A brief technical introduction to Apache BookKeeper, the scalable, fault-tolerant, and low-latency storage service optimized for real-time and streaming workloads.
Apache BookKeeper: A High Performance and Low Latency Storage ServiceSijie Guo
Apache BookKeeper is a high performance and low latency storage service optimized for storing immutable and append-only data (such as log, streaming events, and objects). Sijie Guo and JV shares the experienced with Apache BookKeeper. This talk covers the motivation and overview of BookKeeper, dives into implementation details and describes the use cases built upon it.
High performance messaging with Apache PulsarMatteo Merli
Apache Pulsar is being used for an increasingly broad array of data ingestion tasks. When operating at scale, it's very important to ensure that the system can make use of all the available resources. Karthik Ramasamy and Matteo Merli share insights into the design decisions and the implementation techniques that allow Pulsar to achieve high performance with strong durability guarantees.
Pulsar - flexible pub-sub for internet scaleMatteo Merli
Pub-Sub messaging is a very convenient abstraction that allows system and application developers to decouple components and let them communicate, by acting as durable buffer for transient data, or as a persistent log from where to recover after crashes. This talk will present an overview of Apache Pulsar, the reasons that led to its development and how it enabled many teams at Yahoo and to build scalable and reliable applications. Apache Pulsar has become the defacto pub-sub messaging at Yahoo serving 100+ applications and processing 100’s of billions of messages for over 3+ years.
In this talk, we will explore in detail different categories of use cases that highlight how Pulsar can be applied to solve a broad range of problems thanks to its flexible messaging model that supports both queuing and streaming semantics with a focus on durability and transaction guarantees.
Matteo Merli and Sijie Guo from Streamlio gave a hands-on workshop on Apache Pulsar. #fast #durable #pubsub #messaging system. A low latency alternative to #kafka.
Apache BookKeeper: A High Performance and Low Latency Storage ServiceSijie Guo
Apache BookKeeper is a high performance and low latency storage service optimized for storing immutable and append-only data (such as log, streaming events, and objects). Sijie Guo and JV shares the experienced with Apache BookKeeper. This talk covers the motivation and overview of BookKeeper, dives into implementation details and describes the use cases built upon it.
High performance messaging with Apache PulsarMatteo Merli
Apache Pulsar is being used for an increasingly broad array of data ingestion tasks. When operating at scale, it's very important to ensure that the system can make use of all the available resources. Karthik Ramasamy and Matteo Merli share insights into the design decisions and the implementation techniques that allow Pulsar to achieve high performance with strong durability guarantees.
Pulsar - flexible pub-sub for internet scaleMatteo Merli
Pub-Sub messaging is a very convenient abstraction that allows system and application developers to decouple components and let them communicate, by acting as durable buffer for transient data, or as a persistent log from where to recover after crashes. This talk will present an overview of Apache Pulsar, the reasons that led to its development and how it enabled many teams at Yahoo and to build scalable and reliable applications. Apache Pulsar has become the defacto pub-sub messaging at Yahoo serving 100+ applications and processing 100’s of billions of messages for over 3+ years.
In this talk, we will explore in detail different categories of use cases that highlight how Pulsar can be applied to solve a broad range of problems thanks to its flexible messaging model that supports both queuing and streaming semantics with a focus on durability and transaction guarantees.
Matteo Merli and Sijie Guo from Streamlio gave a hands-on workshop on Apache Pulsar. #fast #durable #pubsub #messaging system. A low latency alternative to #kafka.
October 2016 HUG: Pulsar, a highly scalable, low latency pub-sub messaging s...Yahoo Developer Network
Yahoo recently open-sourced Pulsar, a highly scalable, low latency pub-sub messaging system running on commodity hardware. It provides simple pub-sub messaging semantics over topics, guaranteed at-least-once delivery of messages, automatic cursor management for subscribers, and cross-datacenter replication. Pulsar is used across various Yahoo applications for large scale data pipelines. Learn more about Pulsar architecture and use-cases in this talk.
Speakers:
Matteo Merli from Pulsar team at Yahoo
Effectively-once semantics in Apache PulsarMatteo Merli
“Exactly-once” is a controversial term in the messaging landscape. In this presentation we offer a detailed look at effectively-once delivery semantics in Apache Pulsar and how this is achieved without sacrificing performance.
Matteo Merli, the tech lead for Cloud Messaging Service at Yahoo, went through their design decisions, how they reached that and how they leverage Apache BookKeeper to implement a multi-tenant messaging service.
Linked In Stream Processing Meetup - Apache PulsarKarthik Ramasamy
Apache Pulsar is the next generation messaging system that uses a fundamentally different architecture to achieve durability, performance, scalability, efficiency, multi-tenancy and geo replication.
This is an overview of interesting features from Apache Pulsar. Keep in mind that by the time I did this presentation I did not have used Pulsar yet. It's just my first impressions from the list of features.
Unify Storage Backend for Batch and Streaming Computation with Apache Pulsar_...StreamNative
Nowadays, real-time computation is heavily used in cases such as online product recommendation, online payment fraud detection and etc.. In the streaming pipeline, Kafka is normally used to store a day/week data, but won't store years-long data, as in looking at the trend historically. So, a batch pipeline is needed for historical data computation. Thus, it's where the Lambda architecture comes in. Lambda has been proved to be effective, and a good balance of speed and reliability. We have been running many systems with Lambda architecture for many years. But the biggest detraction to Lambda architecture has been the need to maintain two distinct (and possibly complex) systems to generate both batch and streaming layers. With that, we have to split our business logic into many segments across different places, which is a challenge to maintain as the business grows and it also increases communication overhead. Secondly, the data are duplicated in two different systems, and we have to move data among different systems for processing. With those challenges, we have been searching for alternatives and found Apache Pulsar a great fit. In this topic, I will show how we solve those problems with Apache Pulsar by making pulsar a unified storage backend for both batch and streaming pipeline, a solution that simplifies the s/w stack, lifts up our work efficiency and lowers the cost at the same time.
KSQL is an open source, Apache 2.0 licensed streaming SQL engine that enables stream processing against Apache Kafka. KSQL makes it easy to read, write, and process streaming data in real-time, at scale, using SQL-like semantics.
Scalability, fault tolerance, distributed log…these are terms which we hear more and more these days. Make them happen is quite a challenge sometimes especially if our business need to be data intensive, agile and fast to market.
One way to answer to this challenge is microservices. These are small services that communicate to each other to deliver business value. The key word here is _communication_. Without communication all the power of microservices falls apart. And communication is not a trivial fact when involves systems with multiple data systems that are talking to one another over many channels. Each of the channel requiring their own protocol and communication methods. This is where communication can become a bottleneck if not handled properly.
One answer to this problem is Kafka, a distributed messaging system providing fast, highly scalable and redundant message exchange using a publish-subscribe model. And when we talk about fast we talk about one of the fastest messaging systems out there.
This presentation will show you an alternative way of doing microservices with event-driven architecture through Kafka.
Presenters:
Laszlo-Robert Albert (albertlaszlorobert [at] gmail [dot] com)
Dan Balescu (dfbalescu [at] gmail [dot] com)
A Unified Platform for Real-time Storage and ProcessingStreamNative
In this presentation, Yijie Shen presents how to build a unified platform for real-time storage and processing using Apache Pulsar and Apache Spark. He demonstrates the solution using Apache Pulsar as the Stream Storage and Apache Spark for Processing, and deep-dives into the implementation details of the integration between Apache Pulsar and Apache Spark.
Pulsar Storage on BookKeeper _Seamless EvolutionStreamNative
Apache Pulsar has a distinct architecture from other messaging systems. There is a clear separation of the compute layer that does message processing and dispatching, from the storage layer that handles persistent message storage, using Apache Bookkeeper. This separation of concerns leads to a very efficient design, in terms of performance and cost.
Messaging systems that provide guaranteed delivery, when used in production use cases, impose on the underlying storage, demands that are very different from simple benchmark scenarios that test write throughput. Pulsar, with both I/O isolation and separation of concerns, performs better than other messaging systems in production use cases. The strategy of I/O isolation provides better performance from each storage node at less cost, and the separation between computing and storage means that compute nodes can be scaled independently from storage. Irrespective of the choice of storage, Pulsar can be configured to get the best performance for any of those storage configurations.
This paper also discusses how some of the latest technologies like NVMe and Persistent Memory can be leveraged at a very low cost overhead, by Pulsar, without any architectural or design changes, with some data from real use cases. The fundamental choice of using Bookkeeper as the storage layer for Pulsar is validated from our experience.
Apache Con 2021 : Apache Bookkeeper Key Value Store and use casesShivji Kumar Jha
In order to leverage the best performance characters of your data or stream backend, it is important to understand the nitty gritty details of how your backend store and compute works, how data is stored, how is it indexed and how the read path is. Understanding this empowers you to design your use case solutioning so as to make the best use of resources at hand as well as get the optimum amount of consistency, availability, latency and throughput for a given amount of resources at hand.
With this underlying philosophy, in this slide deck, we will get to the bottom of storage tier of pulsar (apache bookkeeper), the barebones of the bookkeeper storage semantics, how it is used in different use cases ( even other than pulsar), understand the object models of storage in pulsar, different kinds of data structures and algorithms pulsar uses therein and how that maps to the semantics of the storage class shipped with pulsar by default. Oh yes, you can change the storage backend too with some additional code!
The focus will be more on storage backend so as to not keep this tailored to pulsar specifically but to be able to apply it different data stores or streams.
Messaging, storage, or both? The real time story of Pulsar and Apache Distri...Streamlio
Modern enterprises produce data at increasingly high volume and velocity. To process data in real time, new types of storage systems have been designed, implemented, and deployed. This presentation from Strata 2017 in New York provides an overview of Apache DistributedLog and Pulsar, real-time storage systems built using Apache BookKeeper and used heavily in production.
October 2016 HUG: Pulsar, a highly scalable, low latency pub-sub messaging s...Yahoo Developer Network
Yahoo recently open-sourced Pulsar, a highly scalable, low latency pub-sub messaging system running on commodity hardware. It provides simple pub-sub messaging semantics over topics, guaranteed at-least-once delivery of messages, automatic cursor management for subscribers, and cross-datacenter replication. Pulsar is used across various Yahoo applications for large scale data pipelines. Learn more about Pulsar architecture and use-cases in this talk.
Speakers:
Matteo Merli from Pulsar team at Yahoo
Effectively-once semantics in Apache PulsarMatteo Merli
“Exactly-once” is a controversial term in the messaging landscape. In this presentation we offer a detailed look at effectively-once delivery semantics in Apache Pulsar and how this is achieved without sacrificing performance.
Matteo Merli, the tech lead for Cloud Messaging Service at Yahoo, went through their design decisions, how they reached that and how they leverage Apache BookKeeper to implement a multi-tenant messaging service.
Linked In Stream Processing Meetup - Apache PulsarKarthik Ramasamy
Apache Pulsar is the next generation messaging system that uses a fundamentally different architecture to achieve durability, performance, scalability, efficiency, multi-tenancy and geo replication.
This is an overview of interesting features from Apache Pulsar. Keep in mind that by the time I did this presentation I did not have used Pulsar yet. It's just my first impressions from the list of features.
Unify Storage Backend for Batch and Streaming Computation with Apache Pulsar_...StreamNative
Nowadays, real-time computation is heavily used in cases such as online product recommendation, online payment fraud detection and etc.. In the streaming pipeline, Kafka is normally used to store a day/week data, but won't store years-long data, as in looking at the trend historically. So, a batch pipeline is needed for historical data computation. Thus, it's where the Lambda architecture comes in. Lambda has been proved to be effective, and a good balance of speed and reliability. We have been running many systems with Lambda architecture for many years. But the biggest detraction to Lambda architecture has been the need to maintain two distinct (and possibly complex) systems to generate both batch and streaming layers. With that, we have to split our business logic into many segments across different places, which is a challenge to maintain as the business grows and it also increases communication overhead. Secondly, the data are duplicated in two different systems, and we have to move data among different systems for processing. With those challenges, we have been searching for alternatives and found Apache Pulsar a great fit. In this topic, I will show how we solve those problems with Apache Pulsar by making pulsar a unified storage backend for both batch and streaming pipeline, a solution that simplifies the s/w stack, lifts up our work efficiency and lowers the cost at the same time.
KSQL is an open source, Apache 2.0 licensed streaming SQL engine that enables stream processing against Apache Kafka. KSQL makes it easy to read, write, and process streaming data in real-time, at scale, using SQL-like semantics.
Scalability, fault tolerance, distributed log…these are terms which we hear more and more these days. Make them happen is quite a challenge sometimes especially if our business need to be data intensive, agile and fast to market.
One way to answer to this challenge is microservices. These are small services that communicate to each other to deliver business value. The key word here is _communication_. Without communication all the power of microservices falls apart. And communication is not a trivial fact when involves systems with multiple data systems that are talking to one another over many channels. Each of the channel requiring their own protocol and communication methods. This is where communication can become a bottleneck if not handled properly.
One answer to this problem is Kafka, a distributed messaging system providing fast, highly scalable and redundant message exchange using a publish-subscribe model. And when we talk about fast we talk about one of the fastest messaging systems out there.
This presentation will show you an alternative way of doing microservices with event-driven architecture through Kafka.
Presenters:
Laszlo-Robert Albert (albertlaszlorobert [at] gmail [dot] com)
Dan Balescu (dfbalescu [at] gmail [dot] com)
A Unified Platform for Real-time Storage and ProcessingStreamNative
In this presentation, Yijie Shen presents how to build a unified platform for real-time storage and processing using Apache Pulsar and Apache Spark. He demonstrates the solution using Apache Pulsar as the Stream Storage and Apache Spark for Processing, and deep-dives into the implementation details of the integration between Apache Pulsar and Apache Spark.
Pulsar Storage on BookKeeper _Seamless EvolutionStreamNative
Apache Pulsar has a distinct architecture from other messaging systems. There is a clear separation of the compute layer that does message processing and dispatching, from the storage layer that handles persistent message storage, using Apache Bookkeeper. This separation of concerns leads to a very efficient design, in terms of performance and cost.
Messaging systems that provide guaranteed delivery, when used in production use cases, impose on the underlying storage, demands that are very different from simple benchmark scenarios that test write throughput. Pulsar, with both I/O isolation and separation of concerns, performs better than other messaging systems in production use cases. The strategy of I/O isolation provides better performance from each storage node at less cost, and the separation between computing and storage means that compute nodes can be scaled independently from storage. Irrespective of the choice of storage, Pulsar can be configured to get the best performance for any of those storage configurations.
This paper also discusses how some of the latest technologies like NVMe and Persistent Memory can be leveraged at a very low cost overhead, by Pulsar, without any architectural or design changes, with some data from real use cases. The fundamental choice of using Bookkeeper as the storage layer for Pulsar is validated from our experience.
Apache Con 2021 : Apache Bookkeeper Key Value Store and use casesShivji Kumar Jha
In order to leverage the best performance characters of your data or stream backend, it is important to understand the nitty gritty details of how your backend store and compute works, how data is stored, how is it indexed and how the read path is. Understanding this empowers you to design your use case solutioning so as to make the best use of resources at hand as well as get the optimum amount of consistency, availability, latency and throughput for a given amount of resources at hand.
With this underlying philosophy, in this slide deck, we will get to the bottom of storage tier of pulsar (apache bookkeeper), the barebones of the bookkeeper storage semantics, how it is used in different use cases ( even other than pulsar), understand the object models of storage in pulsar, different kinds of data structures and algorithms pulsar uses therein and how that maps to the semantics of the storage class shipped with pulsar by default. Oh yes, you can change the storage backend too with some additional code!
The focus will be more on storage backend so as to not keep this tailored to pulsar specifically but to be able to apply it different data stores or streams.
Messaging, storage, or both? The real time story of Pulsar and Apache Distri...Streamlio
Modern enterprises produce data at increasingly high volume and velocity. To process data in real time, new types of storage systems have been designed, implemented, and deployed. This presentation from Strata 2017 in New York provides an overview of Apache DistributedLog and Pulsar, real-time storage systems built using Apache BookKeeper and used heavily in production.
How Pulsar Stores Your Data - Pulsar Summit NA 2021StreamNative
In order to leverage the best performance characters of your stream backend, it is important to understand the nitty gritty details of how pulsar stores your data. Understanding this empowers you to design your use case solutioning so as to make the best use of resources at hand as well as get the optimum amount of consistency, availability, latency and throughput for a given amount of resources at hand.
With this underlying philosophy, in this talk, we will get to the bottom of storage tier of pulsar (apache bookkeeper), the barebones of the bookkeeper storage semantics, how it is used in different use cases ( even other than pulsar), understand the object models of storage in pulsar, different kinds of data structures and algorithms pulsar uses therein and how that maps to the semantics of the storage class shipped with pulsar by default. Oh yes, you can change the storage backend too with some additional code!
This session will empower you with the right background to map your data right with pulsar.
Optimizing Latency-sensitive queries for Presto at Facebook: A Collaboration ...Alluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Optimizing Latency-sensitive queries for Presto at Facebook: A Collaboration between Presto & Alluxio
Ke Wang, Software Engineer (Facebook)
Bin Fan, Founding Engineer, VP Of Open Source (Alluxio)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Kafka Tiered Storage separates compute and data storage in two independently scalable layers. Uber's Kafka Improvement Proposal (KIP) #405 describes two-tiered storage, which is a major step towards cloud-native Kafka. It stores the most recent data locally and offloads older data to a remote storage service. Operationally, the benefit is faster routine cluster maintenance activities. In Linkedin, Kafka tiered storage is strongly desired to reduce the cost of running Kafka in the Azure cloud environment. As KIP-405 does not dictate the implementation of remote storage substrate, Linkedin's choice for tiering Kafka in Azure deployments is the Azure Blob Service. This presentation will begin with the motivation behind Linkedin efforts to adopt Kafka Tiered Storage. Next, the architecture of KIP-405 will be discussed. Finally, the Remote Storage Manager for Azure Blobs, which is a work-in-progress, will be presented.
Video: https://youtu.be/V5gaBE5CMwg?t=1387
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Integrating Apache Pulsar with Big Data EcosystemStreamNative
In Apache Pulsar Beijing Meetup, Yijieshen gave a presentation of the current state of Apache Pulsar integrating with Big Data Ecosystem. He explains why and how Pulsar fits into current big data computing and query engines, and how Pulsar integrates with Spark, Flink and Presto for unified data processing system.
At Hootsuite, we've been transitioning from a single monolithic PHP application to a set of scalable Scala-based microservices. To avoid excessive coupling between services, we've implemented an event system using Apache Kafka that allows events to be reliably produced + consumed asynchronously from services as well as data stores.
In this presentation, I talk about:
- Why we chose Kafka
- How we set up our Kafka clusters to be scalable, highly available, and multi-data-center aware.
- How we produce + consume events
- How we ensure that events can be understood by all parts of our system (Some that are implemented in other programming languages like PHP and Python) and how we handle evolving event payload data.
Real-Time Data Exploration and Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Elasticsearch is a fully featured search engine used for real-time analytics, and Amazon Elasticsearch Service makes it easy to deploy Elasticsearch clusters on AWS. With Amazon ES, you can ingest and process billions of events per day, and explore the data using Kibana to discover patterns. In this session, we use Apache web logs as example and show you how to build an end-to-end analytics solution.
What are the key considerations people should look at to decide on the right technology to meet their messaging and queuing need? This presentation provides an overview of key requirements and introduces Apache Pulsar, the open source messaging and queuing solution.
Why you should care about data layout in the file system with Cheng Lian and ...Databricks
Efficient data access is one of the key factors for having a high performance data processing pipeline. Determining the layout of data values in the filesystem often has fundamental impacts on the performance of data access. In this talk, we will show insights on how data layout affects the performance of data access. We will first explain how modern columnar file formats like Parquet and ORC work and explain how to use them efficiently to store data values. Then, we will present our best practice on how to store datasets, including guidelines on choosing partitioning columns and deciding how to bucket a table.
Infinite Topic Backlogs with Apache PulsarStreamlio
A look at how the scalable storage architecture of Apache Pulsar makes it possible to retain and access any length of event or message history in Pulsar.
Streamlio and IoT analytics with Apache PulsarStreamlio
To keep up with fast-moving IoT data, you need technology that can collect, process and store data with performance and scalability. This presentation from Data Day Texas looks at the technology requirements and how Apache Pulsar can help to meet them.
Strata London 2018: Multi-everything with Apache PulsarStreamlio
Ivan Kelly offers an overview of Apache Pulsar, a durable, distributed messaging system, underpinned by Apache BookKeeper, that provides the enterprise features necessary to guarantee that your data is where is should be and only accessible by those who should have access. Ivan explores the features built into Pulsar that will help your organization stay in compliance with key requirements and regulations, for multi-data center replication, multi-tenancy, role-based access control, and end-to-end encryption. Ivan concludes by explaining why Pulsar’s multi-data center story will alleviate headaches for the operations teams ensuring compliance with GDPR.
Self Regulating Streaming - Data Platforms Conference 2018Streamlio
Streamlio's Karthik Ramasamy takes a look how the Apache Heron streaming platform uses built-in intelligence to automatically regulate data flow and ensure resiliency.
This presentation examines use cases for event-driven data processing and explains Streamlio's technology and how it applies to handling streaming event data.
Stream-Native Processing with Pulsar FunctionsStreamlio
The Apache Pulsar messaging solution can perform lightweight, extensible processing on messaging as they stream through the system. This presentation provides an overview of this new functionality.
Dr. Karthik Ramasamy of Streamlio draws on his experience building data products at companies including Pivotal, Twitter, and Streamlio to discuss technology and best practices for designing and implementing data-driven microservices:
* The key principles of microservices and microservice architecture
* The implications of microservices for data
* The role of messaging and processing technology in connecting microservices
Distributed Crypto-Currency Trading with Apache PulsarStreamlio
Apache Pulsar was developed to address several shortcomings of existing messaging systems including geo-replication, message durability, and lower message latency.
We will implement a multi-currency quoting application that feeds pricing information to a crypto-currency trading platform that is deployed around the globe. Given the volatility of the crypto-currency prices, sub-second message latency is critical to traders. Equally important is ensuring consistent quotes are available to all geographical locations, i.e the price of Bitcoin shown to a user in the USA should be the same as it to a trader in Hong Kong.
We will highlight the advantages of Apache Pulsar over traditional messaging systems and show how its low latency and replication across multiple geographies make it ideally suited for globally distributed, real-time applications.
Autopiloting Realtime Processing in HeronStreamlio
Heron is a streaming data processing engine developed at Twitter. This presentation explains how resiliency and self-tuning have been built into Heron.
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.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
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.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
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https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
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✅More than 85 AI features are included in the AI pilot.
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
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.
First Steps with Globus Compute Multi-User EndpointsGlobus
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.
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.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
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.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
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.
2. • Distributed storage service
• Developed at Yahoo
• Designed for low latency and scalability
• Architected for resiliency and data
durability
• Users include:
Introducing Apache BookKeeper
!2
3. BookKeeper design goals
!3
Write and read streams of entries
with very low latency (< 5 ms)
Ensure that stored data is durable,
consistent, and resilient
Immediate access to data—
stream or tail data as it is written
Efficiently store and access both
historic and real-time data
4. BookKeeper key capabilities
!4
Data consistency
Simple, repeatable read
consistency model
Data durability
Built-in replication
and resiliency
Performance
Efficient distribution of
load across cluster
Flexibility
Tunable write model to
optimize balance
Scalability
Isolation of writes and
reads for consistent
performance
5. BookKeeper core concepts
!5
Entry
(aka record)
Sequence of bytes that is the
smallest unit of data storage and
access
Log
Ledger: append-only sequence
of records
Stream: unbounded, infinite
sequence of data records
1 2 3 4 5 6
1 2 3
Entry
Stream
Ledger Ledger
1 2 3 4
6. • Bookies
• Individual BookKeeper storage node
• Bookies manage access to ledgers
• Ledgers striped across bookies
• Interfaces
• Ledger API: low-level API for direct interaction
with ledgers
• Distributed Log API: higher-level abstraction for
storing and reading data
• Metadata
• Stored in ZooKeeper cluster
• Ledger and ensemble information
Bookies
BookKeeper architecture
!6
Metadata
Client Interfaces
Ledger API Log API
Ledgers
7. Data storage in BookKeeper
!7
Physical storage
Logical view Segment 1 Segment 2 Segment 3 Segment 4 Segment n
Segment ……
…
…
…
Segment 1
Segment 2
Segment n
Bookie 1
Segment 1
Segment n
Segment 3
Bookie 2
Segment 4
Segment 2
Segment 3
Bookie 3
Segment 3
Segment 4
Segment n
Bookie 4
Segment 1
Segment 2
Segment 4
Bookie 5
• Data stored in segments
• Segments striped across bookies
8. Data storage in BookKeeper
!8
Physical storage
Logical view Segment 1 Segment 2 Segment 3 Segment 4 Segment n
Segment ……
…
…
…
Segment 1
Segment 2
Segment n
Bookie 1
Segment 1
Segment n
Segment 3
Bookie 2
Segment 4
Segment 2
Segment 3
Bookie 3
Segment 3
Segment 4
Segment n
Bookie 4
Segment 1
Segment 2
Segment 4
Bookie 5
• Data stored in segments
• Storage striped across bookies
• Segments replicated across cluster
9. Write quorum
ACK quorum
• Single bookie can serve and store
thousands of ledgers
• Separation of write and read paths
• Bookies use separate I/O paths for writes,
tailing reads, and catch-up reads
• Avoid read activity impact on write latency
• Entries sorted to allow for mostly
sequential reads
Consistent, low latency performance
!9
Bookie 1 Bookie 2 Bookie 3 Bookie 4
Writer
Reader
10. Part of the Streamlio platform for fast data
!10
Interfaces
APIs Libraries & Connectivity
Real-time processing
Messaging & queuing
Stream storage
ConnectorsClientData SourceStormKafka Functional
Management
Resource Management
Metadata
Security
Monitoring
Orchestration
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