ING Meetup - Migrating Spring Boot Config Annotations to Functional with Kotlin
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In the latest years, there has been some push-back against frameworks, and more specifically annotations: some call them magic. Obviously, they make understanding the flow of the application harder. Spring and Spring Boot latest versions go along this trend, by offering an additional way to configure beans with explicit code instead of annotations. It's declarative in the sense it looks like configuration, though it's based on Domain-Specific Language(s). This talk aims to demo a step-by-step process to achieve that.
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4. Deployment
London Java Community - An Experiment in Continuous Deployment of JVM applica...
A couple of years ago, continuous integration in the JVM ecosystem meant Jenkins. Since that time, a lot of other tools have been made available. But new tools don’t mean new features, just new ways. Besides that, what about continuous deployment? There’s no tool that allows deploying new versions of a JVM-based application without downtime. The only way to achieve zero downtime is to have multiple nodes deployed on a platform, and let that platform achieve that e.g. Kubernetes.
And yet, achieving true continuous deployment of bytecode on one single JVM instance is possible if one changes one’s way of looking at things. What if the compilation could be seen as changes? What if those changes could be stored in a data store, and a listener on this data store could stream those changes to the running production JVM via the Attach API?
In this talk, we'll demo exactly that using Hazelcast and Hazelcast Jet - but it’s possible to re-use the principles that will be shown using other streaming technologies.
Zero-downtime deployment on Kubernetes with Hazelcast
Kubernetes allows a lot. After discovering its features, it’s easy to think it can magically transform your application deployment process into a painless no-event. For Hello World applications, that is the case. Unfortunately, not many of us do deploy such applications day-to-day because we need to handle state. Though it would be much easier to have stateless apps, and despite our best efforts in this direction, state is found in (at least) two places: sessions and databases.
You need to think keeping the state while stopping and starting application nodes. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema change with the help of Hazelcast, while keeping the service up during the entire update process.
Orchestrate Your End-to-end Mainframe Application Release Pipeline
What steel and concrete are to a skyscraper, the mainframe is to the global economy. The mainframe is the transactional backbone for 96 of the world’s top 100 banks, 23 of the 25 top US retailers and 9 out of 10 of the world’s largest insurance companies.
When you think of a mainframe, you probably think of an old green computer screen. Did you know you can use the same modern tools and techniques with mainframes that you use with cloud and mobile?
With the growth of mission-critical mainframe workloads showing no signs of slowing down, application delivery cannot remain slow and complex. Organizations must apply the same DevOps processes to the mainframe as they do with other platforms.
Compuware and XebiaLabs enable large enterprises to automatically build, test and deploy mainframe releases within a cross-platform application release pipeline.
JavaDay Istanbul - 3 improvements in your microservices architecture
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I’ll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it. Hint: it’s much easier than one would expect.
XP Days Ukraine - Zero-downtime deployment with Kubernetes, Spring Boot and F...
Kubernetes allows a lot. After discovering its features, it’s easy to think it can magically transform your application deployment process into a painless no-event. For Hello World applications, that is the case. Unfortunately, not many of us do deploy such applications day-to-day.
You need to think about application backward compatibility, possible rollback, database schema migration, etc. I believe the later is one of the biggest pain point. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema migration with the help of Flyway, while keeping the service up during the entire update process.
Bootstrap4XPages
The document provides information about Bootstrap4XPages, a plugin that brings the Bootstrap front-end framework to XPages applications. It discusses what Bootstrap is, the benefits of using it, and how the Bootstrap4XPages plugin works. The plugin provides Bootstrap styling for XPages components out of the box. It can be installed on Domino servers and in Designer. The document demonstrates how to enable Bootstrap styling on an XPages application by selecting the Bootstrap4XPages library and theme. It also covers creating layouts, views, dialogs, and other application elements using Bootstrap styled XPages components. Custom themes and upcoming features are mentioned. Contributions to the open source project are welcomed.
Create a copy of your SharePoint farm with SPDocKit and AutoSPInstaller
SPDocKit helps you generate an XML configuration file that you can use with AutoSPInstaller to create a new SharePoint farm that is the same as or similar to your current farm environment.
The configuration file reduces the time needed to deploy SharePoint, and since everything is reusable, you can create production and staging/testing environments that are identical.
Achieving end-to-end visibility into complex event-sourcing transactions usin...
Event-sourcing systems usage like Kafka is growing rapidly among Node.js applications. Building systems around an event-driven architecture simplifies horizontal scalability in distributed computing models and makes them more resilient to failure. With these advantages, we face new challenges - how to get visibility into these complex processes.
Event-driven architecture is async by nature. Tracking the communication between different components is both extremely difficult and important when debugging or figuring out bottlenecks in the system.
In this talk, I will present ways to achieve end-to-end and granular visibility into complex event-sourcing transactions using distributed tracing. I will use open-source tools like OpenTelemetry, Jaeger, and Zipkin to showcase a complex Node.js system using Kafka.
OpenStack Glance Project Update
Glance is the OpenStack image service that provides services to store, browse, share, distribute, and manage disk images and metadata. It is deployed by 97% of OpenStack clouds. Contributor numbers grew for the Ocata release but declined after budget cuts impacted the core reviewer team. The presentation outlines features added in recent releases and themes for upcoming releases, and asks for help contributing in various ways.
Effective .NET Core Unit Testing with SQLite and Dapper
- The document discusses effective unit testing for .NET Core applications that use SQLite and Dapper for data access.
- It motivates the importance of unit testing, then demonstrates how to set up an in-memory SQLite database to run tests against instead of a live SQL Server database for isolation.
- Best practices like test-driven development, code coverage reports, and writing tests for bug fixes are also covered, with examples shown of integrating the XUnit testing framework.
It’s 2021. Why are we -still- rebooting for patches? A look at Live Patching.
Presented by: Igor Seletskiy
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: IT Teams know the drill. New security bulletins, new issues, new patches to deploy. Schedule another maintenance operation and prepare for system downtime.
There is a better way to do things. Live patching has been around in the Linux Kernel for some time now, but adoption has not been ideal so far - either because of a lack of trust in the technology or just lack of awareness - or sysadmins just enjoy interrupting their workloads or users.
Live patching consists of two aspects. First, there has to be a mechanism for function redirection in the kernel. As in many things, the kernel actually provides three different subset of tools that provide this functionality - kprobes, fprobes and Livepatching. Secondly, Live Patching relies on a set of tools to generate the actual patches to deploy, replacing the old code with new one. This is arguably the most involved part: you need to fit your new code in the proper space, you can’t overwrite other unrelated code and you need to maintain compatibility with other functions. If you change your parameter list, for example, its game over - something will break in the worst possible way.
In this talk we’ll go over issues like Consistency model, patch generation, deployment mechanisms and identify situations that are ideal candidates for live patching instead of traditional patching operations.
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OSCONF Jaipur - A Hitchhiker's Tour to Containerizing a Java application
OSCONF Jaipur - A Hitchhiker's Tour to Containerizing a Java application
GeekOut - Configuration Management with Kubernetes, a Spring-Boot use-case
GeekOut - Configuration Management with Kubernetes, a Spring-Boot use-case
AllTheTalks.online - A Streaming Use-Case: And Experiment in Continuous Deplo...
AllTheTalks.online - A Streaming Use-Case: And Experiment in Continuous Deplo...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
JCon - Zero-Downtime-Deployment with Kubernetes, Spring Boot and Flyway
JCon - Zero-Downtime-Deployment with Kubernetes, Spring Boot and Flyway
VoxxedDays Cluj - Zero-downtime deployment with Kubernetes, Spring Boot and F...
VoxxedDays Cluj - Zero-downtime deployment with Kubernetes, Spring Boot and F...
DevOpsDays Madrid - Zero-downtime deployment with Kubernetes, Spring Boot and...
DevOpsDays Madrid - Zero-downtime deployment with Kubernetes, Spring Boot and...
London Java Community - An Experiment in Continuous Deployment of JVM applica...
London Java Community - An Experiment in Continuous Deployment of JVM applica...
Zero-downtime deployment on Kubernetes with Hazelcast
Zero-downtime deployment on Kubernetes with Hazelcast
Orchestrate Your End-to-end Mainframe Application Release Pipeline
Orchestrate Your End-to-end Mainframe Application Release Pipeline
JavaDay Istanbul - 3 improvements in your microservices architecture
JavaDay Istanbul - 3 improvements in your microservices architecture
XP Days Ukraine - Zero-downtime deployment with Kubernetes, Spring Boot and F...
XP Days Ukraine - Zero-downtime deployment with Kubernetes, Spring Boot and F...
Create a copy of your SharePoint farm with SPDocKit and AutoSPInstaller
Create a copy of your SharePoint farm with SPDocKit and AutoSPInstaller
Achieving end-to-end visibility into complex event-sourcing transactions usin...
Achieving end-to-end visibility into complex event-sourcing transactions usin...
Effective .NET Core Unit Testing with SQLite and Dapper
Effective .NET Core Unit Testing with SQLite and Dapper
It’s 2021. Why are we -still- rebooting for patches? A look at Live Patching.
It’s 2021. Why are we -still- rebooting for patches? A look at Live Patching.
Mehr von Nicolas Fränkel
SnowCamp - Adding search to a legacy application
Most applications evolve to a point where they need to provide search capabilities. But updating an application is always a risk. Plus, sometimes, you don’t have access to the source code. The easiest way to access the data is by getting them directly from the database.
The initial load is the easiest step. However, how do you keep the search index in sync with the database? How do you keep the latency between the search store and the source of truth, so your users don’t have to wait for the next run of the batch to access the newest changes?
In this live coding session, we will show you how you can solve this issue by connecting Elasticsearch to the database with a touch of Hazelcast.
Un CV de dévelopeur toujours a jour
On dit que GitHub est le CV d'un développeur. Un rapide coup d'œil à votre historique de commit et les recruteurs savent tout de vous. Cette approche comporte quelques problèmes. La plupart des entreprises ne publient même pas leur code sous une licence Open Source. Si vous travaillez pour l'une d'entre elles, et si vous n'êtes pas un développeur Open Source les soirs et les week-ends, alors vous n'avez aucune chance.
Récemment, GitHub a permis un certain degré de personnalisation de son profil. Ainsi, même si votre historique de commit a plus de blanc que de vert, vous pouvez fournir un bon point d'entrée pour les employeurs potentiels. Mais ça ne vaut que l'effort que vous y mettez et les données perdent leur valeur rapidement. Pourtant, avec un peu de travail et l'aide d'outils d'automatisation (tels que GitHub Actions), vous pouvez présenter un profil toujours à jour.
jLove - A Change-Data-Capture use-case: designing an evergreen cache
When one’s app is challenged with poor performances, it’s easy to set up a cache in front of one’s SQL database. It doesn’t fix the root cause (e.g. bad schema design, bad SQL query, etc.) but it gets the job done. If the app is the only component that writes to the underlying database, it’s a no-brainer to update the cache accordingly, so the cache is always up-to-date with the data in the database.
Things start to go sour when the app is not the only component writing to the DB. Among other sources of writes, there are batches, other apps (shared databases exist unfortunately), etc. One might think about a couple of ways to keep data in sync i.e. polling the DB every now and then, DB triggers, etc. Unfortunately, they all have issues that make them unreliable and/or fragile.
You might have read about Change-Data-Capture before. It’s been described by Martin Kleppmann as turning the database inside out: it means the DB can send change events (SELECT, DELETE and UPDATE) that one can register to. Just opposite to Event Sourcing that aggregates events to produce state, CDC is about getting events out of states. Once CDC is implemented, one can subscribe to its events and update the cache accordingly. However, CDC is quite in its early stage, and implementations are quite specific.
In this talk, I’ll describe an easy-to-setup architecture that leverages CDC to have an evergreen cache.
BigData conference - Introduction to stream processing
This document discusses stream processing and summarizes a presentation about the topic. It introduces Hazelcast Jet as a stream processing engine and covers open data standards like GTFS. It also describes a demo that uses GTFS data to enrich public transit vehicle position updates in real-time using Hazelcast Jet. The presentation discusses streaming approaches, benefits over batch processing, and provides an overview of stream processing concepts.
ADDO - Your own Kubernetes controller, not only in Go
In Kubernetes, operators allow the API to be extended to your heart content. If one task requires too much YAML, it’s easy to create an operator to take care of the repetitive cruft, and only require a minimum amount of YAML.
On the other hand, since its beginnings, the Go language has been advertised as closer to the hardware, and is now ubiquitous in low-level programming. Kubernetes has been rewritten from Java to Go, and its whole ecosystem revolves around Go. For that reason, It’s only natural that Kubernetes provides a Go-based framework to create your own operator. While it makes sense, it requires organizations willing to go down this road to have Go developers, and/or train their teams in Go. While perfectly acceptable, this is not the only option. In fact, since Kubernetes is based on REST, why settle for Go and not use your own favorite language?
In this talk, I’ll describe what is an operator, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.
TestCon Europe - Mutation Testing to the Rescue of Your Tests
Unit testing ensures your production code is relevant. But what does ensure your testing code is relevant? Come discover mutation testing and make sure your never forget another assert again.
In the realm of testing, the code coverage metrics is the most often talked about. However, it doesn’t mean that the test has been useful or even that an assert has been coded. Mutation testing is a strategy to make sure that the test code is relevant.
In this talk, Nicolas will explain how Code Coverage is computed and what its inherent flaw is. Afterwards, he will describe how Mutation Testing work and how it helps pointing out code that is tested but leave out corner cases. He will also demo PIT, a Java production-grade framework that enables Mutation Testing.
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
CDC is a brand new approach that "turns the database inside out": it allows to get events out of the database state. This can be leveraged to get a cache that is never stale.
OSCONF Hyderabad - Shorten all URLs!
At a point in the past, it was forecast that Java would die, but the JVM platform would be its legacy. And in fact, for a long time, the JVM has been tremendously successful. Wikipedia itself lists a bunch of languages that run on it, some of them close to Java e.g. Kotlin, some of them very remote e.g. Clojure.
But nowadays, the Cloud is becoming ubiquitous. Containerization is the way to go to alleviate some of the vendor lock-in issues. Kubernetes is a de facto platform. If a container needs to be killed for whatever reason (resource consumption, unhealthy, etc.), a new one needs to replace it as fast as possible. In that context, the JVM seems to be a dead-end: its startup time is huge in comparison to a native process. Likewise, it consumes a lot of memory that just increase the monthly bill.
What does that mean for us developers? Has all the time spent in learning the JVM ecosystem been invested with no hope of return over investment? Shall we need to invest even more time in new languages, frameworks, libraries, etc.? That is one possibility for sure. But we can also leverage our existing knowledge, and embrace the Cloud and containers ways with the help of some tools.
In this talk, I’ll create a simple URL shortener with a “standard” stack: Kotlin, JAX-RS and Hazelcast. Then, with the help of Quarkus and GraalVM, I’ll turn this application into a native executable with all Cloud/Container related work has been moved to the build process.
Devclub.lv - Introduction to stream processing
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision-makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing – or data streaming – exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available.
JOnConf - A CDC use-case: designing an Evergreen Cache
This document discusses using change data capture (CDC) and Hazelcast Jet to build an evergreen cache that remains in sync with a database. It covers alternatives to cache invalidation like polling and triggers, introduces CDC and the Debezium implementation, and proposes a Jet job that watches database change events, analyzes them, and updates the cache accordingly to solve the cache freshness problem.
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
When one’s app is challenged with poor performances, it’s easy to set up a cache in front of one’s SQL database. It doesn’t fix the root cause (e.g. bad schema design, bad SQL query, etc.) but it gets the job done. If the app is the only component that writes to the underlying database, it’s a no-brainer to update the cache accordingly, so the cache is always up-to-date with the data in the database.
Things start to go sour when the app is not the only component writing to the DB. Among other sources of writes, there are batches, other apps (shared databases exist, unfortunately), etc. One might think about a couple of ways to keep data in sync i.e. polling the DB every now and then, DB triggers, etc. Unfortunately, they all have issues that make them unreliable and/or fragile.
In this talk, I will describe an easy-to-setup architecture that leverages CDC to have an evergreen cache.
JUG Tirana - Introduction to data streaming
This talk will be about the reasons behind the new stream processing model, how it compare to the old batch model, what are their pros and cons, and a list of existing technologies implementing stream processing with their most prominent characteristics. It will contain details of one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map, beginning with an overview of all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform,showing a working demo implementation of it.
Java.IL - Your own Kubernetes controller, not only in Go!
In Kubernetes, operators allow the API to be extended to your heart content. If one task requires too much YAML, it’s easy to create an operator to take care of the repetitive cruft, and only require a minimum amount of YAML.
On the other hand, since its beginnings, the Go language has been advertised as closer to the hardware, and is now ubiquitous in low-level programming. Kubernetes has been rewritten from Java to Go, and its whole ecosystem revolves around Go. For that reason, It’s only natural that Kubernetes provides a Go-based framework to create your own operator. While it makes sense, it requires organizations willing to go down this road to have Go developers, and/or train their teams in Go. While perfectly acceptable, this is not the only option. In fact, since Kubernetes is based on REST, why settle for Go and not use your own favorite language?
In this talk, I’ll describe what is an operator, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.
vJUG - Introduction to data streaming
The document discusses stream processing and provides an overview of Hazelcast Jet. It begins with explaining why streaming is useful and describes different streaming approaches like event-driven programming. It then provides details on Hazelcast Jet, including its concepts of pipelines and jobs. The document also discusses open data standards like GTFS and demonstrates a sample streaming pipeline that enriches public transportation data from open APIs.
OSCONF - Your own Kubernetes controller: not only in Go
This document discusses creating Kubernetes operators and controllers using different programming languages besides Go. It suggests that a Java-based controller is possible using the GraalVM, which allows creating native executables from Java bytecode. Key points covered include what controllers and operators are, that no specific technology stack is required, and that the JVM could be a good option for controller development with GraalVM's support for polyglot programming and creating native applications.
Tech talks - 3 performance improvements
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I’ll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it.
Hint: it’s much easier than one would expect.
SouJava- 3 easy performance improvements in your microservices architecture
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I'll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it. Hint: it's much easier than one would expect.
JUG SF - Introduction to data streaming
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing - or data streaming - exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available.
In this talk, I’ll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most prominent characteristics. I’ll conclude by describing in detail one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. I’ll go through the all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform, I’ll try to impress attendees with a working demo implementation of it.
SCALE - Stream processing and Open Data, a match made in Heaven
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
Some countries in Europe understand the potential there's in existing data that sits behind closed fences, and passed laws to make this data available to everyone.
On the other hand, the batch processing model gets more and more obsolete: users want the information as soon as possible. While there’s a trade-off between correctness of data, and its speed of delivery, most business decisions do not rely on 100% correct data.
In this talk, I’ll explain how one can leverage the data related to public transportation in Switzerland, to display them in (near) real-time on a map.
ConFoo - 3 performance improvements
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I'll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it.
Mehr von Nicolas Fränkel (20)
jLove - A Change-Data-Capture use-case: designing an evergreen cache
jLove - A Change-Data-Capture use-case: designing an evergreen cache
BigData conference - Introduction to stream processing
BigData conference - Introduction to stream processing
ADDO - Your own Kubernetes controller, not only in Go
ADDO - Your own Kubernetes controller, not only in Go
TestCon Europe - Mutation Testing to the Rescue of Your Tests
TestCon Europe - Mutation Testing to the Rescue of Your Tests
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
JOnConf - A CDC use-case: designing an Evergreen Cache
JOnConf - A CDC use-case: designing an Evergreen Cache
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
Java.IL - Your own Kubernetes controller, not only in Go!
Java.IL - Your own Kubernetes controller, not only in Go!
OSCONF - Your own Kubernetes controller: not only in Go
OSCONF - Your own Kubernetes controller: not only in Go
SouJava- 3 easy performance improvements in your microservices architecture
SouJava- 3 easy performance improvements in your microservices architecture
SCALE - Stream processing and Open Data, a match made in Heaven
SCALE - Stream processing and Open Data, a match made in Heaven
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Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Columbus Data & Analytics Wednesdays - June 2024
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Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
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 automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
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ING Meetup - Migrating Spring Boot Config Annotations to Functional with Kotlin
- 1. @nicolas_frankel Migrating Spring Boot apps from annotation-based config to Functional with Kotlin
- 2. @nicolas_frankel • Developer, team lead, architect, whatever it takes • Recently Developer Advocate • Love Spring • Love Kotlin Me, myself and I
- 3. @nicolas_frankel Hazelcast HAZELCAST IMDG is an operational, in-memory, distributed computing platform that manages data using in-memory storage, and performs parallel execution for breakthrough application speed and scale. HAZELCAST JET is the ultra fast, application embeddable, 3rd generation stream processing engine for low latency batch and stream processing.
- 4. @nicolas_frankel • Open Source • Backed by Pivotal • Built on the shoulders of the Spring framework • Convention over configuration Spring Boot
- 5. @nicolas_frankel • Magic • Via annotations • Solution: • Remove annotations • Functional configuration for the win The issue
- 7. @nicolas_frankel Going further • Spring-Fu • Ko-Fu • Ja-Fu • Experimental!
- 8. @nicolas_frankel Kofu configuration for Spring Boot Kofu configuration is currently developed in Spring Fu, an incubator intended to ultimately contribute features related to Kotlin and functional configuration to Spring Framework, Boot and Data
- 9. @nicolas_frankel Kofu configuration for Spring Boot • Explicit configuration via a Kotlin DSL • Based on Spring Boot infrastructure used in a functional way • No feature enabled based on classpath detection • Both declarative and programmatic • Faster startup and lower memory consumption • Minimal reflection & annotations usage • Pure lambdas, no CGLIB proxy
- 10. @nicolas_frankel
- 11. @nicolas_frankel
- 12. @nicolas_frankel • Controller to routes • Logic in handlers • Kotlin Beans DSL • Kotlin Routes DSL • Reactive is not necessary anymore… till a point Takeaways
- 13. @nicolas_frankel
- 14. @nicolas_frankel • https://blog.frankel.ch/ • @nicolas_frankel • https://git.io/fN3Qv Thanks!