Smart Enterprise Application Integration with Apache Camel

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Some Slides about Apache Camel.

+ Smart Enterprise Application Integration = Kai Wähner, MaibornWolff et al GmbH

Intention of this Workshop Bullet Point Boot Camp Just a few Slides...

Intention of this Workshop ... rather a lot of Live Hacking !!!

What is the Key Message?

Key Message Understand Enteprise Integration Patterns!

Key Message Understand Enteprise Integration Patterns! Understand the Idea behind Apache Camel!

Key Message Understand Enterprise Integration Patterns! Understand the Idea behind Apache Camel! Learn using Apache Camel by Doing!

Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

Enterprise Integration Patterns (EIP)

Enterprise Integration Patterns (EIP)

Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

Apache Camel ImplementstheEIPs

Apache Camel http://java.dzone.com/articles/apache-camel-integration

Use Case for Demo 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

Use Case Toyshop Wholesale Dealer for Toys Partner forVideogames File-Server File-Server FTP Order .csv Order .csv XML over HTTP Trans-former ? If Videogame Queue Else Order Processor

Use Case (EIP-Perspective)

Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

Learning by Doing Enough of theTheory! ,[object Object],[object Object]

Choose your favorite DSL

Choose your required Components HTTP, FTP, File, XSLT, MQ, JDBC, Akka, TCP, SMTP, RSS, Quartz, Log, LDAP, JMS, EJB Many, manymore => Younameit! Besides: AddyourownCustomComponents

Advanced Topics Deployment Scalability Transaction Concurrency Management and Monitoring

And the Winner is… ... the System Integrator!

Key Message arrived?

Key Message Understand Enterprise Integration Patterns! Understand the Idea behind Apache Camel! Learn using Apache Camel by Doing!

Key Message arrived?

Whet your Appetite?

Get Part of the Community!

Thank you for your Attention… Questions? = Smart Enterprise Application Integration + Kai WähnerIT-ConsultantMobile +49 151 544 277 88Email kai.waehner@mwea.de Twitter @KaiWaehnerMaibornWolff et al GmbH | Theresienhöhe 13 | D-80339 München

Smart Enterprise Application Integration with Apache Camel

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Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka. No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck. A detailed article about this topic: https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/

When NOT to use Apache Kafka?

When NOT to use Apache Kafka?

When NOT to use Apache Kafka?

Live commerce combines instant purchasing of a featured product and audience participation. This talk explores the need for real-time data streaming with Apache Kafka between applications to enable live commerce across online stores and brick & mortar stores across regions, countries, and continents in any retail business. The discussion covers several building blocks of a live commerce enterprise architecture, including transactional data processing, omnichannel, natural language processing, augmented reality, edge computing, and more.

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

If there were a buzzword of the hour, it would certainly be "data mesh"! This new architectural paradigm unlocks analytic data at scale and enables rapid access to an ever-growing number of distributed domain datasets for various usage scenarios. As such, the data mesh addresses the most common weaknesses of the traditional centralized data lake or data platform architecture. And the heart of a data mesh infrastructure must be real-time, decoupled, reliable, and scalable. This presentation explores how Apache Kafka, as an open and scalable decentralized real-time platform, can be the basis of a data mesh infrastructure and - complemented by many other data platforms like a data warehouse, data lake, and lakehouse - solve real business problems. There is no silver bullet or single technology/product/cloud service for implementing a data mesh. The key outcome of a data mesh architecture is the ability to build data products; with the right tool for the job. A good data mesh combines data streaming technology like Apache Kafka or Confluent Cloud with cloud-native data warehouse and data lake architectures from Snowflake, Databricks, Google BigQuery, et al.

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

Enterprise integration is more challenging than ever before. The IT evolution requires the integration of more and more technologies. Applications are deployed across the edge, hybrid, and multi-cloud architectures. Traditional middleware such as MQ, ETL, ESB does not scale well enough or only processes data in batch instead of real-time. This presentation explores why Apache Kafka is the new black for integration projects, how Kafka fits into the discussion around cloud-native iPaaS (Integration Platform as a Service) solutions, and why event streaming is a new software category. A concrete real-world example shows the difference between event streaming and traditional integration platforms respectively cloud-native iPaaS. Video Recording of this presentation: https://www.youtube.com/watch?v=I8yZwKg_IJc&t=2842s Blog post about this topic: https://www.kai-waehner.de/blog/2021/11/03/apache-kafka-cloud-native-ipaas-versus-mq-etl-esb-middleware/

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a presentation. The slides cover technologies such as Apache Kafka, Apache Spark, Confluent, Databricks, Snowflake, Elasticsearch, AWS Redshift, GCP with Google Bigquery, and Azure Synapse.

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable. Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden. This session explores different architectures to build serverless Apache Kafka and Apache Spark multi-cloud architectures across regions and continents. We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data Data Lakehouse. Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.

Serverless Kafka and Spark in a Multi-Cloud Lakehouse Architecture

Serverless Kafka and Spark in a Multi-Cloud Lakehouse Architecture

Serverless Kafka and Spark in a Multi-Cloud Lakehouse Architecture

Hybrid cloud architectures are the new black for most companies. A cloud-first strategy is evident for many new enterprise architectures, but some use cases require resiliency across edge sites and multiple cloud regions. Data streaming with the Apache Kafka ecosystem is a perfect technology for building resilient and hybrid real-time applications at any scale. This talk explores different architectures and their trade-offs for transactional and analytical workloads. Real-world examples include financial services, retail, and the automotive industry. Video recording: https://qconlondon.com/london2022/presentation/resilient-real-time-data-streaming-across-the-edge-and-hybrid-cloud

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Agenda: 1) Defence, Modern Warfare, and Cybersecurity in 202X 2) Data in Motion with Apache Kafka as Defence Backbone 3) Situational Awareness 4) Threat Intelligence 5) Forensics and AI / Machine Learning 6) Air-Gapped and Zero Trust Environments 7) SIEM / SOAR Modernization Technologies discussed in the presentation include Apache Kafka, Kafka Streams, kqlDB, Kafka Connect, Elasticsearch, Splunk, IBM QRadar, Zeek, Netflow, PCAP, TensorFlow, AWS, Azure, GCP, Sigma, Confluent Cloud,

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Real-World Deployments of Data Streaming with Apache Kafka across the Healthcare Value Chain using open source and cloud-native technologies and serverless SaaS: 1) Legacy Modernization and Hybrid Cloud: Optum (UnitedHealth Group, Centene, Bayer) 2) Streaming ETL (Bayer, Babylon Health) 3) Real-time Analytics (Cerner, Celmatix, CDC/Centers for Disease Control and Prevention) 4) Machine Learning and Data Science (Recursion, Humana) 5) Open API and Omnichannel (Care.com, Invitae)

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

The Rise of Data in Motion in the Healthcare Industry - Use Cases, Architectures and Examples powered by Apache Kafka. Use Cases for Data in Motion in the Healthcare Industry: - Know Your Patient (= “Customer 360”) - Operations (Healthcare 4.0 including Drug R&D, Patient Care, etc.) - IT Perspective (Cybersecurity, Mainframe Offload, Hybrid Cloud, Streaming ETL, etc) Real-world examples include Covid-19 Electronic Lab Reporting, Cerner, Optum, Centene, Humana, Invitae, Bayer, Celmatix, Care.com.

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Not all workloads allow cloud computing. Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration. This session explores architectures and design patterns for software and hardware considerations to deploy hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell and Confluent Cloud.

Kafka for Real-Time Replication between Edge and Hybrid Cloud

Kafka for Real-Time Replication between Edge and Hybrid Cloud

Kafka for Real-Time Replication between Edge and Hybrid Cloud

The manufacturing industry is moving away from just selling machinery, devices, and other hardware. Software and services increase revenue and margins. Equipment-as-a-Service (EaaS) even outsources the maintenance to the vendor. This paradigm shift is only possible with reliable and scalable real-time data processing leveraging an event streaming platform such as Apache Kafka. This talk explores how Kafka-native Condition Monitoring and Predictive Maintenance help with this innovation. More details: https://www.kai-waehner.de/blog/2021/10/25/apache-kafka-condition-monitoring-predictive-maintenance-industrial-iot-digital-twin/ Video recording: https://youtu.be/tfOuN5KeI9w

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Today, in 2022, Apache Kafka is the central nervous system of many applications in various areas related to the automotive and manufacturing industry for processing analytical and transactional data in motion across edge, hybrid, and multi-cloud deployments. This presentation explores the automotive event streaming landscape, including connected vehicles, smart manufacturing, supply chain optimization, aftersales, mobility services, and innovative new business models. Afterwards, many real-world examples are shown from companies such as Audi, BMW, Porsche, Tesla, Uber, Grab, and FREENOW. More detail in the blog post: https://www.kai-waehner.de/blog/2022/01/12/apache-kafka-landscape-for-automotive-and-manufacturing/

Apache Kafka Landscape for Automotive and Manufacturing

Apache Kafka Landscape for Automotive and Manufacturing

Apache Kafka Landscape for Automotive and Manufacturing

Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers. This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture. The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more. Video recording of this presentation: https://youtu.be/j7D29eyysDw Further reading: https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/ https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/ https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/

Kappa vs Lambda Architectures and Technology Comparison

Kappa vs Lambda Architectures and Technology Comparison

Kappa vs Lambda Architectures and Technology Comparison

I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe: Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter. Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka. Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments. Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka. Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.

The Top 5 Apache Kafka Use Cases and Architectures in 2022

The Top 5 Apache Kafka Use Cases and Architectures in 2022

The Top 5 Apache Kafka Use Cases and Architectures in 2022

Technical thought leadership presentation to discuss how leading organizations move to real-time architecture to support business growth and enhance customer experience. This is a forum to discuss use cases with your peers to understand how other digital-native companies are utilizing data in motion to drive competitive advantage. Agenda: - Data in Motion with Event Streaming and Apache Kafka - Streaming ETL Pipelines - IT Modernisation and Hybrid Multi-Cloud - Customer Experience and Customer 360 - IoT and Big Data Processing - Machine Learning and Analytics

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

The Rise of Data in Motion in the Public Sector powered by event streaming with Apache Kafka. Citizen Services: - Health services, e.g. hospital modernization, track & trace - Covid distance control - Public administration - reduce bureaucracy, data democratization across government departments - eGovernment - Efficient and digital citizen engagement, e.g. personal ID application process Smart City - Smart driving, parking, buildings, environment Waste management - Open exchange – e.g. mobility services (1st and 3rd party) Energy - Smart grid and utilities infrastructure (energy distribution, smart home, smart meters, smart water, etc.) - National Security Law enforcement, surveillance, police/interior security data exchange - Defense and military (border control, intelligent solider) Cybersecurity for situational awareness and threat intelligence

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

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Apache Kafka as Data Hub for Crypto, NFT, Metaverse (Beyond the Buzz!)

Apache Kafka as Data Hub for Crypto, NFT, Metaverse (Beyond the Buzz!)

Apache Kafka as Data Hub for Crypto, NFT, Metaverse (Beyond the Buzz!)

When NOT to use Apache Kafka?

When NOT to use Apache Kafka?

When NOT to use Apache Kafka?

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

Kafka for Live Commerce to Transform the Retail and Shopping Metaverse

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

The Heart of the Data Mesh Beats in Real-Time with Apache Kafka

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

Apache Kafka vs. Cloud-native iPaaS Integration Platform Middleware

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?

Serverless Kafka and Spark in a Multi-Cloud Lakehouse Architecture

Serverless Kafka and Spark in a Multi-Cloud Lakehouse Architecture

Serverless Kafka and Spark in a Multi-Cloud Lakehouse Architecture

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Resilient Real-time Data Streaming across the Edge and Hybrid Cloud with Apac...

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka in the Healthcare Industry

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Apache Kafka for Real-time Supply Chainin the Food and Retail Industry

Kafka for Real-Time Replication between Edge and Hybrid Cloud

Kafka for Real-Time Replication between Edge and Hybrid Cloud

Kafka for Real-Time Replication between Edge and Hybrid Cloud

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0

Apache Kafka Landscape for Automotive and Manufacturing

Apache Kafka Landscape for Automotive and Manufacturing

Apache Kafka Landscape for Automotive and Manufacturing

Kappa vs Lambda Architectures and Technology Comparison

Kappa vs Lambda Architectures and Technology Comparison

Kappa vs Lambda Architectures and Technology Comparison

The Top 5 Apache Kafka Use Cases and Architectures in 2022

The Top 5 Apache Kafka Use Cases and Architectures in 2022

The Top 5 Apache Kafka Use Cases and Architectures in 2022

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

Event Streaming CTO Roundtable for Cloud-native Kafka Architectures

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Apache Kafka in the Public Sector (Government, National Security, Citizen Ser...

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

Telco 4.0 - Payment and FinServ Integration for Data in Motion with 5G and Ap...

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rafiqahmad00786416

In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

Ransomware_Q4_2023. The report. [EN].pdf

Ransomware_Q4_2023. The report. [EN].pdf

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Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Connector Corner: Accelerate revenue generation using UiPath API-centric busi...

Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

DBX First Quarter 2024 Investor Presentation

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Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

Why Teams call analytics are critical to your entire business

Why Teams call analytics are critical to your entire business

💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨‍🏫👨‍💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam

UiPathCommunity

Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

Artificial Intelligence Chap.5 : Uncertainty

Artificial Intelligence Chap.5 : Uncertainty

Artificial Intelligence Chap.5 : Uncertainty

Khushali Kathiriya

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

MINDCTI Revenue Release Quarter One 2024

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1. + Smart Enterprise Application Integration = Kai Wähner, MaibornWolff et al GmbH

2. Intention of this Workshop Bullet Point Boot Camp Just a few Slides...

3. Intention of this Workshop ... rather a lot of Live Hacking !!!

4. What is the Key Message?

5. Key Message Understand Enteprise Integration Patterns!

6. Key Message Understand Enteprise Integration Patterns! Understand the Idea behind Apache Camel!

7. Key Message Understand Enterprise Integration Patterns! Understand the Idea behind Apache Camel! Learn using Apache Camel by Doing!

8. Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

9. Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

10. Enterprise Integration Patterns (EIP)

11. Enterprise Integration Patterns (EIP)

12. Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

13. Apache Camel ImplementstheEIPs

14. Apache Camel http://java.dzone.com/articles/apache-camel-integration

15. Use Case for Demo 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

16. Use Case Toyshop Wholesale Dealer for Toys Partner forVideogames File-Server File-Server FTP Order .csv Order .csv XML over HTTP Trans-former ? If Videogame Queue Else Order Processor

17. Use Case (EIP-Perspective)

18. Agenda 1) What is Enterprise Integration Patterns? 2) What is Apache Camel? 3) Use Case for Demo 4) LIVE HACKING: Apache Camel 5) Outlook

19.

20. Choose your favorite DSL

21. Choose your required Components HTTP, FTP, File, XSLT, MQ, JDBC, Akka, TCP, SMTP, RSS, Quartz, Log, LDAP, JMS, EJB Many, manymore => Younameit! Besides: AddyourownCustomComponents

22. Advanced Topics Deployment Scalability Transaction Concurrency Management and Monitoring

23. And the Winner is… ... the System Integrator!

24. Key Message arrived?

25. Key Message Understand Enterprise Integration Patterns! Understand the Idea behind Apache Camel! Learn using Apache Camel by Doing!

26. Key Message arrived?

27. Whet your Appetite?

28. Get Part of the Community!

29. Thank you for your Attention… Questions? = Smart Enterprise Application Integration + Kai WähnerIT-ConsultantMobile +49 151 544 277 88Email kai.waehner@mwea.de Twitter @KaiWaehnerMaibornWolff et al GmbH | Theresienhöhe 13 | D-80339 München