Rolls Royce collects 0.5 TB of data per fan blade manufactured to perform analytics on design, manufacturing, and operations. Real-time data is transmitted from planes in flight. Caterpillar uses sensors and analytics to monitor equipment for maximum efficiency, saving millions through predictive maintenance. IoT analytics can involve different types and speeds of data, historical, real-time, or predictive processing, and cloud or edge deployment. Common IoT data types include time series, location, and geospatial data. Real-time, interactive, and batch processing modes exist. Data retention policies decide what is stored. Analytics provide hindsight from historical data, insight from real-time data, and foresight through prediction. Edge analytics off
How Robinhood Built a Real-Time Anomaly Detection System to Monitor and Mitig...InfluxData
Robinhood is democratizing the financial systems by offering commission-free investing and trading with the use of your phone or desktop. As exciting as that sounds to the outside world, internally, the team at Robinhood must understand the different risk vectors and build engineering solutions to mitigate these risks. In this talk, Allison will talk about how they build a real-time risk monitoring system with InfluxDB and Faust, an open-source Python stream processing library. She will review the architecture behind the system which will involve both the time series anomaly detection part (InfluxDB) and the real-time stream processing part (Faust/Kafka).
IoT Event Processing and Analytics with InfluxDB in Google Cloud | Christoph ...InfluxData
The presentation introduces a Google Cloud native architecture for collecting, processing, analyzing and archiving of events from IoT devices, vehicles as well as upstream software systems. InfluxDB and its connection to global native Google Cloud services like BigQuery or Cloud Machine Learning Engine as well as Kubernetes is at the center of the architecture. The architecture demonstrates how access to global scaling cloud services addresses use cases from the Energy Sector.
IRUS-UK presentation given by Jo Alcock at Repository Fringe 2014 (Edinburgh) on 31st July 2014. The presentation provides an overview of the IRUS-UK service, screenshots of IRUS-UK reports, and some user feedback.
How Robinhood Built a Real-Time Anomaly Detection System to Monitor and Mitig...InfluxData
Robinhood is democratizing the financial systems by offering commission-free investing and trading with the use of your phone or desktop. As exciting as that sounds to the outside world, internally, the team at Robinhood must understand the different risk vectors and build engineering solutions to mitigate these risks. In this talk, Allison will talk about how they build a real-time risk monitoring system with InfluxDB and Faust, an open-source Python stream processing library. She will review the architecture behind the system which will involve both the time series anomaly detection part (InfluxDB) and the real-time stream processing part (Faust/Kafka).
IoT Event Processing and Analytics with InfluxDB in Google Cloud | Christoph ...InfluxData
The presentation introduces a Google Cloud native architecture for collecting, processing, analyzing and archiving of events from IoT devices, vehicles as well as upstream software systems. InfluxDB and its connection to global native Google Cloud services like BigQuery or Cloud Machine Learning Engine as well as Kubernetes is at the center of the architecture. The architecture demonstrates how access to global scaling cloud services addresses use cases from the Energy Sector.
IRUS-UK presentation given by Jo Alcock at Repository Fringe 2014 (Edinburgh) on 31st July 2014. The presentation provides an overview of the IRUS-UK service, screenshots of IRUS-UK reports, and some user feedback.
Latency is a key indicator of service quality, and important to measure and track. However, measuring latency correctly is not easy. In contrast to familiar metrics like CPU utilization or request counts, the "latency" of a service is not easily expressed in numbers. Percentile metrics have become a popular means to measure the request latency, but have several shortcomings, especially when it comes to aggregation. The situation is particularly dire if we want to use them to specify Service Level Objectives (SLOs) that quantify the performance over a longer time horizons. In the talk we will explain these pitfalls, and suggest three practical methods how to implement effective Latency SLOs.
Monitoring systems will get smarter in order to keep up with the demands of tomorrow's IT architectures. Features like anomaly detection, root cause analysis, and forecasting tools will be critical components of this next level of monitoring. At the same time, the data that monitoring systems ingest is ever increasing in amount and velocity.
This session covers architectural models for advanced online analytics. We argue that stateful online computations provide a means to realize machine learning on high-velocity data. We show how alerting systems, event engines, stream aggregators, and time-series databases interact to support smart, scalable, and resilient monitoring solutions.
Heinrich Hartmann is the Chief Data Scientist at Circonus. He is driving the development of analytics methods that transform monitoring data into actionable information as part of the Circonus monitoring platform. In his prior life, Heinrich pursued an academic career as a mathematician (PhD in Bonn, Oxford). Later he transitioned into computer science and worked as consultant for a number of different companies and research institutions.
Cassandra Day London 2015: British Gas Connected Homes: 5 Things We Wish We H...DataStax Academy
Speaker(s): Josep Casals, Lead Data Engineer at British Gas Connected Homes
British Gas Connected Homes (the creators of Hive Active Heating) embarked last year in the creation of a unified platform for all their different IoT projects. The company is a subsidiary of British Gas specialising in connected devices in the home and handles data ranging from smart gas and electricity meters to connected thermostats and boilers. Jim Anning and Josep Casals will go through their experience building a unified platform with Apache Cassandra, Apache Spark and related technologies. Focus will be of the main lessons learnt and the things they wish they had known beforehand.
Learn more about InfluxData’s time series platform. InfluxDB Cloud is a fast, elastic, serverless real-time monitoring platform, dashboarding engine, analytics service and event and metrics processor. It is available on AWS, Azure and Google Cloud. Since its launch, we have been busy making updates to the product!
Join Balaji Palani, Director of Product Management, as he demonstrates the the latest features of InfluxDB Cloud. This one-hour webinar will feature a product update and Q&A time.
WWW19: SGX-PySpark: Secure Distributed Data AnalyticsLEGATO project
SGX-PySpark: Secure Distributed Data Analytics addresses how public cloud users can protect sensitive data while still preserving the same utility of data analytics.
Streaming Sensor Data with Grafana and InfluxDB | Ryan Mckinley | GrafanaInfluxData
In this session, Ryan will preview the new streaming and shared query support in Grafana. He will show how you can visualize high-resolution real-time sensor streams using InfluxDB and Grafana.
Tapjoy OpenStack Summit Paris Breakout SessionWeston Jossey
Weston Jossey describes how Tapjoy leverages OpenStack and AWS to deliver billions of daily requests all over the world. This presentation was originally delivered at OpenStack Summit Paris.
Today ‘big data’ efforts have solved many IoT analytics challenges, particularly system challenges related to large-scale data management, learning, and data visualizations. Data for ‘big data,’ however, came mostly from computer-based systems, such as transaction logs, system logs, social networks, and mobile phones. On the other hand, IoT data which is derived from the natural world, would be more detailed, fuzzy, and large. The nature of this data, assumptions, and use cases differ between old big data and new IoT data. IoT analytics designers can build on top of big data, yet the work would be far from being done. This session will explain these challenges and discuss how big data analytics is used to architect IoT solutions.
In Apache Cassandra Lunch #101, Obioma Anomnachi will discuss the use of Cassandra for IoT (Internet of Things) workloads. We will discuss data modeling for IoT, as well as different ways devices might send data back to the cluster.
Accompanying Blog: Coming Soon!
Accompanying YouTube: https://youtu.be/IJbgPAcwXQw
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
How to Develop and Operate Cloud First Data PlatformsAlluxio, Inc.
Alluxio Online Meetup
Feb 11, 2020
Speakers:
Du Li, Electronic Arts
Bin Fan, Alluxio
In cloud-based software stacks, there are varying degrees of automation across different layers: infrastructure, platform, and application. The mismatch in automation often breaks balance in devops, causing ops nightmares in platforms and applications. This talk will overview two projects at Electronic Arts (EA) that address the mismatch by data orchestration: One project automatically generates configurations for all components in a large monitoring system, which reduces the daily average number of alerts from ~1000 to ~20. The other project introduces Alluxio for caching and unifying address space across ETL and analytics workloads, which substantially simplifies architecture, improves performance, and reduces ops overheads.
How to Develop and Operate Cloud Native Data Platforms and ApplicationsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
How to Develop and Operate Cloud Native Data Platforms and Applications
Speaker:
Du Li, Electronic Arts (EA)
For more Alluxio events: https://www.alluxio.io/events/
AWS Big Data Demystified #1: Big data architecture lessons learned Omid Vahdaty
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
Latency is a key indicator of service quality, and important to measure and track. However, measuring latency correctly is not easy. In contrast to familiar metrics like CPU utilization or request counts, the "latency" of a service is not easily expressed in numbers. Percentile metrics have become a popular means to measure the request latency, but have several shortcomings, especially when it comes to aggregation. The situation is particularly dire if we want to use them to specify Service Level Objectives (SLOs) that quantify the performance over a longer time horizons. In the talk we will explain these pitfalls, and suggest three practical methods how to implement effective Latency SLOs.
Monitoring systems will get smarter in order to keep up with the demands of tomorrow's IT architectures. Features like anomaly detection, root cause analysis, and forecasting tools will be critical components of this next level of monitoring. At the same time, the data that monitoring systems ingest is ever increasing in amount and velocity.
This session covers architectural models for advanced online analytics. We argue that stateful online computations provide a means to realize machine learning on high-velocity data. We show how alerting systems, event engines, stream aggregators, and time-series databases interact to support smart, scalable, and resilient monitoring solutions.
Heinrich Hartmann is the Chief Data Scientist at Circonus. He is driving the development of analytics methods that transform monitoring data into actionable information as part of the Circonus monitoring platform. In his prior life, Heinrich pursued an academic career as a mathematician (PhD in Bonn, Oxford). Later he transitioned into computer science and worked as consultant for a number of different companies and research institutions.
Cassandra Day London 2015: British Gas Connected Homes: 5 Things We Wish We H...DataStax Academy
Speaker(s): Josep Casals, Lead Data Engineer at British Gas Connected Homes
British Gas Connected Homes (the creators of Hive Active Heating) embarked last year in the creation of a unified platform for all their different IoT projects. The company is a subsidiary of British Gas specialising in connected devices in the home and handles data ranging from smart gas and electricity meters to connected thermostats and boilers. Jim Anning and Josep Casals will go through their experience building a unified platform with Apache Cassandra, Apache Spark and related technologies. Focus will be of the main lessons learnt and the things they wish they had known beforehand.
Learn more about InfluxData’s time series platform. InfluxDB Cloud is a fast, elastic, serverless real-time monitoring platform, dashboarding engine, analytics service and event and metrics processor. It is available on AWS, Azure and Google Cloud. Since its launch, we have been busy making updates to the product!
Join Balaji Palani, Director of Product Management, as he demonstrates the the latest features of InfluxDB Cloud. This one-hour webinar will feature a product update and Q&A time.
WWW19: SGX-PySpark: Secure Distributed Data AnalyticsLEGATO project
SGX-PySpark: Secure Distributed Data Analytics addresses how public cloud users can protect sensitive data while still preserving the same utility of data analytics.
Streaming Sensor Data with Grafana and InfluxDB | Ryan Mckinley | GrafanaInfluxData
In this session, Ryan will preview the new streaming and shared query support in Grafana. He will show how you can visualize high-resolution real-time sensor streams using InfluxDB and Grafana.
Tapjoy OpenStack Summit Paris Breakout SessionWeston Jossey
Weston Jossey describes how Tapjoy leverages OpenStack and AWS to deliver billions of daily requests all over the world. This presentation was originally delivered at OpenStack Summit Paris.
Today ‘big data’ efforts have solved many IoT analytics challenges, particularly system challenges related to large-scale data management, learning, and data visualizations. Data for ‘big data,’ however, came mostly from computer-based systems, such as transaction logs, system logs, social networks, and mobile phones. On the other hand, IoT data which is derived from the natural world, would be more detailed, fuzzy, and large. The nature of this data, assumptions, and use cases differ between old big data and new IoT data. IoT analytics designers can build on top of big data, yet the work would be far from being done. This session will explain these challenges and discuss how big data analytics is used to architect IoT solutions.
In Apache Cassandra Lunch #101, Obioma Anomnachi will discuss the use of Cassandra for IoT (Internet of Things) workloads. We will discuss data modeling for IoT, as well as different ways devices might send data back to the cluster.
Accompanying Blog: Coming Soon!
Accompanying YouTube: https://youtu.be/IJbgPAcwXQw
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
How to Develop and Operate Cloud First Data PlatformsAlluxio, Inc.
Alluxio Online Meetup
Feb 11, 2020
Speakers:
Du Li, Electronic Arts
Bin Fan, Alluxio
In cloud-based software stacks, there are varying degrees of automation across different layers: infrastructure, platform, and application. The mismatch in automation often breaks balance in devops, causing ops nightmares in platforms and applications. This talk will overview two projects at Electronic Arts (EA) that address the mismatch by data orchestration: One project automatically generates configurations for all components in a large monitoring system, which reduces the daily average number of alerts from ~1000 to ~20. The other project introduces Alluxio for caching and unifying address space across ETL and analytics workloads, which substantially simplifies architecture, improves performance, and reduces ops overheads.
How to Develop and Operate Cloud Native Data Platforms and ApplicationsAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
How to Develop and Operate Cloud Native Data Platforms and Applications
Speaker:
Du Li, Electronic Arts (EA)
For more Alluxio events: https://www.alluxio.io/events/
AWS Big Data Demystified #1: Big data architecture lessons learned Omid Vahdaty
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company
The video: https://youtu.be/l5KmaZNQxaU
dont forget to subcribe to the youtube channel
The website: https://amazon-aws-big-data-demystified.ninja/
The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/
The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
Zeus: Uber’s Highly Scalable and Distributed Shuffle as a ServiceDatabricks
Zeus is an efficient, highly scalable and distributed shuffle as a service which is powering all Data processing (Spark and Hive) at Uber. Uber runs one of the largest Spark and Hive clusters on top of YARN in industry which leads to many issues such as hardware failures (Burn out Disks), reliability and scalability challenges.
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.
Anurag Awasthi - Machine Learning applications for CloudStackShapeBlue
While Machine learning and data mining has had profound impact on how we model applications and use data for better product consumption, there is scope for extending prediction algorithms to lower levels as well. Some useful applications of machine learning in ACS could be exploring better resource allocation that is aware of usage statistics, predicting faults, load balancing, etc. In this talk we will * take a broad overview of what Machine Learning/Data mining is and how it is being used in today's tech ecosystemn* explore ways in which we can make ACS more efficientn* discuss some recent advancements in how ML can benefit datacenters from research community
OSMC 2018 | Learnings, patterns and Uber’s metrics platform M3, open sourced ...NETWAYS
At Uber we use high cardinality monitoring to observe and detect issues with our 4,000 microservices running on Mesos and across our infrastructure systems and servers. We’ll cover how we put the resulting 6 billion plus time series to work in a variety of different ways, auto-discovering services and their usage of other systems at Uber, setting up and tearing down alerts automatically for services, sending smart alert notifications that rollup different failures into individual high level contextual alerts, and more. We’ll also talk about how we accomplish all this with a global view of our systems with M3, our open source metrics platform. We’ll take a deep dive look at how we use M3DB, now available as an open source Prometheus long term storage backend, to horizontally scale our metrics platform in a cost efficient manner with a system that’s still sane to operate with petabytes of metrics data.
Analytic Insights in Retail Using Apache Spark with Hari ShreedharanDatabricks
Streamsets Data Collector is designed to make data ingest and processing easy. SDC integrates at several levels with Apache Spark to make data analysis using Spark very easy. SDC works with Databricks Cloud to trigger jobs based on incoming data.
In this talk, you will learn how a larger retail player with thousands of outlets is utilizing StreamSets to power Spark jobs on the Databricks cloud, combining real-time foot traffic data and historic behavioral & transaction data for analytic insights that improve revenue per square foot.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
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.
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.
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.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
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?
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.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
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.
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).
Advanced Flow Concepts Every Developer Should KnowPeter Caitens
Tim Combridge from Sensible Giraffe and Salesforce Ben presents some important tips that all developers should know when dealing with Flows in Salesforce.
2. IoT Analytics in Action
Rolls Royce Trent 1000
Analytics data collected in
● Design
● Manufacture
● After-sales
One fan blade manufacturing -> 0.5 TB of data
Real-time data transmitted back to RR when planes are in-flight.
3. IoT Analytics in Action
Caterpillar
From autonomous mining trucks to locomotives, they have sensors monitoring fuel, idle time,
location for maximum operation efficiency.
Predictive maintenance has saved millions from timely fuel pump replacement to adjusting ship
hull cleaning intervals in their marine services.
4. IoT Analytics Categorized
● What type of data?
● How fast you need results?
● How much data to keep?
● Historical, real-time, or predictive?
● Cloud or fog / edge analytics?
5. IoT Analytics Data
● Time related data
○ Time series processing
■ Energy consumption with time
■ Failure prediction
■ Specialized DBs - OpenTSDB
● Location data
○ GPS / iBeacons
○ Used in agriculture
■ Detect soil moisture, crop growth
■ Manage irrigation equipment
○ Traffic planning
■ Monitor vehicle speeds, location for better route suggestions
○ Geospatial optimized processing engines - GeoTrellis
6. IoT Analytics Processing Modes
Do we need the results instantaneously?, or a few seconds
delay okay?, or else, results after several minutes or more is
fine?
7. IoT Analytics Processing Modes: Realtime / NRT
● The most often used processing mode in IoT
○ Immediately take action on some event occurring with the source
devices
■ Send out alerts from a temperature sensor hitting a limit
■ Notification in a car dashboard of low tire pressure
● Generating instant alerts and information based on the data sent by
sensors, requires stream processing. Process events one by one in
real-time to match to a predefined set of rules.
○ Apache Storm as a stream processing engine
■ Scalable and fault tolerant
○ For advanced pattern matching, a full fledged CEP engine can be
used, e.g. WSO2 CEP, Esper etc..
8. IoT Analytics Processing Modes: Interactive /
Batch
● Long term statistics generations, a batch processing system can be
used: Apache Hadoop, Apache Spark
○ Average temperature in a room in the last month
○ Total power usage of the house in the last year
● Interactive analytics with technologies such as Apache Drill and
indexed storage systems such as Couchbase.
● Most often, we may need to mash-up both batch analytics results with
real-time processing
○ Comparing a long term statistics result with incoming real-time
events for alerts etc..
● Batch operations can be brought together with an indexing system for
real-time analytics to lookup data instantly when required
○ Apache Lucene, WSO2 DAS Analytics / Event Tables
9. IoT Analytics Data Retention
● IoT devices generate high volume or different types of data
● We can decide to process right away when we receive it, and discard it,
or else, keep it for more detailed processing
● Big Data stores gives us the option to store huge amounts of data as
such.
● Purge the data, after the raw data is no longer required
10. IoT Analytics Processing: Hindsight/Insight/Foresight
● Hindsight can be achieved by processing historical data, and
understanding what has happened.
○ Batch processing systems such as Apache Hadoop and Apache
Spark is used in this area
○ Data visualization with dashboards, showing related data together
● Insight would be understanding what is happening now
○ Achieved with real-time processing systems
○ Scenario: How are my jet engines performing right now
● Foresight is predicting what is going to happen
○ Achieved with machine learning systems such as Apache Mahout,
Apache Spark MLlib, Microsoft Azure Machine Learning, WSO2 ML
○ Scenario: Predictive maintenance -> time to change specific parts
in my car, service scheduling on an aeroplane
11. So many things… So much processing…
● IoT will mean, naturally large amounts of data created, thus large
amount of computation resources are required
● Typical scenario of a centralized analytics server for all devices may not
be feasible all the time
○ Centralized analytics hardware may not be scalable for all the
thousands of devices getting added frequently
○ The network communication will get flooded with analytics chatter
when the device count increases
● Solution: edge analytics, a.k.a, fog analytics
○ Some of the analytics operations are offloaded to the end device
itself or to an immediate gateway, for doing most or some of the
analytics operations required. This creates a scalable infrastructure
for device management in the IoT ecosystem.