We are going to go behind the scene of building a data-intensive system. The story includes challenges I have faced and what I learned from those incidents.
https://2021.pycon.org.au/program/8hlvvs/
EuroPython 2020 - Speak python with devicesHua Chu
The talk will be a getting start guide on controlling hardware devices with Python. We know Python users are very keen on multitasking and always wish to know more about how it can be used in different tasks. This talk will help audiences exploring new Python skillset. Audiences may be inspired by this talk and apply it to many scenarios, e.g., IoT and infrastructure automation.
https://ep2020.europython.eu/talks/7kfqf76-speak-python-with-devices/
Natural Language Processing (NLP) practitioners often have to deal with analyzing large corpora of unstructured documents and this is often a tedious process. Python tools like NLTK do not scale to large production data sets and cannot be plugged into a distributed scalable framework like Apache Spark or Apache Flink.
The Apache OpenNLP library is a popular machine learning based toolkit for processing unstructured text. Combining a permissive licence, a easy-to-use API and set of components which are highly customize and trainable to achieve a very high accuracy on a particular dataset. Built-in evaluation allows to measure and tune OpenNLP’s performance for the documents that need to be processed.
From sentence detection and tokenization to parsing and named entity finder, Apache OpenNLP has the tools to address all tasks in a natural language processing workflow. It applies Machine Learning algorithms such as Perceptron and Maxent, combined with tools such as word2vec to achieve state of the art results. In this talk, we’ll be seeing a demo of large scale Name Entity extraction and Text classification using the various Apache OpenNLP components wrapped into Apache Flink stream processing pipeline and as an Apache NiFI processor.
NLP practitioners will come away from this talk with a better understanding of how the various Apache OpenNLP components can help in processing large reams of unstructured data using a highly scalable and distributed framework like Apache Spark/Apache Flink/Apache NiFi.
ppbench - A Visualizing Network Benchmark for MicroservicesNane Kratzke
Companies like Netflix, Google, Amazon, Twitter successfully exemplified elastic and scalable microservice architectures for very large systems. Microservice architectures are often realized in a way to deploy services as containers on container clusters. Containerized microservices often use lightweight and REST-based mechanisms. However, this lightweight communication is often routed by container clusters through heavyweight software defined networks (SDN). Services are often implemented in different programming languages adding additional complexity to a system, which might end in decreased performance. Astonishingly it is quite complex to figure out these impacts in the upfront of a microservice design process due to missing and specialized benchmarks. This contribution proposes a benchmark intentionally designed for this microservice setting. We advocate that it is more useful to reflect fundamental design decisions and their performance impacts in the upfront of a microservice architecture development and not in the aftermath. We present some findings regarding performance impacts of some TIOBE TOP 50 programming languages (Go, Java, Ruby, Dart), containers (Docker as type representative) and SDN solutions (Weave as type representative).
Anomaly Detection and Automatic Labeling with Deep LearningAdam Gibson
Adam Gibson demonstrates how to use variational autoencoders to automatically label time series location data. You'll explore the challenge of imbalanced classes and anomaly detection, learn how to leverage deep learning for automatically labeling (and the pitfalls of this), and discover how you can deploy these techniques in your organization.
I'll provide guidelines for thinking about empirical performance evaluation of parallel programs in general and of Spark jobs in particular. It's easier to be systematic about this if you think in terms of "what's the effective network bandwidth we're getting?" instead of "How fast does this particular job run?" In addition, the figure of merit for parallel performance isn't necessarily obvious. If you want to minimize your AWS bill you should almost certainly run on a single node (but your job may take six months to finish). You may think you want answers as quickly as possible, but if you could make a job finish in 55 minutes instead 60 minutes while doubling your AWS bill, would you do it? No? Then what exactly is the metric that you should optimize?
EuroPython 2020 - Speak python with devicesHua Chu
The talk will be a getting start guide on controlling hardware devices with Python. We know Python users are very keen on multitasking and always wish to know more about how it can be used in different tasks. This talk will help audiences exploring new Python skillset. Audiences may be inspired by this talk and apply it to many scenarios, e.g., IoT and infrastructure automation.
https://ep2020.europython.eu/talks/7kfqf76-speak-python-with-devices/
Natural Language Processing (NLP) practitioners often have to deal with analyzing large corpora of unstructured documents and this is often a tedious process. Python tools like NLTK do not scale to large production data sets and cannot be plugged into a distributed scalable framework like Apache Spark or Apache Flink.
The Apache OpenNLP library is a popular machine learning based toolkit for processing unstructured text. Combining a permissive licence, a easy-to-use API and set of components which are highly customize and trainable to achieve a very high accuracy on a particular dataset. Built-in evaluation allows to measure and tune OpenNLP’s performance for the documents that need to be processed.
From sentence detection and tokenization to parsing and named entity finder, Apache OpenNLP has the tools to address all tasks in a natural language processing workflow. It applies Machine Learning algorithms such as Perceptron and Maxent, combined with tools such as word2vec to achieve state of the art results. In this talk, we’ll be seeing a demo of large scale Name Entity extraction and Text classification using the various Apache OpenNLP components wrapped into Apache Flink stream processing pipeline and as an Apache NiFI processor.
NLP practitioners will come away from this talk with a better understanding of how the various Apache OpenNLP components can help in processing large reams of unstructured data using a highly scalable and distributed framework like Apache Spark/Apache Flink/Apache NiFi.
ppbench - A Visualizing Network Benchmark for MicroservicesNane Kratzke
Companies like Netflix, Google, Amazon, Twitter successfully exemplified elastic and scalable microservice architectures for very large systems. Microservice architectures are often realized in a way to deploy services as containers on container clusters. Containerized microservices often use lightweight and REST-based mechanisms. However, this lightweight communication is often routed by container clusters through heavyweight software defined networks (SDN). Services are often implemented in different programming languages adding additional complexity to a system, which might end in decreased performance. Astonishingly it is quite complex to figure out these impacts in the upfront of a microservice design process due to missing and specialized benchmarks. This contribution proposes a benchmark intentionally designed for this microservice setting. We advocate that it is more useful to reflect fundamental design decisions and their performance impacts in the upfront of a microservice architecture development and not in the aftermath. We present some findings regarding performance impacts of some TIOBE TOP 50 programming languages (Go, Java, Ruby, Dart), containers (Docker as type representative) and SDN solutions (Weave as type representative).
Anomaly Detection and Automatic Labeling with Deep LearningAdam Gibson
Adam Gibson demonstrates how to use variational autoencoders to automatically label time series location data. You'll explore the challenge of imbalanced classes and anomaly detection, learn how to leverage deep learning for automatically labeling (and the pitfalls of this), and discover how you can deploy these techniques in your organization.
I'll provide guidelines for thinking about empirical performance evaluation of parallel programs in general and of Spark jobs in particular. It's easier to be systematic about this if you think in terms of "what's the effective network bandwidth we're getting?" instead of "How fast does this particular job run?" In addition, the figure of merit for parallel performance isn't necessarily obvious. If you want to minimize your AWS bill you should almost certainly run on a single node (but your job may take six months to finish). You may think you want answers as quickly as possible, but if you could make a job finish in 55 minutes instead 60 minutes while doubling your AWS bill, would you do it? No? Then what exactly is the metric that you should optimize?
In Data Engineer’s Lunch #41: Pygrametl , we discussed PygramETL, a python ETL tool in order to close out our series on them.
Accompanying Blog: https://blog.anant.us/data-engineers-lunch-41-pygrametl
Accompanying YouTube: https://youtu.be/YiPuJyYLXxs
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-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
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
This presentation provides an overview of the aims and infrastructure of the Materials Project, including an overview of the open-source pymatgen materials analysis code and the Materials API.
EKAW - Publishing with Triple Pattern FragmentsRuben Taelman
Slides for the presentation on Publishing with Triple Pattern Fragments in the Modeling, Generating and Publishing knowledge as Linked Data tutorial at EKAW 2016.
Self driving computers active learning workflows with human interpretable ve...Adam Gibson
Human in the loop learning workflows leveraging deep learning to group and cluster data. Also, techniques for accounting for machine learning failures.
Recent presentation on deeplearning4j's new features as well as some underused features of the AI framework like arbiter,datavec's transform process and libnd4j.
How a Particle Accelerator Monitors Scientific Experiments Using InfluxDBInfluxData
European XFEL are the creators of the strongest x-ray beam in the world. Their 3.4-km long X-ray free-electron laser underground tunnel is used by researchers from around the world. Scientists use their facilities to map atomic details of viruses, film chemical reactions, and study the processes in the interior of planets. Discover how European XFEL uses InfluxDB to monitor their scientific experiments and research.
In this webinar, Alessandro Silenzi will dive into:
European XFEL’s approach to empowering the worldwide community to push the boundaries of science
The evolution of their data management solution — from homegrown to InfluxDB
How a time series platform is used to analyze and validate experiment data
Frossie Economou & Angelo Fausti [Vera C. Rubin Observatory] | How InfluxDB H...InfluxData
Frossie Economou & Angelo Fausti [Vera C. Rubin Observatory] | How InfluxDB Helps Vera C. Rubin Observatory Make the Deepest, Widest Image of the Universe | InfluxDays Virtual Experience NA 2020
Mathias Brandewinder, Software Engineer & Data Scientist, Clear Lines Consult...MLconf
Scripts that Scale with F# and mbrace.io:
Nothing beats interactive scripting for productive data exploration and rapid prototyping: grab data, run code, and iterate based on feedback. However, that story starts to break down once you need to process large datasets or expensive computations. Your local machine becomes the bottleneck, and your are left with a slow and unresponsive environment.
In this talk, we will demonstrate on live examples how you can have your cake and eat it, too, using mbrace.io, a free, open-source engine for scalable cloud programming. Using a simple programming model, you can keep working from your favorite scripting environment, and execute code interactively against a cluster on the Azure cloud. We will discuss the relevance of F# and mbrace in a data science and machine learning context, from parallelizing code and data processing in a functional style, to leveraging F# type providers to consume data or even run R packages.
Open Tracing, to order and understand your mess. - ApiConf 2017Gianluca Arbezzano
This about how many api calls your applications were doing 3-4 years ago, and think about how many integration and difference services your requests is crossing before to come back to the final destination. How do you know this step of your pipeline is taking too much time? What is taking 2 seconds to answer? Is it the authentication service? Maybe it's the invoice generation service or the notification platform. Open Tracing is a distributed tracing cross vendor and open source that help you to understand bottleneck and to profile the requests from where they arrive at the final user. In an ecosystem where microservices and as a service concept are growing this can be a real challenge. During this presentation, we will see how it works from a general point of view to land in some real implementation, examples, and demo.
Performance monitoring and call tracing in microservice environmentsMartin Gutenbrunner
Performance analysis can easily be done with on-board tools of nearly any programming language. In microservice environments, the real challenge is not in single, high-performing services, but in resiliently running a complex ecosystem of many services.This talk will introduce open-source tools for analysis and call tracing. Concluding, we will briefly get to know Dynatrace Ruxit - a commercial alternative. After this session, the audience will know about how to get started in performance analysis and call-tracing and some according tools.
Jake Mannix, Lead Data Engineer, Lucidworks at MLconf SEA - 5/20/16MLconf
Smarter Search With Spark-Solr: Search gets smarter when you know more about your documents and their relationship to each other (think: PageRank) and the users (i.e. popularity), in addition to what you already know about their content (text search). It also gets smarter when you know more about your users (personalization) and both their affinity for certain kinds of content and their similarities to each other (collaborative filtering recommenders).
Building all of these pieces typically requires a big mix of batch workloads to do log processing, as well as training machine-learned models to use during realtime querying, and are highly domain specific, but many techniques are fairly universal: we will discuss how Spark can interface with a Solr Cloud cluster to efficiently perform many of the pieces to this puzzle in one relatively self-contained package (no HDFS/S3, all data stored in Solr!), and introduce “spark-solr” – an open-source JVM library to facilitate this.
Big Data and Fast Data combined – is it possible ? Introduction aux architectures Big Data. M. Ulises Fasoli, Senior Consultant Trivadis. Conférence donnée dans le cadre du Swiss Data Forum du 24 novembre 2015 à Lausanne
Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (In...exponential-inc
Over the last few years we’ve seen a frenzy of interest and buzz around the area of Big Data. Beyond the hype, there is a solid base of growing use cases, which are becoming center stage to most businesses. 2012 was the year of awareness. There was a great amount of sharing from the early core developers of the analytic platforms – showing the rest of the world the capabilities of the tools and platforms that had been developed for special purpose high scale analytics. The big names at the core of open source analytics development include Facebook, eBay, Linkedin, Twitter – all blazing the trail with new approaches. These companies brought along with them a new and expanding interest in leveraging the same technologies for commercial interest.
This talk is focused at how a growing number of enterprises that are already heavily invested in the use cases – but by volume, most customers now have some form of big data proof-of-concept underway. These proof of concepts typically start with a thesis of how competitive advantage can be gained through insight from the data. A proof of concept can quickly validate the theory, and helps sell further investment in the analytics platform, and it snowballs from there.
In Data Engineer’s Lunch #41: Pygrametl , we discussed PygramETL, a python ETL tool in order to close out our series on them.
Accompanying Blog: https://blog.anant.us/data-engineers-lunch-41-pygrametl
Accompanying YouTube: https://youtu.be/YiPuJyYLXxs
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-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
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
This presentation provides an overview of the aims and infrastructure of the Materials Project, including an overview of the open-source pymatgen materials analysis code and the Materials API.
EKAW - Publishing with Triple Pattern FragmentsRuben Taelman
Slides for the presentation on Publishing with Triple Pattern Fragments in the Modeling, Generating and Publishing knowledge as Linked Data tutorial at EKAW 2016.
Self driving computers active learning workflows with human interpretable ve...Adam Gibson
Human in the loop learning workflows leveraging deep learning to group and cluster data. Also, techniques for accounting for machine learning failures.
Recent presentation on deeplearning4j's new features as well as some underused features of the AI framework like arbiter,datavec's transform process and libnd4j.
How a Particle Accelerator Monitors Scientific Experiments Using InfluxDBInfluxData
European XFEL are the creators of the strongest x-ray beam in the world. Their 3.4-km long X-ray free-electron laser underground tunnel is used by researchers from around the world. Scientists use their facilities to map atomic details of viruses, film chemical reactions, and study the processes in the interior of planets. Discover how European XFEL uses InfluxDB to monitor their scientific experiments and research.
In this webinar, Alessandro Silenzi will dive into:
European XFEL’s approach to empowering the worldwide community to push the boundaries of science
The evolution of their data management solution — from homegrown to InfluxDB
How a time series platform is used to analyze and validate experiment data
Frossie Economou & Angelo Fausti [Vera C. Rubin Observatory] | How InfluxDB H...InfluxData
Frossie Economou & Angelo Fausti [Vera C. Rubin Observatory] | How InfluxDB Helps Vera C. Rubin Observatory Make the Deepest, Widest Image of the Universe | InfluxDays Virtual Experience NA 2020
Mathias Brandewinder, Software Engineer & Data Scientist, Clear Lines Consult...MLconf
Scripts that Scale with F# and mbrace.io:
Nothing beats interactive scripting for productive data exploration and rapid prototyping: grab data, run code, and iterate based on feedback. However, that story starts to break down once you need to process large datasets or expensive computations. Your local machine becomes the bottleneck, and your are left with a slow and unresponsive environment.
In this talk, we will demonstrate on live examples how you can have your cake and eat it, too, using mbrace.io, a free, open-source engine for scalable cloud programming. Using a simple programming model, you can keep working from your favorite scripting environment, and execute code interactively against a cluster on the Azure cloud. We will discuss the relevance of F# and mbrace in a data science and machine learning context, from parallelizing code and data processing in a functional style, to leveraging F# type providers to consume data or even run R packages.
Open Tracing, to order and understand your mess. - ApiConf 2017Gianluca Arbezzano
This about how many api calls your applications were doing 3-4 years ago, and think about how many integration and difference services your requests is crossing before to come back to the final destination. How do you know this step of your pipeline is taking too much time? What is taking 2 seconds to answer? Is it the authentication service? Maybe it's the invoice generation service or the notification platform. Open Tracing is a distributed tracing cross vendor and open source that help you to understand bottleneck and to profile the requests from where they arrive at the final user. In an ecosystem where microservices and as a service concept are growing this can be a real challenge. During this presentation, we will see how it works from a general point of view to land in some real implementation, examples, and demo.
Performance monitoring and call tracing in microservice environmentsMartin Gutenbrunner
Performance analysis can easily be done with on-board tools of nearly any programming language. In microservice environments, the real challenge is not in single, high-performing services, but in resiliently running a complex ecosystem of many services.This talk will introduce open-source tools for analysis and call tracing. Concluding, we will briefly get to know Dynatrace Ruxit - a commercial alternative. After this session, the audience will know about how to get started in performance analysis and call-tracing and some according tools.
Jake Mannix, Lead Data Engineer, Lucidworks at MLconf SEA - 5/20/16MLconf
Smarter Search With Spark-Solr: Search gets smarter when you know more about your documents and their relationship to each other (think: PageRank) and the users (i.e. popularity), in addition to what you already know about their content (text search). It also gets smarter when you know more about your users (personalization) and both their affinity for certain kinds of content and their similarities to each other (collaborative filtering recommenders).
Building all of these pieces typically requires a big mix of batch workloads to do log processing, as well as training machine-learned models to use during realtime querying, and are highly domain specific, but many techniques are fairly universal: we will discuss how Spark can interface with a Solr Cloud cluster to efficiently perform many of the pieces to this puzzle in one relatively self-contained package (no HDFS/S3, all data stored in Solr!), and introduce “spark-solr” – an open-source JVM library to facilitate this.
Big Data and Fast Data combined – is it possible ? Introduction aux architectures Big Data. M. Ulises Fasoli, Senior Consultant Trivadis. Conférence donnée dans le cadre du Swiss Data Forum du 24 novembre 2015 à Lausanne
Keynote Address at 2013 CloudCon: Future of Big Data by Richard McDougall (In...exponential-inc
Over the last few years we’ve seen a frenzy of interest and buzz around the area of Big Data. Beyond the hype, there is a solid base of growing use cases, which are becoming center stage to most businesses. 2012 was the year of awareness. There was a great amount of sharing from the early core developers of the analytic platforms – showing the rest of the world the capabilities of the tools and platforms that had been developed for special purpose high scale analytics. The big names at the core of open source analytics development include Facebook, eBay, Linkedin, Twitter – all blazing the trail with new approaches. These companies brought along with them a new and expanding interest in leveraging the same technologies for commercial interest.
This talk is focused at how a growing number of enterprises that are already heavily invested in the use cases – but by volume, most customers now have some form of big data proof-of-concept underway. These proof of concepts typically start with a thesis of how competitive advantage can be gained through insight from the data. A proof of concept can quickly validate the theory, and helps sell further investment in the analytics platform, and it snowballs from there.
DM Radio Webinar: Adopting a Streaming-Enabled ArchitectureDATAVERSITY
Architecture matters. That's why today's innovators are taking a hard look at streaming data, an increasingly attractive option that can transform business in several ways: replacing aging data ingestion techniques like ETL; solving long-standing data quality challenges; improving business processes ranging from sales and marketing to logistics and procurement; or any number of activities related to accelerating data warehousing, business intelligence and analytics.
Register for this DM Radio Deep Dive Webinar to learn how streaming data can rejuvenate or supplant traditional data management practices. Host Eric Kavanagh will explain how streaming-first architectures can relieve data engineers from time-consuming, error-prone processes, ideally bidding farewell to those unpleasant batch windows. He'll be joined by Kevin Petrie of Attunity, who will explain why (with real-world story successes) streaming data solutions can keep the business fueled with trusted data in a timely, efficient manner for improved business outcomes.
A Key to Real-time Insights in a Post-COVID World (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2EpHGyd
Presented at Data Champions, Online Asia 2020
Businesses and individuals around the world are experiencing the impact of a global pandemic. With many workers and potential shoppers still sequestered, COVID-19 is proving to have a momentous impact on the global economy. Regardless of the current situation and post-pandemic era, real-time data becomes even more critical to healthcare practitioners, business owners, government officials, and the public at large where holistic and timely information are important to make quick decisions. It enables doctors to make quick decisions about where to focus the care, business owners to alter production schedules to meet the demand, government agencies to contain the epidemic, and the public to be informed about prevention.
In this on-demand session, you will learn about the capabilities of data virtualization as a modern data integration technique and how can organisations:
- Rapidly unify information from disparate data sources to make accurate decisions and analyse data in real-time
- Build a single engine for security that provides audit and control by geographies
- Accelerate delivery of insights from your advanced analytics project
Building a data pipeline to ingest data into Hadoop in minutes using Streamse...Guglielmo Iozzia
Slides from my talk at the Hadoop User Group Ireland meetup on June 13th 2016: building a data pipeline to ingest data from sources of different nature into Hadoop in minutes (and no coding at all) using the Open Source Streamsets Data Collector tool.
Apache Kafka and the Data Mesh | Ben Stopford and Michael Noll, ConfluentHostedbyConfluent
Data mesh is a relatively recent term that describes a set of principles that good modern data systems uphold. A kind of “microservices” for the data-centric world. While the data mesh is not technology-specific as a pattern, the building of systems that adopt and implement data mesh principles have a relatively long history under different guises.
In this talk, we share our recommendations and picks of what every developer should know about building a streaming data mesh with Kafka. We introduce the four principles of the data mesh: domain-driven decentralization, data as a product, self-service data platform, and federated governance. We then cover topics such as the differences between working with event streams versus centralized approaches and highlight the key characteristics that make streams a great fit for implementing a mesh, such as their ability to capture both real-time and historical data. We’ll examine how to onboard data from existing systems into a mesh, modelling the communication within the mesh, how to deal with changes to your domain’s “public” data, give examples of global standards for governance, and discuss the importance of taking a product-centric view on data sources and the data sets they share.
Monitoring and Scaling Redis at DataDog - Ilan Rabinovitch, DataDogRedis Labs
Think you have big data? What about high availability
requirements? At DataDog we process billions of data points every day including metrics and events, as we help the world
monitor the their applications and infrastructure. Being the world’s monitoring system is a big responsibility, and thanks to
Redis we are up to the task. Join us as we discuss how the DataDog team monitors and scales Redis to power our SaaS based monitoring offering. We will discuss our usage and deployment patterns, as well as dive into monitoring best practices for production Redis workloads
Big Data made easy in the era of the Cloud - Demi Ben-AriDemi Ben-Ari
Talking about the ease of use and handling Big Data technologies in the Cloud. Using Google Cloud Platform and Amazon Web Services and all of the tools around it.
Showing the problems and how we can solve them with simple tools.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
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✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-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
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Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
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.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
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GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
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Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
PyConline AU 2021 - Things might go wrong in a data-intensive application
1. Things might go wrong in a
data-intensive application
Petertc Chu | PyConline AU 2021
2. Scope
Applications deal with huge volumes of data
- Web applications, mobile apps, IoT...
Challenges
- “the quantity of data, the complexity of data, the speed
at which it is changing”
Key factors
- Scalability, Reliability
(dataintensive.net)
3. About me
Research engineer and Pythonista from Taiwan
Working on data infrastructures for ten years
kiwislife.com
4. The case
Host and manage UGC (User-generated content) with various usage patterns
- Streaming, IoT data aggregation, file distribution, archiving...
- ~10PiB raw capacity
- Processing several TiBs per day
We can cover a football field if we put all our disks on the ground
5. Structured data store
Sharding / partitioning,
RDMBS clusters,
NoSQL...
Concepts
Cache layer
Unstructured data store
Various kind of DFSs,
heterogeneous storage
media
Application
servers
Job processing
systems,
Other
subsystems
Various usage patterns
7. What happened?
Thousands of IoT devices push data to
our cluster 24-7-365, got
- error rate: ~30%
- Avg RTT: 39.005s
8. The build up
DB race condition
- Optimistic locking doesn’t help in this pattern (W >> R)
databases
IoT
devices
application
servers
contention
occurred! 😱
😡
9. The build up
Pessimistic locking is too expensive for other usage patterns
databases
IoT
devices
application
servers
Implement global
locking
🚘🚘
🚘
🚘🚘
🚘
🚘
🚘
🚘
other users
😡
😡
😡
👍
10. The build up
Final: a hybrid / adaptive approach
- Only do pessimistic locking for specific operations
- Do locking in local by default
- Switch to global locking for specific resource automatically while collision detected
- (switch back after a certain duration)
- Keep using optimistic locking otherwise
11. The build up
Final: a hybrid / adaptive approach
databases
IoT
devices
application
servers
local lock
local lock
local lock
(Global lock)
other users
👍
👍
👍
👍
12. Root cause #scalability
We don’t design for a usage pattern and workload like that
Action taken
- Test concurrency scenarios before each release
- Introduce observability and proactive monitoring systems for quick incident
detection and diagnosis
14. What
happened?
We have an advanced data management feature
- Not production ready, just a prototype
- No one use it for several years
One day, a user discovered it and made a million
times more requests to this subsystem!!
15. The build up
We needed some kind of distributed solution to handle this.
- resque: a Redis-backed framework for creating background jobs
https://github.blog/2009-11-03-introducing-resque/ https://gist.github.com/defunkt/225369
16. Root cause #scalability
Load exceeds expectations
Action taken
- All batch processing subsystems are now implemented in a distributed way
18. What
happened?
A supplier built a data protection subsystem for us
...after we deployed it...
Users complain data corruption!!
19. The build up
Defective padding in the encryption process
Example 1:
Input data: “DD” * 12
Expected result:
| DD DD DD DD DD DD DD DD | DD DD DD DD 04 04 04 04 |
Example 2:
Input data: “DD” * 16
Expected result:
| DD DD DD DD DD DD DD DD | DD DD DD DD DD DD DD DD |
| 16 16 16 16 16 16 16 16 | 16 16 16 16 16 16 16 16 |
Incorrect result:
| DD DD DD DD DD DD DD DD | DD DD DD DD DD DD DD DD |
(If the length of the original data is an integer multiple of the block size B,
then an extra block of bytes with value B is added. B is 16 in this case.)
20. The build up
Design a process to fix all affected data
- List all affected records from DBs
- Read corresponding data with an “incorrect” decryption algorithm
- Write data back with a correct encryption algorithm
Id Size Encryption method Version number Data reference key
1 32 (Not encrypted) 0 aaa
2 6 Indefective algorithm 0 bbb
3 5 (not affected) Defective algorithm 0 ccc
4 32 (affected) Defective algorithm 1 (fixed) ddd
5 64 (affected) Defective algorithm 0 (not yet fixed) eee
Only the last one needs a fix (block size = 16)
21. The build up
Just a silly bug, if it didn’t affect…
- Millions of user records
We set up a job processing system to correct all affected data in our system
gearman [Gearman Job Server] https://github.com/Yelp/python-gearman
22. Root cause #reliability #softwareFaults
1. Unreliable solution provider
2. Less than 1% possibility to find the bug by testing
Action taken
- Not outsourcing anymore
- More comprehensive tests with various kinds of scenarios
- ~10 TiB test dataset
24. What
happened?
To keep reliability, we
- Replicate user data multiple times
- Distribute replicas to different failure domains
(different host/data center)
Data still lost!!
http://dx.doi.org/10.6861/tanet.201810.0398
25. The build up
Our system balances loading by writing data into nodes that have more resource
- A newly added node has more resource in general
- Result in data tend to be placed on new nodes
Data are written to unreliable newly added nodes and lost even though they are
distributed in different failure domains.
Topic: Electronic/Electrical Reliability (cmu.edu)
26. Root cause #reliability #hardwareFaults
It’s hard to prevent data loss completely
- Modeling or simulation cannot truly reflect situations in
real world
Action taken
- Do more stability tests on new coming nodes
- Add a batch of new nodes each time, so it has less
opportunity to write data into an unreliable node
http://dx.doi.org/10.6861/tanet.201810.0398
28. #1 “There is unfortunately no easy fix for
making applications reliable, scalable”
- No way to enumerate all possible reliability causes (hardware faults,
software faults, human errors)
- Usage pattern and load keep changing while your business
expanded, cannot have an ultimate scalability design beforehand
29. #2 Before trying to build a faultless
architecture, think twice
- Consider maintainability
- We need a team to sustain a large-scale system, not just a talented engineer
(dataintensive.net)