Arhitectural Patterns - Message Queues (Beanstalkd)

•

0 likes•766 views

Discussion about message queue systems, like Beanstalkd pros and advantages. Asynchronous: Queue it now, run it later. Decoupling: Separates application logic. Resilience: Won't take down your whole application if part of it fails. Redundancy: Can retry jobs if they fail. Guarantees: Makes sure that jobs will be processed. Scalable: Many workers can process individual jobs in a queue. Elasticity & "Spikability": When your application hits the front page of Hacker News, you see unusual traffic Buffering: operate at peak efficiency by offering a buffer layer Profiling: Can aid in identifying performance issues.

Arhitectural Patterns - Message Queues (Beanstalkd)

Yahoo!広告ではプロダクトの統合も含めたリニューアルを進めています。本セッションでは、リニューアルの概要および広告管理ツールの刷新を進めるにあたり、ユーザーへ必要な機能を素早く届けるためにスクラムを取り入れた背景や気付きをご紹介します。また、プロダクトの改善をどういった流れで進めているか実際の事例も含めてお話しします。 Yahoo! JAPAN Tech Conference 2021 は2021年1月22日に開催しました。 https://techconference.yahoo.co.jp/2021/

Apache Kuduは何がそんなに「速い」DBなのか? #dbts2017

Cloudera Japan

Mechanical Vibrations by SS Rao 4th Edition Solution manual chapter 02

Rpiya

In a world of serverless computing users tend to be frugal when it comes to expenditure on compute, storage and other resources. Paying for the same when they aren’t in use becomes a significant factor. Offering Spark as service on cloud presents very unique challenges. Running Spark on Kubernetes presents a lot of challenges especially around storage and persistence. Spark workloads have very unique requirements of Storage for intermediate data, long time persistence, Share file system and requirements become very tight when it same need to be offered as a service for enterprise to mange GDPR and other compliance like ISO 27001 and HIPAA certifications. This talk covers challenges involved in providing Serverless Spark Clusters share the specific issues one can encounter when running large Kubernetes clusters in production especially covering the scenarios related to persistence. This talk will help people using Kubernetes or docker runtime in production and help them understand various storage options available and which is more suitable for running Spark workloads on Kubernetes and what more can be done

Engineering mechanics 2 handwritten classes notes (study materials) for IES P...

Khagendra Gautam

Surah Mominun

Azaakhaana

786 frenki serif

Milenko Gavric

λουκυ λουκ τεύχος 30 - η κούρσα του μισισιπή

Theodoros Vavouras

λουκυ λουκ τεύχος 34 - το 20ο συνταγμα ιππικου

Theodoros Vavouras

BTC Daily Report November 15 Ar.pdf

BTC

Interim reference letter infront sports & mediaPascal Sommerhalder

クロスユースプラットフォーム～秒間10万リクエスト・レスポンスタイム100ms以下を実現するシステムについて～ / YJTC19 in Shibuy...

Yahoo!デベロッパーネットワーク

Yahoo! JAPAN トップページ、Yahoo!ニュース、Yahoo!ショッピングなどのヤフー主要サービスにおいて、ユーザーに応じて適切なヤフーコンテンツを紹介する基盤である、クロスユースプラットフォームを支える技術についてお話しします。本セッションでは、インフラレイヤーからアプリレイヤーまで、パフォーマンスと安定性の両立を図る構成や工夫についてお伝えしました。

The walking dead vol 29

Tuguldur Heiko

Doctorautomatico(Je)

guest3d72ba

Presentación Informáticalarenasdaniela

What's hot

Тренажер по русскому языку. Состав слова

Наташа Митрофанова

Barras de cobre de Metales Diaz

Metales Diaz

λουκυ λουκ τεύχος 31 - οι νταλτον στους ινδιανους

Theodoros Vavouras

التجويد المصور أيمن رشدى سويد ج1

Safa Alyousif

λουκυ λουκ τεύχος 29 - h μπαλαντα των νταλτονTheodoros Vavouras

Kupdf.com zagor tajna-na-mapi

zoran radovic

Mister NO LIB 087 - Sedmorica sa Santa Cruza

Stripovizijacom

Veliki Blek L 008 - Šumski car

Stripovizijacom

Unidad 4: Criptografía

carmenrico14

BTC Daily Report A. 29.11.2022.pdf

BTC

Storage Requirements and Options for Running Spark on Kubernetes

DataWorks Summit

Engineering mechanics 2 handwritten classes notes (study materials) for IES P...

Khagendra Gautam

Surah Mominun

Azaakhaana

786 frenki serif

Milenko Gavric

λουκυ λουκ τεύχος 30 - η κούρσα του μισισιπή

Theodoros Vavouras

λουκυ λουκ τεύχος 34 - το 20ο συνταγμα ιππικου

Theodoros Vavouras

BTC Daily Report November 15 Ar.pdf

BTC

Interim reference letter infront sports & mediaPascal Sommerhalder

クロスユースプラットフォーム～秒間10万リクエスト・レスポンスタイム100ms以下を実現するシステムについて～ / YJTC19 in Shibuy...

Yahoo!デベロッパーネットワーク

The walking dead vol 29

Tuguldur Heiko

What's hot (20)

Тренажер по русскому языку. Состав слова

Barras de cobre de Metales Diaz

λουκυ λουκ τεύχος 31 - οι νταλτον στους ινδιανους

التجويد المصور أيمن رشدى سويد ج1

λουκυ λουκ τεύχος 29 - h μπαλαντα των νταλτον

Kupdf.com zagor tajna-na-mapi

Mister NO LIB 087 - Sedmorica sa Santa Cruza

Veliki Blek L 008 - Šumski car

Unidad 4: Criptografía

BTC Daily Report A. 29.11.2022.pdf

Storage Requirements and Options for Running Spark on Kubernetes

Engineering mechanics 2 handwritten classes notes (study materials) for IES P...

Surah Mominun

786 frenki serif

λουκυ λουκ τεύχος 30 - η κούρσα του μισισιπή

λουκυ λουκ τεύχος 34 - το 20ο συνταγμα ιππικου

BTC Daily Report November 15 Ar.pdf

Interim reference letter infront sports & media

クロスユースプラットフォーム～秒間10万リクエスト・レスポンスタイム100ms以下を実現するシステムについて～ / YJTC19 in Shibuy...

The walking dead vol 29

Viewers also liked

Doctorautomatico(Je)

guest3d72ba

Presentación Informáticalarenasdaniela

PresentacióN Influen Zia 09

mdmgamero

Lecturasserarockcld

Paramétrage Du BlogLaTulipeBlanche

Action de communication

guestdc6c11b

Tetas para bebes 2009

guest192d15

Les gouttes d'eauJuan Ignacio B.

televisionguestce644cb

Val d-or05-2010ARMVOP Médecin Vasculaire

PresentacióN Romances Dia Del LibroJavier Galiana

Trabajo De Geografiaangela94

Que Es Una Red Extensaluis perez

Trabajo de Jose Hoyos

Luz García

Presentacionmagdalenes

Corona De AdvientoMARISSA208

Diccionario universal de términos parlamentarios

Lilia G. Torres Fernández

Petra

COSMOS33

Noticiaimportante!guestc890ff

Oat ch. 1 fr. 1Doug Doug

Viewers also liked (20)

Doctorautomatico(Je)

Presentación Informática

PresentacióN Influen Zia 09

Lecturas

Paramétrage Du Blog

Action de communication

Tetas para bebes 2009

Les gouttes d'eau

television

Val d-or05-2010

PresentacióN Romances Dia Del Libro

Trabajo De Geografia

Que Es Una Red Extensa

Trabajo de Jose Hoyos

Presentacion

Corona De Adviento

Diccionario universal de términos parlamentarios

Petra

Noticiaimportante!

Oat ch. 1 fr. 1

More from Márton Kodok

Gen Apps on Google Cloud PaLM2 and Codey APIs in Action

Márton Kodok

Build applications with generative AI on Google Cloud! We are going to see in action what Gen App Builder is for developers to build and deploy AI-driven applications. We will explore Model Garden powered experiences, then we are going to learn more about the integration of these generative AI APIs. Vertex AI includes a suite of models that work with code. Together these code models are referred to as the PaLM and Codey APIs. The Vertex AI Codey APIs include the code generation API which supports generating code using a natural language description. We will show strategies for creating prompts that work with the model to generate code. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative AI industry trends.

DevBCN Vertex AI - Pipelines for your MLOps workflows

Márton Kodok

In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated. Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all classic resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner. Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.

Discover BigQuery ML, build your own CREATE MODEL statement

Márton Kodok

With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. In this demo session we are going to demonstrate common marketing Machine Learning use cases of how to build, train, eval, and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases: - Customer Segmentation + Product cross sale recommendation - Conversion/Purchase prediction - Inference with other in-built >20 models The audience will get first-hand experience with how to write CREATE MODEL sql syntax to build machine learning models such as: - Multiclass logistic regression for classification - K-means clustering - Matrix factorization - ARIMA time series predictions ... and more Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision-making through predictive analytics across the organization without leaving the query editor. In the end, the audience will learn how everyday developers can build/train/run their own machine-learning models straight from the database query editor, by issuing CREATE MODEL statements

Cloud Run - the rise of serverless and containerization

Márton Kodok

Two of the biggest trends in applications development in recent years have been the rise of serverless and containerization. And Cloud Run has become a defacto container runtime service to production in seconds. Based on practical examples we will demonstrate how Cloud Run scores high in terms of developer experience. It differs from functions runtime as You can bring your own container, your own code, a folder, or binarys and it pairs great with the container ecosystem: Cloud Build, Cloud Code, Artifact Registry, and Docker. Each Cloud Run service gets an out-of-the-box stable HTTPS endpoint, with TLS termination handled for you. Map your services to your own domains and use either for web sites, backend APIs, workflows, invoke and connect services with the newest protocols of HTTP/2, WebSockets or gRPC (unary and streaming). Cloud Run is serverless containers, which means you don't have to fiddle with infrastructure or back-end resources to run applications.

BigQuery best practices and recommendations to reduce costs with BI Engine, S...

Márton Kodok

best practices and recommendations for tuning BI Engine for your existing BigQuery workloads for cheaper and faster queries. Learn how we at REEA are orchestrating BI Engine reservations, on a 5TB dataset, considered small for BigQuery but with big cost savings and accelerated queries. We are seeing many presentations for big enterprises, but now we are showcasing how our queries perform better with lower costs. We are going to address the top considerations when to turn on BI Engine, how to use cloud orchestration for making this an automatic process, and combined with BigQuery and Datastudio query complexity that might save precious development time, lower bills, faster queries.

Vertex AI - Unified ML Platform for the entire AI workflow on Google Cloud

Márton Kodok

Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models. Enhanced developer experience - Build with the groundbreaking ML tools that power Google - Approachable from the non-ML developer perspective (AutoML, managed models, training) - Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks) - Infrastructure management overhead have been almost completely eliminated - Unified UI for the entire ML workflow - End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks - Explainable AI and TensorBoard to visualize and track ML experiments

Vertex AI: Pipelines for your MLOps workflows

Márton Kodok

In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated. Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all standard resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner. Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.

Cloud Workflows What's new in serverless orchestration and automation

Márton Kodok

understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the newest features that lets you automate the cloud and integration with any Google Cloud product without worrying about authentication

Serverless orchestration and automation with Cloud Workflows

Márton Kodok

Join this session to understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the built-in decision and conditional executions, subworkflows, support for external built-in API calls, and integration with any Google Cloud product without worrying about authentication. We are going to cover Marketing, Retail, Industrial and Developer possibilities, such as event driven marketing workflow execution, or inventory chain operations, generating and automatic state machines, or orchestrate DevOps workflows and automating the Cloud.

Serverless orchestration and automation with Cloud Workflows

Márton Kodok

Serverless orchestration and automation with Cloud Workflows

Márton Kodok

BigdataConference Europe - BigQuery ML

Márton Kodok

One of the hottest topics in database land these days is BigQuery ML. A new way to use machine learning on top of tabular data straight on your tables without leaving the query editor. With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. In this demo session, we are going to demonstrate common marketing Machine Learning use cases how to build, train, eval and predict, your own scalable machine learning models using SQL language. The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as: – Multiclass logistic regression for classification – K-means clustering – Matrix factorization – ARIMA time series predictions – Import TensorFlow models for prediction in BigQuery Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.

DevFest Romania 2020 Keynote: Bringing the Cloud to you.

Márton Kodok

Next OnAir 20 in review, Real-time AI solutions like anomaly detection, pattern recognition, and predictive forecasting 2. Recommendations AI rich experience to personalized product recommendations 3. Media Translation API real-time speech translation from streaming audio 4. Lending DocAI solution powered by Document AI for mortgage industry 5. Contact Center AI support over chat/voice calls by identifying intent and providing assistance Confidential VMs are a breakthrough technology that allow customers to encrypt their most sensitive data in the cloud while it's being processed Cloud Run: - Minimum idle instances - Allocate 4 vCPUs and 4GiB memory - Requests up to 60 minutes - Server-side HTTP + gRPC streaming - VPC access support - External Load Balancing Serverless orchestration and automation with Cloud Workflows (beta) - Steps defined in YAML - Built-in decision and conditional exec - Subworkflows - Support for external API calls - Custom predicate for retries Predict, recommend and forecast with BigQuery ML CREATE MODEL syntax in BigQuery to run Machine Learning tasks Supported models: - K-means clustering for data segmentation - Recommend with Matrix Factorization - Perform time-series forecast - Import TensorFlow models Single interface for multiple services with API Gateway Find Your Topic and Skill Level Qwiklabs + New Tutorials Center

BigQuery ML - Machine learning at scale using SQL

Márton Kodok

With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery. We will see how can we use CREATE MODEL sql syntax to build different models such as: -Linear regression -Multiclass logistic regression for classification -K-means clustering -Import TensorFlow models for prediction in BigQuery We will see how we can apply these models on tabular data in retail and marketing use cases. Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.

Applying BigQuery ML on e-commerce data analytics

Márton Kodok

With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. We are going to demonstrate common marketing Machine Learning use cases we do at REEA.net to build, train, eval and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases: Customer Segmentation Customer Lifetime Value (LTV) prediction Conversion/Purchase prediction The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as: Multiclass logistic regression for classification K-means clustering Import TensorFlow models for prediction in BigQuery Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor

Supercharge your data analytics with BigQuery

Márton Kodok

Powering interactive data analysis require massive architecture, and Know-How to build a fast real-time computing system. BigQuery solves this problem by enabling super-fast, SQL-like queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, creating tables, columns, views, working with partitions, clustering for cost optimizations, streaming inserts, User Defined Functions, and several use cases for everydaay developer: funnel analytics, behavioral analytics, exploring unstructured data. The other part will be about BigQuery ML, which enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.

Vibe Koli 2019 - Utazás az egyetem padjaitól a Google Developer Expertig

Márton Kodok

VIBE Koli 2019 - Vibe Garázs - Gokart. Kodok Márton, miután elvégezte tanulmányait a Sapientián, IT-s karriert épített ki magának, ma pedig már tagja a Google Developer Expert (GDE) csapatának, így az ország kiemelkedő szakemberei közé tartozik. A VIBE Kolin abban segít neked, hogy megtaláld a saját utad. Bebizonyítja, csak akaraterő kell ahhoz, hogy egy társadhoz képest mást, többet csinálj.

BigQuery ML - Machine learning at scale using SQL

Márton Kodok

With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery. We will see how can we use CREATE MODEL sql syntax to build different models such as: Linear regression Multiclass logistic regression for classification K-means clustering Import TensorFlow models for prediction in BigQuery We will see how we can apply these models on tabular data in retail and marketing use cases. Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.

Google Cloud Platform Solutions for DevOps Engineers

Márton Kodok

learn the DevOps essentials about cloud components, FaaS, PaaS architectural patterns that make use of Cloud Functions, Pub/Sub, Dataflow, Kubernetes and how we develop and deploy cloud software. You will get hands on information how to build, run, monitor highly scalable and flexible applications optimized to run on GCP. We will discuss cloud concepts and highlights various design patterns and best practices.

GDG DevFest Romania - Architecting for the Google Cloud Platform

Márton Kodok

Learn about FaaS, PaaS architectural patterns that make use of Cloud Functions, Pub/Sub, Dataflow, Kubernetes and platforms that hides the management of servers from the user and have changed how we develop and deploy future software. We discuss the difference between an event-driven approach - this means that you can trigger a function whenever something interesting happens within the cloud environment - and the simpler HTTP approach. Quota and pricing of per invocation, and the advantages and disadvantages of the serverless systems.

More from Márton Kodok (20)

Gen Apps on Google Cloud PaLM2 and Codey APIs in Action

DevBCN Vertex AI - Pipelines for your MLOps workflows

Discover BigQuery ML, build your own CREATE MODEL statement

Cloud Run - the rise of serverless and containerization

BigQuery best practices and recommendations to reduce costs with BI Engine, S...

Vertex AI - Unified ML Platform for the entire AI workflow on Google Cloud

Vertex AI: Pipelines for your MLOps workflows

Cloud Workflows What's new in serverless orchestration and automation

Serverless orchestration and automation with Cloud Workflows

BigdataConference Europe - BigQuery ML

DevFest Romania 2020 Keynote: Bringing the Cloud to you.

BigQuery ML - Machine learning at scale using SQL

Applying BigQuery ML on e-commerce data analytics

Supercharge your data analytics with BigQuery

Vibe Koli 2019 - Utazás az egyetem padjaitól a Google Developer Expertig

BigQuery ML - Machine learning at scale using SQL

Google Cloud Platform Solutions for DevOps Engineers

GDG DevFest Romania - Architecting for the Google Cloud Platform

Recently uploaded

openEuler Case Study - The Journey to Supply Chain Security

Shane Coughlan

Webinar: Salesforce Document Management 2.0 - Smarter, Faster, Better

XfilesPro

Enhancing Research Orchestration Capabilities at ORNL.pdf

Globus

Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.

Orion Context Broker introduction 20240604

Fermin Galan

How Recreation Management Software Can Streamline Your Operations.pptx

wottaspaceseo

Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.

First Steps with Globus Compute Multi-User Endpoints

Globus

In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.

Pro Unity Game Development with C-sharp Book

abdulrafaychaudhry

BoxLang: Review our Visionary Licenses of 2024

Ortus Solutions, Corp

Text-Summarization-of-Breaking-News-Using-Fine-tuning-BART-Model.pptx

ShamsuddeenMuhammadA

In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...

Juraj Vysvader

How to Position Your Globus Data Portal for Success Ten Good Practices

Globus

Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.

Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...

Globus

The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.

Top 7 Unique WhatsApp API Benefits | Saudi Arabia

Yara Milbes

Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience. In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.

Quarkus Hidden and Forbidden Extensions

Max Andersen

Atelier - Innover avec l’IA Générative et les graphes de connaissances

Neo4j

Atelier - Innover avec l’IA Générative et les graphes de connaissances Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement. Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.

A Sighting of filterA in Typelevel Rite of Passage

Philip Schwarz

Top Features to Include in Your Winzo Clone App for Business Growth (4).pptx

rickgrimesss22

Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...

Globus

The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.

A Study of Variable-Role-based Feature Enrichment in Neural Models of Code

Aftab Hussain

Understanding variable roles in code has been found to be helpful by students in learning programming -- could variable roles help deep neural models in performing coding tasks? We do an exploratory study. - These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia

Cracking the code review at SpringIO 2024

Paco van Beckhoven

Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production. Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process? In this session we will cover: - The Art of Effective Code Reviews - Streamlining the Review Process - Elevating Reviews with Automated Tools By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces

Recently uploaded (20)

openEuler Case Study - The Journey to Supply Chain Security

Webinar: Salesforce Document Management 2.0 - Smarter, Faster, Better

Enhancing Research Orchestration Capabilities at ORNL.pdf

Orion Context Broker introduction 20240604

How Recreation Management Software Can Streamline Your Operations.pptx

First Steps with Globus Compute Multi-User Endpoints

Pro Unity Game Development with C-sharp Book

BoxLang: Review our Visionary Licenses of 2024

Text-Summarization-of-Breaking-News-Using-Fine-tuning-BART-Model.pptx

In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...

How to Position Your Globus Data Portal for Success Ten Good Practices

Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...

Top 7 Unique WhatsApp API Benefits | Saudi Arabia

Quarkus Hidden and Forbidden Extensions

Atelier - Innover avec l’IA Générative et les graphes de connaissances

A Sighting of filterA in Typelevel Rite of Passage

Top Features to Include in Your Winzo Clone App for Business Growth (4).pptx

Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...

A Study of Variable-Role-based Feature Enrichment in Neural Models of Code

Cracking the code review at SpringIO 2024

Arhitectural Patterns - Message Queues (Beanstalkd)

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

More from Márton Kodok

More from Márton Kodok (20)

Recently uploaded

Recently uploaded (20)