos una ventaja en el diseño de la solución, dado que son estructuras que han sido probadas durante el tiempo en forma repetitiva, hasta consolidarse como un patrón. Sin embargo, los patrones de diseño han cambiado con la llegada de la nube y el enfoque de microservicios. En esta oportunidad vamos a discutir en profundidad estos patrones de diseño y su aplicabilidad.
https://www.meetup.com/Cloud-Native-Chile/
Performance Tales of Serverless - CloudNative London 2018☁️ Mikhail Shilkov
Function-as-a-Service "serverless" cloud offerings provide you with a super easy way to run custom code in response to events. One promise of FaaS model is the ability to scale without limits, up or down, whenever needed. But how does that work in practice? Can AWS Lambda handle thousands of messages per second? How fast can Azure Functions scale up under sudden heavy load? What kind of latency can you expect from Google Cloud Functions? During this session Mikhail will share with you short tales, each of them teaching a lesson about practical scalability of serverless applications. You will also explore the steps to evaluate whether your application profile is suitable for serverless today.
From mabl software engineer Joseph Lust.
Tips and tricks to maximize performance and minimize serverless costs with Firebase and Google Cloud Functions. Live examples and analysis to show that GCF is the cheapest function provider, compared to Azure Functions and AWS Lambda.
Serverless Apps on Google Cloud: more dev, less opsmabl
From mabl Software Engineer Joseph Lust.
Serverless on GCP is a perfect match to do more dev and less ops. We discuss the many GCP serverless services used @ mabl and how they reduce both time to market and operating expenses. We focus on the nuances of Google Cloud Functions and many way to optimize your serverless apps.
1) The document discusses using microservices on Google Cloud Functions (GCF) for serverless applications. It outlines how GCF provides a serverless platform for building microservices without provisioning servers and with automatic scaling.
2) It addresses challenges with the serverless model like function failures, security, and deployment speed. It describes patterns used by Mabl to solve issues like decoupling services, handling changes, and making deployments fast.
3) It provides tips for optimizing performance and costs on GCF like right-sizing memory, caching objects, batching work, and direct billing integration for tracking usage. In conclusion, it advocates for embracing GCF microserverless for building low latency, event-
Google Cloud Bangla session. I had given I talk. This is all about Google Firebase it's features and technical benefits. After a session, I had a small workshop so that people get realtime hands-on experiences
IDEALIZE 2023 - NodeJS & Firebase SessionBrion Mario
Node.js Firebase
This document discusses Node.js, RESTful APIs, and Firebase. It provides an overview of Node.js and its event loop model. It also explains what npm and RESTful APIs are. Finally, it demonstrates how to build a basic RESTful API with Node.js and store data in Firestore using Firebase.
Automation of Hadoop cluster operations in Arm Treasure DataYan Wang
This talk will focus on the journey we in the Arm Treasure Data hadoop team is on to simplify and automate how we deploy hadoop. In Arm Treasure Data, up to recently we were running hadoop clusters in two clouds. Due to fast increase of deployments into more sites, the overhead of manual operations has started to strain us. Due to this, we started a project last year to automate and simplify how we deploy using tools like AWS autoscaling groups. Steps we have taken so far are modernize and standardize instance types, moved from manually executed deployment scripts to api triggered work flows, actively working to deprecate chef in favor of debian packages and AWS Codedeploy. We have also started to automate a lot of operations that up to recently were manual, like scaling in and out clusters, and routing traffic between clusters. We also started simplify health check and node snapshotting. And our goal of the year is close to fully automated cluster operations.
Serverless with Google Cloud FunctionsJerry Jalava
This document discusses Google Cloud Functions, a serverless platform for running code in response to events. It provides an overview of Google Cloud Functions' features such as triggers from Cloud Pub/Sub and Storage, integration with other Google Cloud services, and use cases including building mobile backends, APIs, data processing, and IoT. The document also discusses using Google Cloud Functions with Firebase and pricing.
Serverless Big Data Architecture on Google Cloud Platform at Credit OKKriangkrai Chaonithi
Serverless Big Data Architecture on Google Cloud Platform was presented by Kriangkrai Chaonithi. The presentation covered Credit OK's use of serverless architecture on GCP for their big data analytics platform. Credit OK processes large amounts of customer data from over 400 sites to perform credit scoring. They use Google Cloud Functions to ingest data from sites, as well as Compute Engine and Google Cloud Storage. This serverless architecture allows them to automatically scale infrastructure as needed, reducing costs since they only pay for resources used. While serverless architectures don't require managing servers, there are still resource limits that must be considered to avoid issues like exhausted worker pools during peak loads.
The document introduces Google Cloud Functions, a serverless computing platform. It discusses how Cloud Functions allows running code without managing servers and paying only for the resources consumed. It provides examples of using Cloud Functions to process events from Cloud Storage and Pub/Sub and chain multiple functions together. Finally, it outlines some use cases and considerations for building serverless applications with Cloud Functions.
Build event driven, low latency, decoupled microservices on the serverless GCP infrastructure with Cloud Functions, PubSub, and Cloud Storage. Don't pay for what you don't use. Don't wait for daemons to sink up. Take computing actions when their needed, rapidly, cheaply, and robustly with microserverless.
Going Microserverless on Google Cloud @ mablJoseph Lust
Build event driven, low latency, decoupled microservices on the serverless GCP infrastructure with Cloud Functions, PubSub, and Cloud Storage. Don't pay for what you don't use. Don't wait for daemons to sink up. Take computing actions when their needed, rapidly, cheaply, and robustly with microserverless.
Serverless Apps on Google Cloud: more dev, less opsJoseph Lust
Serverless on GCP is a perfect match to do more dev and less ops. We discuss the many GCP serverless services used @ mabl and how they reduce both time to market and operating expenses. We focus on the nuances of Google Cloud Functions and many way to optimize your serverless apps.
This document summarizes Google Cloud Platform (GCP) learning resources. It provides an overview of GCP services like Compute Engine, App Engine, and Cloud Storage. It then lists various official training resources from Google including courses, exams, and documentation. Links are also provided to the GCP blog, GitHub, and other sources of information about GCP.
Life of a startup - Sjoerd Mulder - Codemotion Amsterdam 2017Codemotion
Building a minimum viable product in 3 months is easy. Scaling it towards a reactive system that can handle thousands of requests per second and deploying new versions without causing a denial of service is another challenge. Find out how at Crobox we scaled from a single machine (and point of failures) towards the high-available server cluster we are now running. On this journey you can also learn how we solved challenges with monitoring, logging and deployments.
L’evoluzione delle pratiche di sviluppo, delle architetture e delle infrastrutture è un processo che anche Drupal ha abbracciato, trasformandosi da un CMS per community a un framework PHP moderno.
Drupal oggi permette di creare un'esperienza developer-friendly e può essere la base su cui costruire la vostra applicazione cloud-native.
This document provides an introduction to modern DevOps technologies. It discusses DevOps concepts like source code management using Git, different methods of deploying programs including using bare metal servers, virtualization, containers, and cloud functions. Specific container and container orchestration technologies like Docker and Kubernetes are explained. Continuous integration and continuous delivery (CI/CD) practices are also introduced. The presentation includes an agenda with slides on these topics and ends with a question and answer section and announcement of a Docker workshop to deploy an HTTP server container.
Los patrones están en todos lados. Los patrones de diseño han existido desde hace mucho tiempo para las arquitecturas tradicionales (monolíticas). Los patrones nos permiten tener un abanico de opciones de diseño predeterminadas, que se pueden aplicar según cada problema de negocio y tecnológico, dándonos una ventaja en el diseño de la solución, dado que son estructuras que han sido probadas durante el tiempo en forma repetitiva, hasta consolidarse como un patrón. Sin embargo, los patrones de diseño han cambiado con la llegada de la nube y el enfoque de microservicios. En esta oportunidad vamos a discutir en profundidad estos patrones de diseño y su aplicabilidad.
https://www.meetup.com/Cloud-Native-Chile/
Performance Tales of Serverless - CloudNative London 2018☁️ Mikhail Shilkov
Function-as-a-Service "serverless" cloud offerings provide you with a super easy way to run custom code in response to events. One promise of FaaS model is the ability to scale without limits, up or down, whenever needed. But how does that work in practice? Can AWS Lambda handle thousands of messages per second? How fast can Azure Functions scale up under sudden heavy load? What kind of latency can you expect from Google Cloud Functions? During this session Mikhail will share with you short tales, each of them teaching a lesson about practical scalability of serverless applications. You will also explore the steps to evaluate whether your application profile is suitable for serverless today.
From mabl software engineer Joseph Lust.
Tips and tricks to maximize performance and minimize serverless costs with Firebase and Google Cloud Functions. Live examples and analysis to show that GCF is the cheapest function provider, compared to Azure Functions and AWS Lambda.
Serverless Apps on Google Cloud: more dev, less opsmabl
From mabl Software Engineer Joseph Lust.
Serverless on GCP is a perfect match to do more dev and less ops. We discuss the many GCP serverless services used @ mabl and how they reduce both time to market and operating expenses. We focus on the nuances of Google Cloud Functions and many way to optimize your serverless apps.
1) The document discusses using microservices on Google Cloud Functions (GCF) for serverless applications. It outlines how GCF provides a serverless platform for building microservices without provisioning servers and with automatic scaling.
2) It addresses challenges with the serverless model like function failures, security, and deployment speed. It describes patterns used by Mabl to solve issues like decoupling services, handling changes, and making deployments fast.
3) It provides tips for optimizing performance and costs on GCF like right-sizing memory, caching objects, batching work, and direct billing integration for tracking usage. In conclusion, it advocates for embracing GCF microserverless for building low latency, event-
Google Cloud Bangla session. I had given I talk. This is all about Google Firebase it's features and technical benefits. After a session, I had a small workshop so that people get realtime hands-on experiences
IDEALIZE 2023 - NodeJS & Firebase SessionBrion Mario
Node.js Firebase
This document discusses Node.js, RESTful APIs, and Firebase. It provides an overview of Node.js and its event loop model. It also explains what npm and RESTful APIs are. Finally, it demonstrates how to build a basic RESTful API with Node.js and store data in Firestore using Firebase.
Automation of Hadoop cluster operations in Arm Treasure DataYan Wang
This talk will focus on the journey we in the Arm Treasure Data hadoop team is on to simplify and automate how we deploy hadoop. In Arm Treasure Data, up to recently we were running hadoop clusters in two clouds. Due to fast increase of deployments into more sites, the overhead of manual operations has started to strain us. Due to this, we started a project last year to automate and simplify how we deploy using tools like AWS autoscaling groups. Steps we have taken so far are modernize and standardize instance types, moved from manually executed deployment scripts to api triggered work flows, actively working to deprecate chef in favor of debian packages and AWS Codedeploy. We have also started to automate a lot of operations that up to recently were manual, like scaling in and out clusters, and routing traffic between clusters. We also started simplify health check and node snapshotting. And our goal of the year is close to fully automated cluster operations.
This document discusses Spring Boot and Spring Cloud applications running on Kubernetes and Pivotal Cloud Foundry. It introduces the speaker and covers topics like different platforms, choosing between Kubernetes and PCF, and developer experience differences. PCF is highlighted as providing lower development complexity while Kubernetes offers more flexibility and control. The benefits of both platforms for running Spring applications are discussed.
Serverless preview environments to the rescueJoseph Lust
End to end testing every commit is a challenge solved with serverless preview environments. Traditionally only select environments like "integration" and "UAT" could be fully tested, because apps required deployment tooling. Using modern serverless cloud solutions, you can deploy and host every commit, providing immediate PR feedback on feature branches and discovering problems long before they're merged.
Best practices for developing your Magento Commerce on CloudOleg Posyniak
Properly implementing Magento Commerce Cloud is critical to the success of your online store. In this session, we’ll take a look under the hood and share how to maximize the value of your Cloud project through Docker-based local development, configurations to optimize deployments, and tools for performance monitoring (New Relic), and optimization (Blackfire).
This document discusses the use of CI/CD (continuous integration and continuous delivery) pipelines to safely and quickly deliver code from development to production. It outlines the tools used, including BitBucket, Jenkins, Docker, and Mesos. It describes build conventions and how the build infrastructure is scaled horizontally. The deployment flow and types of operations like deploy, start, stop, and restart are explained. Auto healing, saving money, and the need for a PAAS platform are also covered. Build and deployment demos are included to demonstrate the workflows.
Kubernetes is awesome! But what does it takes for a Java developer to design, implement and run Cloud Native applications? In this session, we will look at Kubernetes from a user point of view and demonstrate how to consume it effectively. We will discover which concerns Kubernetes addresses and how it helps to develop highly scalable and resilient Java applications.
FOSDEM TALK: https://fosdem.org/2017/schedule/event/cnjavadev/
Serverless Architecture GCP In ProductionOliver Fierro
The document defines serverless architecture and discusses various Google Cloud Platform products that can be used to build serverless applications. It examines compute options like Cloud Functions, App Engine, and CloudRun. It also looks at services for persistence, processing, triggering, messaging, machine learning, containers, CI/CD pipelines, and more. The document provides considerations for serverless projects, architecture patterns, and use case examples.
stackconf 2020 | The path to a Serverless-native era with Kubernetes by Paolo...NETWAYS
Serverless is one of the hottest design patterns in the cloud today, i’ll cover how the Serverless paradigms are changing the way we develop applications and the cloud infrastructures and how to implement Serveless-kind workloads with Kubernetes.
We’ll go through the latest Kubernetes-based serverless technologies, covering the most important aspects including pricing, scalability, observability and best practices
This document discusses LINE's private cloud platform Verda and two new services: Verda Kubernetes as a Service (KaaS) and Verda Event Handler. Verda KaaS provides managed Kubernetes clusters to developers. It is built using Rancher and aims to simplify Kubernetes usage. Verda Event Handler aims to improve automation by defining operations as functions that are triggered by events. It will utilize Knative to provide a functions-as-a-service platform and improve visibility, operability, and maintenance of automation scripts. The status and future plans of these new services are also outlined.
Serverless Preview Environments @ Boston DevOpsJoseph Lust
This document discusses how to create serverless preview environments for testing every commit in a continuous integration/continuous delivery (CI/CD) workflow. Some key points:
1. Preview environments are short-lived environments that allow validating deployments without impacting production. This provides rapid developer feedback.
2. Serverless architectures are well-suited for preview environments since deployments are fast, the environments automatically scale down to zero cost when not in use, and only pay for resources used.
3. Techniques like using a wildcard domain and mapping the app and version as subpaths allow creating unique previews without separate networking for each. Storage services like S3 can host the preview artifacts with near-instant
Serverless/Frugal Architecture describes the benefits of serverless computing including continuous scaling, developer productivity, and fully managed operations. It discusses AWS Lambda's programming model of handlers, contexts, events, and asynchronous exceptions. Lambda supports various languages and has resource limits. Serverless computing is gaining adoption with Amazon Lambda as the pioneer, and other cloud providers like IBM, Microsoft, and Google developing their own serverless offerings. Challenges of serverless include testing, state management, and lack of observability. Open source projects are also emerging in this space like OpenWhisk.
The document discusses the Fn Project, an open-source serverless computing platform. It provides lessons learned from using serverless technologies, including issues with execution times, timeouts, and vendor lock-in. The Fn Project aims to address these issues by providing a platform that can be deployed anywhere and uses containers as primitives. It has components like the Fn CLI and Fn Server and supports building scalable and reliable functions.
This document discusses Apache Airflow and Google Cloud Composer. It begins by providing background on Apache Airflow, including that it is an open source workflow engine contributed by Airbnb. It then discusses how Codementor uses Airflow for ETL pipelines and machine learning workflows. The document mainly focuses on comparing self-hosting Airflow versus using Google Cloud Composer. Cloud Composer reduces efforts around hosting, permissions management, and monitoring. However, it has some limitations like occasional zombie tasks and higher costs. Overall, Cloud Composer allows teams to focus more on data logic and performance versus infrastructure maintenance.
The sprint report summarizes work done in Sprint 17 of the ManageIQ project, including developing REST API actions for managing VMs, converting parts of the UI to use jQuery and AngularJS, integrating additional cloud providers, and performing upgrades to prepare for Rails 4. Testing and architecture work also continued around areas like Dockerization, caching, and the provider layer. Future iterations will focus on additional providers, IPv6 support, and fleecing capabilities.
Cloud Native Night November 2017, Munich: Talk by Mario-Leander Reimer (@LeanderReimer, Principal Software Architect at QAware).
Join our Meetup: www.meetup.com/cloud-native-muc
Abstract: Until today existing enterprise applications are integrated, tested, and deployed as monoliths. This is very time-consuming and hinders agile business models. Cloud technology promises unlimited scalability, short release cycles, quick deployments and antifragility. But can we evolve these systems into the cloud with reasonable effort? What do we have to change and what are the risks involved? This talk will share the experiences from a real world customer project and present an industrialized approach for the Cloud-native evolution of existing IT landscapes.
Similar to Firebase Cloud Functions: a quick overview (20)
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
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
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
13. the ugly: firebase cloud functions
■ All functions run as FB project Editor
■ No failure retry semantics
■ Cannot access resources cross project
■ CLI deploy requires rigid project layout
■ Automatic undeploy of all functions for non-standard layouts
■ Slow deployment ~ 5min
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14. the uglier: gcf
■ Cannot pass configs to function
■ No failure retry semantics
■ All functions run as project Editor
■ Complex multi-module deploy
▲ Impossible from SCM deploy
■ Slow deployment ~ 2min
■ Non-Deterministic Deployment Failures
■ Single Supported Region (Iowa US-Centra1)
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20. work around the warts
■ Load ServiceAccount key from KMS encrypted CS file
■ Cache resources between function calls
■ Use Firebase Admin lib from GCF
▲ Best of most worlds
■ Use the lowest sensible memory and timeout limits
■ Use --local-path and --include-ignored-files for multi-module deploy
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21. embrace serverless
■ to focus on shipping code
■ to harness NodeJS microservices
■ to power low latency experiences
■ to provision, deploy, and scale automatically
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22. cloud functions resources
■ FB CF Examples Repo: github.com/firebase/functions-samples
■ GCF Examples: github.com/GoogleCloudPlatform/nodejs-docs-samples
■ I/O 2017 CF Presentations
▲ Building the Fire!sale demo app: youtu.be/G-MBeEW92v4
▲ FB and ML with CF: youtu.be/RdqV_N0sCpM
▲ FB CF and Testability: youtu.be/SnWwkURpwxs
▲ Data Pipelines with CF: youtu.be/guo-4IOqx2M
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