The good, the bad, the ugly side of step functionsMohsiur Rahman
The following is a talk about when and why you should and shouldn't use step functions with real world examples at two different companies by Mohsiur Rahman at Serverless Meetup in Philadelphia.
Youtube Link : https://youtu.be/XrCOQ24RSTM
Social Media Links
LinkedIn : https://www.linkedin.com/in/mohsiur/
GitHub : https://github.com/mohsiur
Medium : https://medium.com/@mohsiurr
Kotlin is a language from the tool gurus at JetBrains. In 2016, after about six years of development, Kotlin reached version 1.0. In 2017 it won the hearts of developers and became an officially supported language for Android.
Kotlin, like Java, is for more than creating Android applications. It can replace or enhance Java most places it is used today including on AWS. AWS Lambda functions sometimes called Serverless Computing, is a service which lets us developers build web services without worrying about configuring servers.
In this session, we will create a lambda service on AWS using Kotlin. Along the way, we will learn what a makes Kotlin an excellent replacement for Java and how simple it is to construct an AWS Lambda function.
Tom Yitav (Co-Founder & CEO) @ CaStory:
We will talk about the use of GraphQL as an API layer and its deployment as AWS Lambda. We will see a demo of bootsrapping such a service using a CLI tool called create-graphql-app. We will also share some of the main pros and cons compared to non-serverless APIs, and benefits of going Serverless in a startup company.
Managing Serverless Microservices in the Wild
Handing off responsibility for your microservice's infrastructure with AWS API Gateway and Lambda is great, but it also means losing visibility and influence over your app in production.
In this talk, we'll discuss different strategies to gain insights into what actually is going on inside your serverless app. We will present a tool we've built at atomData to get high quality, actionable logs and metrics about our services.
Over 9 weeks of my internship, I learned Scala and worked on migrating a back-end service from Ruby to Scala. Over the course of the experience I discovered some reasons that Ruby works well for prototyping, while Scala works well for production.
The good, the bad, the ugly side of step functionsMohsiur Rahman
The following is a talk about when and why you should and shouldn't use step functions with real world examples at two different companies by Mohsiur Rahman at Serverless Meetup in Philadelphia.
Youtube Link : https://youtu.be/XrCOQ24RSTM
Social Media Links
LinkedIn : https://www.linkedin.com/in/mohsiur/
GitHub : https://github.com/mohsiur
Medium : https://medium.com/@mohsiurr
Kotlin is a language from the tool gurus at JetBrains. In 2016, after about six years of development, Kotlin reached version 1.0. In 2017 it won the hearts of developers and became an officially supported language for Android.
Kotlin, like Java, is for more than creating Android applications. It can replace or enhance Java most places it is used today including on AWS. AWS Lambda functions sometimes called Serverless Computing, is a service which lets us developers build web services without worrying about configuring servers.
In this session, we will create a lambda service on AWS using Kotlin. Along the way, we will learn what a makes Kotlin an excellent replacement for Java and how simple it is to construct an AWS Lambda function.
Tom Yitav (Co-Founder & CEO) @ CaStory:
We will talk about the use of GraphQL as an API layer and its deployment as AWS Lambda. We will see a demo of bootsrapping such a service using a CLI tool called create-graphql-app. We will also share some of the main pros and cons compared to non-serverless APIs, and benefits of going Serverless in a startup company.
Managing Serverless Microservices in the Wild
Handing off responsibility for your microservice's infrastructure with AWS API Gateway and Lambda is great, but it also means losing visibility and influence over your app in production.
In this talk, we'll discuss different strategies to gain insights into what actually is going on inside your serverless app. We will present a tool we've built at atomData to get high quality, actionable logs and metrics about our services.
Over 9 weeks of my internship, I learned Scala and worked on migrating a back-end service from Ruby to Scala. Over the course of the experience I discovered some reasons that Ruby works well for prototyping, while Scala works well for production.
What's new with .NET Core 3 - covering features from C#, .NET Core, ASP.NET Core, WPF - including nullability, indices and ranges, switch expressions, enhanced pattern matching, changes with ASP.NET Core, Blazor server-side components, and WPF with .NET Core.
Developing and Deploying Deep Learning Based Computer Vision Systems - Alka N...CodeOps Technologies LLP
Deep Learning is enabling a wide range of computer vision applications from advanced driver assistance systems to sophisticated medical diagnostic devices. However, designing and deploying these applications involve a lot of challenges like handling large datasets, developing optimized models, effectively performing GPU computing and efficiently deploying deep learning models to embedded boards like NVIDIA Jetson. This session illustrates how MATLAB supports all phases of this workflow starting with algorithm design to automatically generating portable and optimized CUDA code helping engineers and scientists address the commonly observed challenges in deep learning workflow
Fore features of .NET Core: dependency injection, logging, and configuration, and using the .NET Core 3.0 Host class.
Only few slides but live coding with many samples available at: https://github.com/christiannagel/bastafrankfurt2020
Special features of Entity Framework Core like logging, query tags, DbContext pools, shadow properties, model-level query filters, mapping to fields, table splitting, and more - including Cosmos DB provider with EF Core 3.0
Flink Forward Berlin 2018: Ravi Suhag & Sumanth Nakshatrithaya - "Managing Fl...Flink Forward
At GO-JEK, we build products that help millions of Indonesians At GO-JEK, we build products that help millions of Indonesians commute, shop, eat and pay, daily. Data Engineering team is responsible to create a reliable data infrastructure across all of GO-JEK’s 18+ products. We use Flink extensively to provide real-time streaming aggregation and analytics for billions of data points generated on daily basis. Working at such a large scale makes it really important to automate operations from infrastructure, failover, and monitoring. This way we can push features faster without causing chaos and disruption to the production environment. 1. Provisioning and deployment: With the nature of business at GoJek, we found ourselves provisioning Flink clusters quite often. Currently we run around 1000 jobs across 10 clusters for different data streams with increasing number of requests day by day. We also provision on the fly clusters with custom configuration for load testing, experimentation and chaos engineering. Provisioning these many clusters from ground up required lot of man hours and involved setting up virtual machines, monitoring agents, access management, configuration management, load testing and data stream integration. Our current setup has Flink over Yarn clusters as well as Kubernetes. We use our in-house provisioning tool Odin, built on top of Terraform and Chef for Yarn clusters and Kubernetes controllers for Kubernetes based deployments. It enables us to safely and predictably create and modify Flink infrastructure. Odin has helped us reduce provisioning time by 99% despite increasing number of requests. 2. Isolation and access control: Given the real-time and distributed nature of GoJek's services, events are classified into different streams depending on nature, time and transactional criticality, sensitivity and volume of data. Which requires setting up separate clusters based on security concerns, team segregation, job loads and criticality which comes at the cost of handling large volume data replication and maintenance. 3. Data quality control: The quality of ingestion events are controlled by Protobuf based version controlled strict event type schema with fully automated deployment pipeline. Deployed jobs are locked to a certain data schema and version which helps us accidental breaking schema changes and backward compatibility during migration and failover. 4. Monitoring and alerting: All the clusters are monitored using dedicated TICK setup. We monitors clusters for resource utilization, job stats and business impact per job. 5. Failover and Upgrading: Failover and upgrade operations are fully automated for yarn cluster failover, input stream failovers e.g. Kafka failover with stateless job strategies. Which helps us moving jobs from one cluster to another without any data loss or broken metric flow. 6. Chaos engineering and load testing: Loki is our disaster simulation tool that helps ensure the Flink infrastructure can tolerat
The basics of Reactive Cocoa. The tips and tricks in this presentation will cover almost all the use cases for Reactive Cocoa. Demo here: https://github.com/rob-brown/Demos/tree/master/RACDemo.
Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...Flink Forward
Distributed tracing is used to analyze performance and error cases in service oriented architectures. The Observability team at Airbnb recently created Upshot, a data pipeline that uses Flink to analyze over 40 million trace events per minute. Summaries of the resulting data are sent to Druid, Datadog, and other downstream datastores. This talk will focus on how we use Flink and how we analyzed and addressed scaling issues we encountered while building Upshot.
6 years ago, I wrote my first "serverless" application: Fewbytes' clients timesheets interface. This was an AppEngine application, and its architecture was similar to other server applications - clients communicating with a central piece of code which talks on their behalf to other components. But today, with the growing power and sophistication of Cloud platforms, our familiar architectural patterns fail us. To fully unleash the power of the Cloud, a new type of architectural pattern is required, one in which there is no central server and many peers cooperate. We need a truly Cloud Native architecture: Total Cloud Immersion
This talk will present R as a programming language suited for solving data analysis and modeling problems, MLflow as an open source project to help organizations manage their machine learning lifecycle and the intersection of both by adding support for R in MLflow. It will be highly interactive and touch on some of the technical implementation choices taken while making R available in MLflow. It will also demonstrate using MLflow tracking, projects, and models directly from R as well as reusing R models in MLflow to interoperate with other programming languages and technologies.
What's new with .NET Core 3 - covering features from C#, .NET Core, ASP.NET Core, WPF - including nullability, indices and ranges, switch expressions, enhanced pattern matching, changes with ASP.NET Core, Blazor server-side components, and WPF with .NET Core.
Developing and Deploying Deep Learning Based Computer Vision Systems - Alka N...CodeOps Technologies LLP
Deep Learning is enabling a wide range of computer vision applications from advanced driver assistance systems to sophisticated medical diagnostic devices. However, designing and deploying these applications involve a lot of challenges like handling large datasets, developing optimized models, effectively performing GPU computing and efficiently deploying deep learning models to embedded boards like NVIDIA Jetson. This session illustrates how MATLAB supports all phases of this workflow starting with algorithm design to automatically generating portable and optimized CUDA code helping engineers and scientists address the commonly observed challenges in deep learning workflow
Fore features of .NET Core: dependency injection, logging, and configuration, and using the .NET Core 3.0 Host class.
Only few slides but live coding with many samples available at: https://github.com/christiannagel/bastafrankfurt2020
Special features of Entity Framework Core like logging, query tags, DbContext pools, shadow properties, model-level query filters, mapping to fields, table splitting, and more - including Cosmos DB provider with EF Core 3.0
Flink Forward Berlin 2018: Ravi Suhag & Sumanth Nakshatrithaya - "Managing Fl...Flink Forward
At GO-JEK, we build products that help millions of Indonesians At GO-JEK, we build products that help millions of Indonesians commute, shop, eat and pay, daily. Data Engineering team is responsible to create a reliable data infrastructure across all of GO-JEK’s 18+ products. We use Flink extensively to provide real-time streaming aggregation and analytics for billions of data points generated on daily basis. Working at such a large scale makes it really important to automate operations from infrastructure, failover, and monitoring. This way we can push features faster without causing chaos and disruption to the production environment. 1. Provisioning and deployment: With the nature of business at GoJek, we found ourselves provisioning Flink clusters quite often. Currently we run around 1000 jobs across 10 clusters for different data streams with increasing number of requests day by day. We also provision on the fly clusters with custom configuration for load testing, experimentation and chaos engineering. Provisioning these many clusters from ground up required lot of man hours and involved setting up virtual machines, monitoring agents, access management, configuration management, load testing and data stream integration. Our current setup has Flink over Yarn clusters as well as Kubernetes. We use our in-house provisioning tool Odin, built on top of Terraform and Chef for Yarn clusters and Kubernetes controllers for Kubernetes based deployments. It enables us to safely and predictably create and modify Flink infrastructure. Odin has helped us reduce provisioning time by 99% despite increasing number of requests. 2. Isolation and access control: Given the real-time and distributed nature of GoJek's services, events are classified into different streams depending on nature, time and transactional criticality, sensitivity and volume of data. Which requires setting up separate clusters based on security concerns, team segregation, job loads and criticality which comes at the cost of handling large volume data replication and maintenance. 3. Data quality control: The quality of ingestion events are controlled by Protobuf based version controlled strict event type schema with fully automated deployment pipeline. Deployed jobs are locked to a certain data schema and version which helps us accidental breaking schema changes and backward compatibility during migration and failover. 4. Monitoring and alerting: All the clusters are monitored using dedicated TICK setup. We monitors clusters for resource utilization, job stats and business impact per job. 5. Failover and Upgrading: Failover and upgrade operations are fully automated for yarn cluster failover, input stream failovers e.g. Kafka failover with stateless job strategies. Which helps us moving jobs from one cluster to another without any data loss or broken metric flow. 6. Chaos engineering and load testing: Loki is our disaster simulation tool that helps ensure the Flink infrastructure can tolerat
The basics of Reactive Cocoa. The tips and tricks in this presentation will cover almost all the use cases for Reactive Cocoa. Demo here: https://github.com/rob-brown/Demos/tree/master/RACDemo.
Flink Forward Berlin 2018: Brian Wolfe - "Upshot: distributed tracing using F...Flink Forward
Distributed tracing is used to analyze performance and error cases in service oriented architectures. The Observability team at Airbnb recently created Upshot, a data pipeline that uses Flink to analyze over 40 million trace events per minute. Summaries of the resulting data are sent to Druid, Datadog, and other downstream datastores. This talk will focus on how we use Flink and how we analyzed and addressed scaling issues we encountered while building Upshot.
6 years ago, I wrote my first "serverless" application: Fewbytes' clients timesheets interface. This was an AppEngine application, and its architecture was similar to other server applications - clients communicating with a central piece of code which talks on their behalf to other components. But today, with the growing power and sophistication of Cloud platforms, our familiar architectural patterns fail us. To fully unleash the power of the Cloud, a new type of architectural pattern is required, one in which there is no central server and many peers cooperate. We need a truly Cloud Native architecture: Total Cloud Immersion
This talk will present R as a programming language suited for solving data analysis and modeling problems, MLflow as an open source project to help organizations manage their machine learning lifecycle and the intersection of both by adding support for R in MLflow. It will be highly interactive and touch on some of the technical implementation choices taken while making R available in MLflow. It will also demonstrate using MLflow tracking, projects, and models directly from R as well as reusing R models in MLflow to interoperate with other programming languages and technologies.
Serverless is new trend in software development. It’s confusing many developers around the world. In this talk I’ll explain how to build not only crop images or select data from DynamoDB, but build real application, what kind of troubles are we should expect, how to make decision is your task fit into serverless architecture in Python or may be you should use, general approach. How fast serverless applications and more important how to scale it.
Streaming Trend Discovery: Real-Time Discovery in a Sea of Events with Scott ...Databricks
Time is the one thing we can never get in front of. It is rooted in everything, and “timeliness” is now more important than ever especially as we see businesses automate more and more of their processes. This presentation will scratch the surface of streaming discovery with a deeper dive into the telecommunications space where it is normal to receive billions of events a day from globally distributed sub-systems and where key decisions “must” be automated.
We’ll start out with a quick primer on telecommunications, an overview of the key components of our architecture, and make a case for the importance of “ringing”. We will then walk through a simplified solution for doing windowed histogram analysis and labeling of data in flight using Spark Structured Streaming and mapGroupsWithState. I will walk through some suggestions for scaling up to billions of events, managing memory when using the spark StateStore as well as how to avoid pitfalls with the serialized data stored there.
What you’ll learn:
1. How to use the new features of Spark 2.2.0 (mapGroupsWithState / StateStore)
2. How to bucket and analyze data in the streaming world
3. How to avoid common Serialization mistakes (eg. how to upgrade application code and retain stored state)
4. More about the telecommunications space than you’ll probably want to know!
5. Learn a new approach to building applications for enterprise and production.
Assumptions:
1. You know Scala – or want to know more about it.
2. You have deployed spark to production at your company or want to
3. You want to learn some neat tricks that may save you tons of time!
Take Aways:
1. Fully functioning spark app – with unit tests!
Kyo is a next-generation effect system that introduces an approach based on algebraic effects to deliver straightforward APIs in the pure Functional Programming paradigm. Unlike similar solutions, Kyo achieves this without inundating developers with esoteric concepts from Category Theory or using cryptic symbolic operators. This results in a development experience that is both intuitive and robust.
Kyo generalizes the innovative effect rotation mechanism introduced by ZIO. While ZIO restricts effects to two channels, namely dependency injection and short-circuiting, Kyo allows for an arbitrary number of effectful channels. This enhancement offers developers greater flexibility in effect management and simplifies Kyo's internal codebase through more principled design patterns.
In addition to this novel approach to effect handling, Kyo provides seamless direct syntax inspired by Monadless and a comprehensive set of built-in effects like Aborts for short-circuiting, Envs for dependency injection, and Fibers for green threads with fine-grained uncooperative preemption.
After over two years in development, the first public release of the project will be made during Functional Scala 2023. Attendees will also be treated to benchmark results that showcase Kyo's unparalleled performance.
Hierarchical free monads and software design in fpAlexander Granin
I invented the approach I call "Hierarchical Free Monads". It helps to build applications in Haskell with achieving all the needed code quality requirements. I tested this approach in several real world projects and companies, and it works very well.
Stream Puzzlers – Traps and Pitfalls in Using Java 8 Streams langer4711
How well do you know the Stream-API in Java 8? Do you like brainteasers? Then you are invited to take a look at some short programs involving stream operations whose behavior isn’t obvious at first sight. Can you figure out what it does? Using these puzzlers we will take a closer look at some stream operations’ behavior and will show you how to avoid common traps and pitfalls.
It is not uncommon for Notes client developers to feel intimidated by the wide range of technologies available when modernizing an existing portfolio of applications with XPages. In this 2-hour workshop we will provide a series of 20-minute introductions to many of these new and emerging technologies. Learn about Java, Beans, REST Services, Bootstrap, Mobile Controls, data visualization and a whole lot more.
Serverless applications in Python sounds, strange isn’t? In this talk I’ll explain how to build not only crop images or select data from DynamoDB, but build real application, what kind of troubles are we should expect, how to make decision is your task fit into serverless architecture in Python or may be you should use, general approach. How fast serverless applications written in Python, and more important how to scale it.
This presentation shares the journey of voxgig, a Software-as-a-Service platform for conference exhibitors, and its strategic decision to adopt microservices from the outset. The narrative unfolds with an exploration of how a small, remote team can build a significant platform, detailing a development timeline that spans from initial exploration to platform launch, all structured around microservices.
The architecture of voxgig is dissected to reveal two pivotal tactics: Transport Independence and Pattern Matching, which collectively enable emergent design within the microservices ecosystem. These concepts are expanded to illustrate their role in facilitating service distribution, extension, specialization, and composition, laying the groundwork for a robust microservice architecture.
Despite aspiring to an ideal microservice structure characterized by clean separation between client, application, and core data layers, practical challenges lead to a more organic coding approach, often necessitating later technical debt repayment. The evolution of voxgig's architecture culminates in a diverse ecosystem of 65 Node.js services, enriched by a detailed statistical analysis highlighting service size, age, quality, and the overarching message patterns that constitute the system's backbone.
Key lessons emerge from voxgig's experience, underscoring the complexity of UX design, the importance of adhering to one's methodologies, and the value of investing time in a robust core system. Insights into operational efficiencies, such as service bundling for cost savings and the indispensability of a message REPL, are shared. The presentation concludes by emphasizing the critical role of components within the microservices framework, advocating for system-wide operability in a single process locally and the pragmatic use of synchronous messages for the majority of use cases, all while championing the concept of microservices as fundamental components of a scalable, agile architecture.
NYC Titanium User's Group - tiConf US RevisitedJohn Oliva
John Oliva provides a summary of topics and announcements from tiConf US held in Baltimore in June 2013. Some of the topics include: Titanium product direction, Ti.Next, URL Schemes, Alloy, better Javascript, Plantino for Titanium mobile gaming, Internet of Things.
A Learning to Rank Project on a Daily Song Ranking ProblemSease
Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situation; understanding how to adapt a specific dataset and to design the best approach to solve a ranking problem in a real-world scenario is thus crucial.This talk aims to illustrate how to set up and build a Learning to Rank (LTR) project starting from the available data, in our case a Spotify Dataset (available on Kaggle) on the Worldwide Daily Song Ranking, and ending with the implementation of a ranking model. A step by step (phased) approach to cope with this task using open source libraries will be presented.We will examine in depth the most important part of the pipeline that is the data preprocessing and in particular how to model and manipulate the features in order to create the proper input dataset, tailored to the machine learning algorithm requirements.
Alessandro Confetti - Oop vs functional: stop the fight and start building me...Codemotion
Let's get back to the time when languages could be defined with just 6 reserved keywords or none at all, and learn how the ideas that John McCarthy and Alan Key envisioned in Lisp and Smalltalk are still alive and kicking in our serverless and message-driven world. No matter the language you are using now, and how much you are an OOP or functional guy, you'll learn how to develop better serverless applications and build extensible and decoupled workflows.
Alessandro Confetti - Oop vs functional: stop the fight and start building me...Codemotion
Let's get back to the time when languages could be defined with just 6 reserved keywords or none at all, and learn how the ideas that John McCarthy and Alan Key envisioned in Lisp and Smalltalk are still alive and kicking in our serverless and message-driven world. No matter the language you are using now, and how much you are an OOP or functional guy, you'll learn how to develop better serverless applications and build extensible and decoupled workflows.
Oop vs functional stop the fight and start building message driven serverle...Alessandro Confetti
Let's get back to the time when languages could be defined with just 6 reserved keywords or none at all, and learn how the ideas that John McCarthy and Alan Key envisioned in Lisp and Smalltalk are still alive and kicking in our serverless and message-driven world. No matter the language you are using now, and how much you are an OOP or functional guy, you'll learn how to develop better serverless applications and build extensible and decoupled workflows.
Modern Release Engineering in a Nutshell - Why Researchers should Care!Bram Adams
Invited talk at the Leaders of Tomorrow Symposium of the 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016).
The presentation (and its accompanying paper, see http://mcis.polymtl.ca/publications/2016/fose.pdf) explain the basics of release engineering pipelines, common challenges industry is facing as well as pitfalls software engineering researchers are falling into.
Speakers are Bram Adams (MCIS, http://mcis.polymtl.ca) and Shane McIntosh (McGill University, http://shanemcintosh.org).
A video-taped version of the talk will be available soon at https://www.youtube.com/channel/UCL8yG6qpHk7V66l1Jt3aZrA/featured.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
2. Developer Meetup #33
l (Kotaro WATANABE)
l (Development Support)
l Infra Engineer (2016/04~)
l
l LINE
l LINE LIVE
l LINE
l LINE
l LINE
l Clova
l Etc.
3. Developer Meetup #33
l
l Elasticsearch
l Prometheus
l HBase
l MySQL
l Redis
l Memcached
l Kafka
l Hadoop
l Rabbitmq
l Tomcat
l Nginx
l Etc.
LINE
4. Developer Meetup #33
l
l Elasticsearch
l Prometheus
l HBase
l MySQL
l Redis
l Memcached
l Kafka
l Hadoop
l Rabbitmq
l Tomcat
l Nginx
l Etc.
LINE
17. Developer Meetup #33
l
l Master node
l
l VM 3
l discovery.zen.minimum_master_nodes: 2
l Primary shard Replica shard
l Primary shard: 5 (Data node )
l Replica shard: 1
l 1node 2 shard
19. Developer Meetup #33
l 20
l Master node
l Master node Data node
l discovery.zen.minimum_master_nodes
l (master_eligible_nodes / 2) + 1 = 2
l batch indexing rebalance allocation
l
l Shard Allocation Settings
l 5Mbps 20Mbps
20. Developer Meetup #33
2. Admin Elasticsearch
l Admin Elasticsearch
l Data node
l Forced Awareness
l /path/to/elasticsearch.yml
l e.g. /etc/elasticsearch/elasticsearch.yml
# for service zone
node.attr.zone: service
cluster.routing.allocation.awareness.force.zone.values: service,admin
cluster.routing.allocation.awareness.attributes: zone
# for admin zone
node.attr.zone: admin
cluster.routing.allocation.awareness.force.zone.values: service,admin
cluster.routing.allocation.awareness.attributes: zone
22. Developer Meetup #33
zone
l zone index
l zone shard replica
l Primary shard Replica shard
l shard rebalance
l zone index
l zone Elasticsearch zone
l admin
24. Developer Meetup #33
l
l Thread Pool
l int((available processors * 3) / 2) + 1
l available processors 32
l (32 * 3 / 2) + 1 = 49 ( Thread Pool )
l processors (PM : 40)
l (40 * 3) / 2 + 1 = 61
l Queue (1000 → 3000)
l thread_pool.search.queue_size: 3000
l ES5 ES6
31. Developer Meetup #33
l Prometheus (Alerting) + Grafana (Monitoring) ← NEW
l line/promgen
l LINE Prometheus
l
l
l
l IMON (Monitoring + Alerting)
l Java
l PMC
33. Developer Meetup #33
Prometheus + Grafana + Promgen
l Prometheus
l Pull
l agent
l *_exporter
l http
l EXPORTERS AND INTEGRATIONS
l Grafana
l Prometheus DataSource
l exporter Overview Dashboard
l Promgen
l line/promgen
l LINE Prometheus
34. Developer Meetup #33
Elasticsearch Prometheus
l elasticsearch_exporter
l justwatchcom/elasticsearch_exporter
l GET /_nodes/stats Prometheus
exporter
l Promgen
Prometheus
l Alertmanager
push post
l
l e.g. elaseticsearch_cluster_health_status{color=“red”} == 1
37. Developer Meetup #33
l Prometheus + Grafana
l
l line/promgen
l LINE Engineering Blog
l PromCon 2017: Prometheus as an (internal) Service
l Prometheus Casual Talks
39. Developer Meetup #33
l Client
l Transport Client High Revel REST Client
l Elasticsearch
l 5 6
l
40. Developer Meetup #33
l Elasticsearch5.x 6.x
l Java Client (TransportClient → High Level Rest Client)
41. Developer Meetup #33
5.X 6.X
l Elasticsearch
l Upgrade Elasticsearch
l 6.x Rolling Upgrade 5.6
l 5.x 5.6 Rolling Upgrade
l
l 5.x -> 5.6 -> 6.x
l Upgrade
l …
l 1index 1type
l _all
l Etc.
l Kibana Upgrade-Assistant
42. Developer Meetup #33
Java Client
l Transport Client …
l Deprecate Elasticsearch7.0
l Remove: Elasticsearch8.0
l Client
l High Level Rest Client
l Migration Guide
l
l Transport Client ES
l
l 5.6.x 6.x