1) Apache Beam is an open source unified model for defining both batch and streaming data processing pipelines. It allows writing pipelines once that can run on multiple distributed processing backends.
2) The Beam model separates the data processing logic from runtime requirements. It defines concepts like processing time vs event time to allow portability across batch and streaming runners.
3) Beam supports extensible IO connectors and aims to allow pipelines written in one language to run on different runtimes through language-specific SDKs. Currently, Java and Python SDKs can run on backends like Apache Spark, Flink, and Google Cloud Dataflow.
At Docker, we like to “eat our own dog food” or “drink our own champagne.” Whatever your favorite phrase, the importance of a software company using their own software is critical to relating to our customers. In this talk, we will discuss how the Docker Infrastructure and engineering teams have deployed and operationalized Docker Enterprise Edition (EE) for our staging and production environments, what we have learned in the process, and how it's making Docker EE better.
At Docker, we like to “eat our own dog food” or “drink our own champagne.” Whatever your favorite phrase, the importance of a software company using their own software is critical to relating to our customers. In this talk, we will discuss how the Docker Infrastructure and engineering teams have deployed and operationalized Docker Enterprise Edition (EE) for our staging and production environments, what we have learned in the process, and how it's making Docker EE better.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Secured API Acceleration with Engineers from Amazon CloudFront and SlackAmazon Web Services
In this session, we talk about how customers running their websites or APIs on AWS can improve security while increasing performance of their applications by using Amazon CloudFront’s globally distributed edge locations. We go through architectural patterns such as TLS termination at the edge, inherent DDoS Protection, and AWS Web Application Firewall (WAF). Then Slack, makers of cloud-based team collaboration software, discuss how they are using ELB and CloudFront to accelerate their APIs.
Realizing the promise of portability with Apache BeamJ On The Beach
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam (incubating) aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms.
In this talk, I will:
Cover briefly the capabilities of the Beam model for data processing and integration with IOs, as well as the current state of the Beam ecosystem.
Discuss the benefits Beam provides regarding portability and ease-of-use.
Demo the same Beam pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Flink on Google Cloud, Apache Spark on AWS, Apache Apex on-premise).
Give a glimpse at some of the challenges Beam aims to address in the future.
Unbounded, unordered, global scale datasets are increasingly common in day-to-day business, and consumers of these datasets have detailed requirements for latency, cost, and completeness. Apache Beam defines a new data processing programming model that evolved from more than a decade of experience building Big Data infrastructure within Google, including MapReduce, FlumeJava, Millwheel, and Cloud Dataflow.
Apache Beam handles both batch and streaming use cases, offering a powerful, unified model. It neatly separates properties of the data from run-time characteristics, allowing pipelines to be portable across multiple run-time environments, both open source, including Apache Apex, Apache Flink, Apache Gearpump, Apache Spark, and proprietary. Finally, Beam's model enables newer optimizations, like dynamic work rebalancing and autoscaling, resulting in an efficient execution.
This talk will cover the basics of Apache Beam, touch on its evolution, and describe main concepts in its powerful programming model. We'll show how Beam unifies batch and streaming use cases, and show efficient execution in real-world scenarios. Finally, we'll demonstrate pipeline portability across Apache Apex, Apache Flink, Apache Spark and Google Cloud Dataflow in a live setting.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
Secured API Acceleration with Engineers from Amazon CloudFront and SlackAmazon Web Services
In this session, we talk about how customers running their websites or APIs on AWS can improve security while increasing performance of their applications by using Amazon CloudFront’s globally distributed edge locations. We go through architectural patterns such as TLS termination at the edge, inherent DDoS Protection, and AWS Web Application Firewall (WAF). Then Slack, makers of cloud-based team collaboration software, discuss how they are using ELB and CloudFront to accelerate their APIs.
Realizing the promise of portability with Apache BeamJ On The Beach
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam (incubating) aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms.
In this talk, I will:
Cover briefly the capabilities of the Beam model for data processing and integration with IOs, as well as the current state of the Beam ecosystem.
Discuss the benefits Beam provides regarding portability and ease-of-use.
Demo the same Beam pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Flink on Google Cloud, Apache Spark on AWS, Apache Apex on-premise).
Give a glimpse at some of the challenges Beam aims to address in the future.
Unbounded, unordered, global scale datasets are increasingly common in day-to-day business, and consumers of these datasets have detailed requirements for latency, cost, and completeness. Apache Beam defines a new data processing programming model that evolved from more than a decade of experience building Big Data infrastructure within Google, including MapReduce, FlumeJava, Millwheel, and Cloud Dataflow.
Apache Beam handles both batch and streaming use cases, offering a powerful, unified model. It neatly separates properties of the data from run-time characteristics, allowing pipelines to be portable across multiple run-time environments, both open source, including Apache Apex, Apache Flink, Apache Gearpump, Apache Spark, and proprietary. Finally, Beam's model enables newer optimizations, like dynamic work rebalancing and autoscaling, resulting in an efficient execution.
This talk will cover the basics of Apache Beam, touch on its evolution, and describe main concepts in its powerful programming model. We'll show how Beam unifies batch and streaming use cases, and show efficient execution in real-world scenarios. Finally, we'll demonstrate pipeline portability across Apache Apex, Apache Flink, Apache Spark and Google Cloud Dataflow in a live setting.
Portable batch and streaming pipelines with Apache Beam (Big Data Application...Malo Denielou
Apache Beam is a top-level Apache project which aims at providing a unified API for efficient and portable data processing pipeline. Beam handles both batch and streaming use cases and neatly separates properties of the data from runtime characteristics, allowing pipelines to be portable across multiple runtimes, both open-source (e.g., Apache Flink, Apache Spark, Apache Apex, ...) and proprietary (e.g., Google Cloud Dataflow). This talk will cover the basics of Apache Beam, describe the main concepts of the programming model and talk about the current state of the project (new python support, first stable version). We'll illustrate the concepts with a use case running on several runners.
Present and future of unified, portable, and efficient data processing with A...DataWorks Summit
The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
Davor Bonaci, Apache Software Foundation; Simbly, V.P. of Apache Beam; Founder/CEO at Operiant
Present and future of unified, portable and efficient data processing with Ap...DataWorks Summit
The world of big data involves an ever-changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the big data ecosystem together; it enables users to "run any data processing pipeline anywhere."
This talk will briefly cover the capabilities of the Beam model for data processing and discuss its architecture, including the portability model. We’ll focus on the present state of the community and the current status of the Beam ecosystem. We’ll cover the state of the art in data processing and discuss where Beam is going next, including completion of the portability framework and the Streaming SQL. Finally, we’ll discuss areas of improvement and how anybody can join us on the path of creating the glue that interconnects the big data ecosystem.
Speaker
Davor Bonaci, V.P. of Apache Beam; Founder/CEO at Operiant
Realizing the promise of portable data processing with Apache BeamDataWorks Summit
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere".
This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future.
Realizing the Promise of Portable Data Processing with Apache BeamDataWorks Summit
The world of big data involves an ever changing field of players. Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. In a way, Apache Beam is a glue that can connect the Big Data ecosystem together; it enables users to "run-anything-anywhere".
This talk will briefly cover the capabilities of the Beam model for data processing, as well as the current state of the Beam ecosystem. We'll discuss Beam architecture and dive into the portability layer. We'll offer a technical analysis of the Beam's powerful primitive operations that enable true and reliable portability across diverse environments. Finally, we'll demonstrate a complex pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Spark on Amazon Web Services, Apache Flink on Google Cloud, Apache Apex on-premise), and give a glimpse at some of the challenges Beam aims to address in the future.
Speaker
Davor Bonaci, Senior Software Engineer, Google
HBaseCon2017 Efficient and portable data processing with Apache Beam and HBaseHBaseCon
In this talk we introduce Apache Beam, a unified model to create efficient and portable data processing pipelines. Beam uses a single set of abstractions to implement both batch and streaming computations that can be executed in different environments, e.g. Apache Spark, Apache Flink and Google Dataflow. Beam not only does data processing, but can be used as a tool to ingest/extract data to/from different data stores including HBase. We will present interaction scenarios between HBase and Beam and explore Beam's Input/Output (IO) model and how we leverage it to provide support for HBase.
Introduction to GCP DataFlow PresentationKnoldus Inc.
In this session, we will learn about how Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.
Introduce to Apache Beam
Dive in to Beam's architecture and live demo running data pipeline on different runners such as Google Dataflow, Flink and Spark
Data Summer Conf 2018, “Building unified Batch and Stream processing pipeline...Provectus
Apache Beam is an open source, unified model and set of language-specific SDKs for defining and executing data processing pipelines, and also data ingestion and integration flows, supporting for both batch and streaming use cases. In presentation I will provide a general overview of Apache Beam and programming model comparison Apache Beam vs Apache Spark.
Practices and tools for building better API (JFall 2013)Peter Hendriks
Een belangrijke voorwaarde om goede en leesbare Java code te schrijven is om gebruik te maken van een goede API. Een goede API helpt ontwikkelaars om sneller hoogwaardige code te schrijven. Het ontwerp van een API is daarom belangrijk, zeker als er grotere systemen worden gerealiseerd in teamverband. Moderne ontwikkeltools als Eclipse, IntelliJ IDEA en FindBugs helpen met het schrijven van goede API, en het detecteren van slecht gebruik. Deze sessie gaat in op de laatste ontwikkelingen en mogelijkheden, inclusief nieuwe taalmogelijkheden in Java 8. Er wordt hierbij gebruik gemaakt van praktische situaties en concrete codevoorbeelden, gebaseerd op echte ervaringen in grote codebases. Met praktische tips en toegankelijke tools kan al een grote stap gemaakt worden om in de praktijk beter met API ontwerp om te gaan!
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...confluent
In our exclusive webinar, you'll learn why event-driven architecture is the key to unlocking cost efficiency, operational effectiveness, and profitability. Gain insights on how this approach differs from API-driven methods and why it's essential for your organization's success.
Unlocking the Power of IoT: A comprehensive approach to real-time insightsconfluent
In today's data-driven world, the Internet of Things (IoT) is revolutionizing industries and unlocking new possibilities. Join Data Reply, Confluent, and Imply as we unveil a comprehensive solution for IoT that harnesses the power of real-time insights.
Workshop híbrido: Stream Processing con Flinkconfluent
El Stream processing es un requisito previo de la pila de data streaming, que impulsa aplicaciones y pipelines en tiempo real.
Permite una mayor portabilidad de datos, una utilización optimizada de recursos y una mejor experiencia del cliente al procesar flujos de datos en tiempo real.
En nuestro taller práctico híbrido, aprenderás cómo filtrar, unir y enriquecer fácilmente datos en tiempo real dentro de Confluent Cloud utilizando nuestro servicio Flink sin servidor.
Industry 4.0: Building the Unified Namespace with Confluent, HiveMQ and Spark...confluent
Our talk will explore the transformative impact of integrating Confluent, HiveMQ, and SparkPlug in Industry 4.0, emphasizing the creation of a Unified Namespace.
In addition to the creation of a Unified Namespace, our webinar will also delve into Stream Governance and Scaling, highlighting how these aspects are crucial for managing complex data flows and ensuring robust, scalable IIoT-Platforms.
You will learn how to ensure data accuracy and reliability, expand your data processing capabilities, and optimize your data management processes.
Don't miss out on this opportunity to learn from industry experts and take your business to the next level.
La arquitectura impulsada por eventos (EDA) será el corazón del ecosistema de MAPFRE. Para seguir siendo competitivas, las empresas de hoy dependen cada vez más del análisis de datos en tiempo real, lo que les permite obtener información y tiempos de respuesta más rápidos. Los negocios con datos en tiempo real consisten en tomar conciencia de la situación, detectar y responder a lo que está sucediendo en el mundo ahora.
Eventos y Microservicios - Santander TechTalkconfluent
Durante esta sesión examinaremos cómo el mundo de los eventos y los microservicios se complementan y mejoran explorando cómo los patrones basados en eventos nos permiten descomponer monolitos de manera escalable, resiliente y desacoplada.
Purpose of the session is to have a dive into Apache, Kafka, Data Streaming and Kafka in the cloud
- Dive into Apache Kafka
- Data Streaming
- Kafka in the cloud
Build real-time streaming data pipelines to AWS with Confluentconfluent
Traditional data pipelines often face scalability issues and challenges related to cost, their monolithic design, and reliance on batch data processing. They also typically operate under the premise that all data needs to be stored in a single centralized data source before it's put to practical use. Confluent Cloud on Amazon Web Services (AWS) provides a fully managed cloud-native platform that helps you simplify the way you build real-time data flows using streaming data pipelines and Apache Kafka.
Q&A with Confluent Professional Services: Confluent Service Meshconfluent
No matter whether you are migrating your Kafka cluster to Confluent Cloud, running a cloud-hybrid environment or are in a different situation where data protection and encryption of sensitive information is required, Confluent Service Mesh allows you to transparently encrypt your data without the need to make code changes to you existing applications.
Citi Tech Talk: Event Driven Kafka Microservicesconfluent
Microservices have become a dominant architectural paradigm for building systems in the enterprise, but they are not without their tradeoffs. Learn how to build event-driven microservices with Apache Kafka
Confluent & GSI Webinars series - Session 3confluent
An in depth look at how Confluent is being used in the financial services industry. Gain an understanding of how organisations are utilising data in motion to solve common problems and gain benefits from their real time data capabilities.
It will look more deeply into some specific use cases and show how Confluent technology is used to manage costs and mitigate risks.
This session is aimed at Solutions Architects, Sales Engineers and Pre Sales, and also the more technically minded business aligned people. Whilst this is not a deeply technical session, a level of knowledge around Kafka would be helpful.
Transforming applications built with traditional messaging solutions such as TIBCO, MQ and Solace to be scalable, reliable and ready for the move to cloud
How can applications built with traditional messaging technologies like TIBCO, Solace and IBM MQ be modernised and be made cloud ready? What are the advantages to Event Streaming approaches to pub/sub vs traditional message queues? What are the strengeths and weaknesses of both approaches, and what use cases and requirements are actually a better fit for messaging than Kafka?
This session will show why the old paradigm does not work and that a new approach to the data strategy needs to be taken. It aims to show how a Data Streaming Platform is integral to the evolution of a company’s data strategy and how Confluent is not just an integration layer but the central nervous system for an organisation
Vous apprendrez également à :
• Créer plus rapidement des produits et fonctionnalités à l’aide d’une suite complète de connecteurs et d’outils de gestion des flux, et à connecter vos environnements à des pipelines de données
• Protéger vos données et charges de travail les plus critiques grâce à des garanties intégrées en matière de sécurité, de gouvernance et de résilience
• Déployer Kafka à grande échelle en quelques minutes tout en réduisant les coûts et la charge opérationnelle associés
Confluent Partner Tech Talk with Synthesisconfluent
A discussion on the arduous planning process, and deep dive into the design/architectural decisions.
Learn more about the networking, RBAC strategies, the automation, and the deployment plan.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
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.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
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.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
2. Apache Beam: Open Source data processing APIs
● Expresses data-parallel batch and streaming
algorithms using one unified API
● Cleanly separates data processing logic
from runtime requirements
● Supports execution on multiple distributed
processing runtime environments
3. The evolution of Apache Beam
MapReduce
Apache
Beam
Cloud
Dataflow
BigTable DremelColossus
FlumeMegastore Spanner
PubSub
Millwheel
4. Agenda
1. Beam Model: Model Basics
2. Extensible IO Connectors
3. Portability: Write Once, Run Anywhere
4. Demo
5. Getting Started
7. The Beam Model: asking the right questions
What results are calculated?
Where in event time are results calculated?
When in processing time are results materialized?
How do refinements of results relate?
10. PCollection<KV<String, Integer>> scores = input
.apply(Window.into(FixedWindows.of(Duration.standardMinutes(2)))
.apply(Sum.integersPerKey());
The Beam Model: Where in event time?
19. Beam Vision: mix and match SDKs and runtimes
● The Beam Model: the abstractions
at the core of Apache BeamLanguage B
SDK
Language A
SDK
Language C
SDK
Runner 1 Runner 3Runner 2
● Choice of SDK: Users write their
pipelines in a language that’s
familiar and integrated with their
other tooling
● Choice of Runners: Users choose
the right runtime for their current
needs -- on-prem / cloud, open
source / not, fully managed / not
● Scalability for Developers: Clean
APIs allow developers to contribute
modules independently
The Beam Model
Language A Language CLanguage B
The Beam Model
20. ● Beam’s Java SDK runs on multiple
runtime environments, including:
• Apache Apex
• Apache Spark
• Apache Flink
• Google Cloud Dataflow
• [in development] Apache Gearpump
● Cross-language infrastructure is in
progress.
• Beam’s Python SDK currently runs
on Google Cloud Dataflow
Beam Vision: as of March 2017
Beam Model: Fn Runners
Apache
Spark
Cloud
Dataflow
Beam Model: Pipeline Construction
Apache
Flink
Java
Java
Python
Python
Apache
Apex
Apache
Gearpump
21. Example Beam Runners
Apache Spark
● Open-source cluster-
computing framework
● Large ecosystem of
APIs and tools
● Runs on premise or in
the cloud
Apache Flink
● Open-source
distributed data
processing engine
● High-throughput and
low-latency stream
processing
● Runs on premise or in
the cloud
Google Cloud Dataflow
● Fully-managed service
for batch and stream
data processing
● Provides dynamic
auto-scaling,
monitoring tools, and
tight integration with
Google Cloud
Platform
22. How do you build an abstraction layer?
Apache
Spark
Cloud
Dataflow
Apache
Flink
????????
????????
Good afternoon! My name is Frances Perry. I’m an engineer at Google and on the project management committee for Apache Beam.Today I’m going to give an introduction to Apache Beam…
can you see me?
-- which is a new open source project for expressing both batch and streaming data processing use cases
When you use Beam, you’re focusing on your logic and your data, without letting runtime details leak through into your code.
That separation means that a Beam pipeline can be run many existing runtimes that you know and love, including Apache Spark, Apache Flink, and Google Cloud Dataflow.
To put Beam context in the broader BigData ecosystem, let’s talk briefly about its evolution.
Google published the original paper on MapReduce in 2004 -- fundamentally change the way we do distributed processing. <animate> Inside Google, kept innovating, but initially just publishing papers. In 2014, we released Google Cloud Dataflow -- included both a new programming model and fully managed service
<animate> Externally the open source community created Hadoop, and an entire ecosystem flourished around this.
Beam is bring these two streams of work.
It’s based on the Dataflow programming model, but generalized and integrated with the broader ecosystem.
Today I’m going to go into more detail on two key pieces of Apache Beam
the programming model that intuitively expresses data-parallel operations, including both batch and streaming use cases
the portability infrastructure, which lets you execute the same Beam pipeline across multiple runtimes
Next it’s time to get concrete -- I’ll show you how these concepts in practice with a demo of the same pipeline, reading from Kafka, and running on Apache Spark, Apache Flink, and Cloud Dataflow.And finally we’ll end with some pointers for getting started.
We’ll start with a brief overview of the Beam model. If you been to other talks over the last few days, you may have heard my favorite example already, so I’ll just set the context briefly.
We’re going to be be using a running example of analyzing mobile gaming logs. We’ve just launched an addictive new mobile game, where we’ve got users across the globe forming teams and scoring points on their mobile devices.
Let’s take a look at some sample data -- the points scored for a specific team. On the x-axis we’ve got event time, and the y-axis is processing time.<animate>If everything was perfect, elements would arrive in our system immediately, and so we’d see things along this dashed line. But distributed systems often don’t cooperate.<animate> Sometimes it’s not so bad. So here this event from just before 12:07 maybe just encountered a small network delay and arrives almost immediately after 12:07 <animate> But this one over here was more like 7 minutes delayed. Perhaps our user was playing in an elevator or in a subway -- so the score is delayed by a temporary lack of network connectivity. And this graph can’t even contain what we’d see if our game supports an offline mode. If a user is playing on a transatlantic flight in airplane mode, it might be hours until that flight lands and we get those scores for processing.These types of infinite, out of order data sources can be really tricky to reason about… unless you know what questions to ask.
The Beam model is based on four key questions:
What results are calculated? Are you doing computing sums, joins, histograms, machine learning models?
Where in event time are results calculated? How does the time each event originally occurred affect results? Are results aggregated for all time, in fixed windows, or as user activity sessions?
When in processing time are results materialized? Does the time each element arrives in the system affect results? How do we know when to emit a result? What do we do about data that comes in late from those pesky users playing on transatlantic flights?How do refinements relate? If we choose to emit results multiple times, is each result independent and distinct, do they build upon one another?Let’s take a quick look at how we can use this questions to build a pipeline.
Here’s a snippet from a pipeline that processes scoring results from that mobile gaming application.In Yellow, you can see the computation that we’re performing -- the what -- in this case taking team-score pairs and summing them per team.So now let’s see what happens to our sample data if we execute this in traditional batch style.
In this looping animation, the grey line represents processing time.As the pipeline executes and processes elements, they’re accumulated into the intermediate state, just under the processing time line..
When processing completes, the system emits the result in yellow.
This is pretty standard batch processing. But as we dive into the remaining three questions, that’s going to change.
Let’s start by playing with event time.
By specifying a windowing function, we can calculate independent results for different slices of event time. For example every minute, every hour, every day...
In this case, our same integer summation will output one sum every two minutes.
Now if we look at how things execute, you can see that we are calculating a independent answer for every two minute period of event time.
But we’re still waiting until the entire computation completes to emit any results. That might work fine for bounded data sets, when we’ll eventually finish processing. But it’s not going to work if we’re trying to process an infinite amount of data!
In that case we want to reduce the latency of individual results.
We do that by asking for results to be triggered based on the system’s best estimate of when it has all the input data. We call this estimate the watermark.
The watermark is drawn in green. And now the result for each window is emitted as soon as we roughly think we’re done.But again, the watermark is often just a heuristic. It’s the system’s best guess about data completeness.Right now, the watermark is too fast -- and in some cases we’re moving on without all the data. So that user who scored 9 points in the elevator is just plain out of luck. But we don’t want to be too slow either -- it’s no good if we wait to emit anything until all the flights everywhere had landed just in case someone in 16B is playing our game.
So let’s use a more sophisticated trigger to request both speculative, early firings as data is still trickling in -- and also update results if late elements arrive.
Once we do this though, we might get multiple results for the same window of event time. So we have answer the fourth question about how refined results relate. Here we choose to just continually accumulate the score.
Now, there are multiple results for each window.Some windows, like the second, produce early, incomplete results as data arrives. There’s one on time result when we think we’ve pretty much got all the data. And there are late results if additional data comes in behind the watermark, like in the first window.And because we chose to accumulate, each result includes all the elements in the window, even if they have already been part of an earlier result.
So we took an algorithm -- in this case it happened to be a simple integer summation. I could have used something more complicated -- but the animations would have gotten out of control.
And just by tweaking just a line here or there, we went through a number of use cases -- from the simple traditional batch style through to advanced streaming situations.
Just like the MapReduce model fundamentally changed the way we do distributed processing by providing the right set of abstractions, we hope that the Beam model will change the way we unify batch and streaming processing in the future.
We’ll start with a brief overview of the Beam model. If you been to other talks over the last few days, you may have heard my favorite example already, so I’ll just set the context briefly.
We’re going to be be using a running example of analyzing mobile gaming logs. We’ve just launched an addictive new mobile game, where we’ve got users across the globe forming teams and scoring points on their mobile devices.
So that was the conceptual introduction to the types of use cases the Beam model can cover.
Next, let’s talk about how the model enable portability.
The heart of Beam is the model.We have multiple language-specific SDKs for constructing a Beam pipeline. Developers often have strong opinions about their language of choice, so we want to meet them where they are. Next, we have multiple runners for executing Beam pipelines on existing distributed processing engines. This let’s the user choose the right environment for their use case. It might be on premise, or in the cloud. It might be open source. It might be fully managed. And these needs may change over time.Now each of these runners needs to execute user processing, which means they need the ability to execute code in different languages. And to do that, while making Beam components modular, we are building APIs to that cleanly specify how a runner calls language-specific progressing.
Now that was where the project is going. In reality, this is where we are.
The Java SDK runs across multiple runtimes.
However the Python SDK currently runs only on Cloud Dataflow in batch, as we’re still building out the streaming and cross-language infrastructure.
Let’s go into a bit more detail about some of these runners. I’m going to focus today on Spark, Flink, and Dataflow. These were the original three runners in Beam and are also the three I’ll be demoing today.
Many of you are probably familiar with Apache Spark. It’s a very popular choice right now in the Big Data world. It excels at in-memory and interactive computations.
Apache Flink is more of a newcomer to the broader big data scene. It’s got really clean semantics for stream processing.
And Cloud Dataflow is GCP’s fully managed service for data processing pipelines that evolved from all those years of internal work.
And though each of these runners does parallel data processing, they have some significant differences in how they go about that. And that makes it tricky to build an abstraction layer around them.
We can’t just take the intersection of the functionality of all the engines -- that’s too limited.
And on the other hand, taking the union would be a kitchen sink of chaos…
Really, Beam tries to be at the forefront of where data processing is going, both pushing functionality into and pulling patterns out of the runtime engines.Keyed State is a great example of functionality that existed in various engines for a while and enabled interesting and common use cases, and was only recently added to Beam.And vice versa, we hope that Beam will influence the roadmaps of various engines as well. For example, the semantics of Flink's DataStreams were influenced by the Beam model.
This also means there may be, at times, some divergence between the Beam model and the support a given runner has. So that's why Beam is tracking which portions of the model each runner currently supports -- and this get updated as new functionality is being built out.
So with those concepts behind us, let’s get hands on.
Finally, let’s look at how you can get started using Apache Beam
And of course, please come learn more about Beam in general.
The Beam website has all sorts of good information, including details on all the different runners.