Learning How to Build Event Streaming Applications with Pac-Man

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Learning How to Build Event Streaming Applications with Pac-Man, Ricardo Ferreira, Developer Advocate, Confluent https://www.meetup.com/Raleigh-Apache-Kafka-Meetup-by-Confluent/events/269215507/

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About me
@riferrei | #kafkameetup | @CONFLUENTINC
• RICARDO FERREIRA
• Works for confluent
• Developer advocate
• Ricardo@confluent.iO
• HTTPS://RIFERREI.NET

Building event
streaming apps
with pac-man
@riferrei | #kafkameetup | @CONFLUENTINC

@riferrei | #kafkameetup | @CONFLUENTINC
Current state
SQL
SQL
SQL
application
application
applicationdatabase
LOG

@riferrei | #kafkameetup | @CONFLUENTINC
Why?

@riferrei | #kafkameetup | @CONFLUENTINC
I don’t know
“my app only understands sql”
“the source only understands sql”
“I can only process atomic data”
“that is what current books says”
“it just feels right doing like this”

@riferrei | #kafkameetup | @CONFLUENTINC
But what if…
database
1000x more volume
Non-transactional events
Transactional events
LOG

@riferrei | #kafkameetup | @CONFLUENTINC
This is fine

@riferrei | #kafkameetup | @CONFLUENTINC
Solving the
problem

@riferrei | #kafkameetup | @CONFLUENTINC
Using the log
database
LOG
0 1 2 3 4 5 6 7 8LOG
reads
New records
application a
(time = 1)
application b
(time = 3)

@riferrei | #kafkameetup | @CONFLUENTINC
But log programming hurts
public void readTransactions() {
while (true) {
ConsumerRecords<String, Type> records =
consumer.poll(Duration.ofMillis(100));
for (Record<String, Type> record : records) {
if (record.offset() != lastSavedOffset) {...}
}
}
}

@riferrei | #kafkameetup | @CONFLUENTINC
What then?

@riferrei | #kafkameetup | @CONFLUENTINC
Event
streaming
appS

@riferrei | #kafkameetup | @CONFLUENTINC
PAC-MAN

@riferrei | #kafkameetup | @CONFLUENTINC
https://github.com/confluentinc/demo-scene
<<Pacman-ccloud>>
Getting started

@riferrei | #kafkameetup | @CONFLUENTINC
IMPLEMENTATION
Api
gateway
Lambda
function
Kafka
topic
Ksqldb
apps
Kafka
topic
scoreboard

@riferrei | #kafkameetup | @CONFLUENTINC
IMPLEMENTATION
Kafka
topic
Ksqldb
apps
Kafka
topic

@riferrei | #kafkameetup | @CONFLUENTINC
Let’s create
the streams

@riferrei | #kafkameetup | @CONFLUENTINC
instructions
1. Get the game 2. Name yourself

@riferrei | #kafkameetup | @CONFLUENTINC
BUILDING the
scoreboard

@riferrei | #kafkameetup | @CONFLUENTINC
Introduction to ksqldb
https://ksqldb.io

@riferrei | #kafkameetup | @CONFLUENTINC
Source: USER_GAME TOPIC

@riferrei | #kafkameetup | @CONFLUENTINC
Creating User_game stream

@riferrei | #kafkameetup | @CONFLUENTINC
Querying USER_GAME STREAM

@riferrei | #kafkameetup | @CONFLUENTINC
Creating Stats_per_user table

@riferrei | #kafkameetup | @CONFLUENTINC
Querying STATS_PER_USER TABLE

@riferrei | #kafkameetup | @CONFLUENTINC
Low latency Pull queries

@riferrei | #kafkameetup | @CONFLUENTINC
Source: User_losses topic

@riferrei | #kafkameetup | @CONFLUENTINC
Creating USER_LOSSES STREAM

@riferrei | #kafkameetup | @CONFLUENTINC
querying USER_LOSSES STREAM

@riferrei | #kafkameetup | @CONFLUENTINC
Creating LOSSES_PER_USER TABLE

@riferrei | #kafkameetup | @CONFLUENTINC
Querying LOSSES_PER_USER TABLE

@riferrei | #kafkameetup | @CONFLUENTINC
Creating SCOREBOARD TABLE

@riferrei | #kafkameetup | @CONFLUENTINC
Querying SCOREBOARD TABLE

@riferrei | #kafkameetup | @CONFLUENTINC
Playing with
The data

@riferrei | #kafkameetup | @CONFLUENTINC
Read from kafka
Api
gateway
Lambda
function
scoreboardRedis
cache
push

@riferrei | #kafkameetup | @CONFLUENTINC
Read from kafka
Amazon
alexa
Lambda
function
scoreboardRedis
cache
push

@riferrei | #kafkameetup | @CONFLUENTINC
Read from ksqldb
Your
code
Ksqldb
table
pull
Kafka
topic

@riferrei | #kafkameetup | @CONFLUENTINC
Creating highest_score TABLE

@riferrei | #kafkameetup | @CONFLUENTINC
Low latency Pull queries
69920

@riferrei | #kafkameetup | @CONFLUENTINC
how can I
learn more?

@riferrei | #kafkameetup | @CONFLUENTINC
Get kafka: confluent cloud
Try free:
https://cnfl.io/confluent-cloud

@riferrei | #kafkameetup | @CONFLUENTINC
https://cnfl.io/tutorials
Get examples: kafka tutorials

@riferrei | #kafkameetup | @CONFLUENTINC
https://cnfl.io/books
Get books: o’reilly bundle

@riferrei | #kafkameetup | @CONFLUENTINC
https://kafka-summit.org/events/kafka-summit-austin-2020
join kafka summit
https://myeventi.events/kafka20/aus
Use 25% discount code: KSa20Meetup

@riferrei | #kafkameetup | @CONFLUENTINC
Thank you

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Citi Tech Talk: Data Governance for streaming and real time data

Confluent & GSI Webinars series: Session 2

Data In Motion Paris 2023

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June Patch Tuesday

Ivanti

Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.

Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...

Pitangent Analytics & Technology Solutions Pvt. Ltd

Generating privacy-protected synthetic data using Secludy and Milvus

Zilliz

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.

Energy Efficient Video Encoding for Cloud and Edge Computing Instances

Alpen-Adria-Universität

Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe

Precisely

Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market. Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.

Programming Foundation Models with DSPy - Meetup Slides

Zilliz

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c5vrf27qcz

Leveraging the Graph for Clinical Trials and Standards

Neo4j

The Microsoft 365 Migration Tutorial For Beginner.pptx

operationspcvita

“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...

Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/ Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit. As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies. In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

DianaGray10

Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more. The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications. We’ll discuss and demo the benefits of UiPath Apps and connectors including: Creating a compelling user experience for any software, without the limitations of APIs. Accelerating the app creation process, saving time and effort Enjoying high-performance CRUD (create, read, update, delete) operations, for seamless data management. Speakers: Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP Charlie Greenberg, host

GNSS spoofing via SDR (Criptored Talks 2024)

Javier Junquera

In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security. This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing. The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.

Northern Engraving | Nameplate Manufacturing Process - 2024

Northern Engraving

Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!

GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph

Neo4j

Monitoring and Managing Anomaly Detection on OpenShift.pdf

Tosin Akinosho

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.

Mutation Testing for Task-Oriented Chatbots

Pablo Gómez Abajo

Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots. To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.

HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU

panagenda

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

Artificial Intelligence and Electronic Warfare

Papadakis K.-Cyber-Information Warfare Analyst & Cyber Defense/Security Consultant-Hellenic MoD

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/ Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit. The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers. Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.

Introduction of Cybersecurity with OSS at Code Europe 2024

Hiroshi SHIBATA

I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems. The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS. Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application. I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.

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Learning How to Build Event Streaming Applications with Pac-Man

1. The talk will start shortly - before then, have a chat with your fellow attendees! Questions at the end This is unless the speaker says otherwise. But generally, because of internet delays and lags it can be a bit disruptive during an Online talk. Online Meetup Etiquette Mute during talk But remember to unmute when you want to ask a question at the end (or chat with fellow attendees before/After) Use chat to engage Use Zoom chat and/or create a thread in the #events channel on our Community Slack space so that your discussion can continue afterwards with fellow Kafkateers. Be on Camera & React! Speakers during these events are talking to blank screens. And it can be exhausting! Use Zoom reactions at the bottom of the page to give them something to work off of! 1. 2. Continue learning and collaborating WELCOME! Thank you for joining us in these unique circumstances. We hope you’re safe and well. 3. If you haven’t already joined, get on the our slack workspace and continue the conversation with other Kafkateers. If you’re already a member, start a thread for this event in #events. A single online source of everything you’ll need to learn Kafka. Plus it’s totally free and ungated… Confluent Developer developer.confluent.iohttps://cnfl.io/slack Confluent Community Slack You are being recorded and this footage may be added to public channels

2. About me @riferrei | #kafkameetup | @CONFLUENTINC • RICARDO FERREIRA • Works for confluent • Developer advocate • Ricardo@confluent.iO • HTTPS://RIFERREI.NET

3. Building event streaming apps with pac-man @riferrei | #kafkameetup | @CONFLUENTINC

4. @riferrei | #kafkameetup | @CONFLUENTINC Current state SQL SQL SQL application application applicationdatabase LOG

5. @riferrei | #kafkameetup | @CONFLUENTINC Why?

6. @riferrei | #kafkameetup | @CONFLUENTINC I don’t know “my app only understands sql” “the source only understands sql” “I can only process atomic data” “that is what current books says” “it just feels right doing like this”

7. @riferrei | #kafkameetup | @CONFLUENTINC But what if… database 1000x more volume Non-transactional events Transactional events LOG

8. what? Are you kidding me?

9. @riferrei | #kafkameetup | @CONFLUENTINC This is fine

10. @riferrei | #kafkameetup | @CONFLUENTINC Solving the problem

11. @riferrei | #kafkameetup | @CONFLUENTINC Using the log database LOG 0 1 2 3 4 5 6 7 8LOG reads New records application a (time = 1) application b (time = 3)

12. @riferrei | #kafkameetup | @CONFLUENTINC But log programming hurts public void readTransactions() { while (true) { ConsumerRecords<String, Type> records = consumer.poll(Duration.ofMillis(100)); for (Record<String, Type> record : records) { if (record.offset() != lastSavedOffset) {...} } } }

13. @riferrei | #kafkameetup | @CONFLUENTINC What then?

14. @riferrei | #kafkameetup | @CONFLUENTINC Event streaming appS

15. @riferrei | @confluentinc | @itau

16. @riferrei | @confluentinc | @itau

17. @riferrei | #kafkameetup | @CONFLUENTINC PAC-MAN

18. @riferrei | #kafkameetup | @CONFLUENTINC https://github.com/confluentinc/demo-scene <<Pacman-ccloud>> Getting started

19. @riferrei | #kafkameetup | @CONFLUENTINC IMPLEMENTATION Api gateway Lambda function Kafka topic Ksqldb apps Kafka topic scoreboard

20. @riferrei | #kafkameetup | @CONFLUENTINC IMPLEMENTATION Kafka topic Ksqldb apps Kafka topic

21. @riferrei | #kafkameetup | @CONFLUENTINC Let’s create the streams

22. @riferrei | #kafkameetup | @CONFLUENTINC instructions 1. Get the game 2. Name yourself

23. @riferrei | #kafkameetup | @CONFLUENTINC BUILDING the scoreboard

24. @riferrei | #kafkameetup | @CONFLUENTINC Introduction to ksqldb https://ksqldb.io

25. @riferrei | #kafkameetup | @CONFLUENTINC Source: USER_GAME TOPIC

26. @riferrei | #kafkameetup | @CONFLUENTINC Creating User_game stream

27. @riferrei | #kafkameetup | @CONFLUENTINC Querying USER_GAME STREAM

28. @riferrei | #kafkameetup | @CONFLUENTINC Creating Stats_per_user table

29. @riferrei | #kafkameetup | @CONFLUENTINC Querying STATS_PER_USER TABLE

30. @riferrei | #kafkameetup | @CONFLUENTINC Low latency Pull queries

31. @riferrei | #kafkameetup | @CONFLUENTINC Source: User_losses topic

32. @riferrei | #kafkameetup | @CONFLUENTINC Creating USER_LOSSES STREAM

33. @riferrei | #kafkameetup | @CONFLUENTINC querying USER_LOSSES STREAM

34. @riferrei | #kafkameetup | @CONFLUENTINC Creating LOSSES_PER_USER TABLE

35. @riferrei | #kafkameetup | @CONFLUENTINC Querying LOSSES_PER_USER TABLE

36. @riferrei | #kafkameetup | @CONFLUENTINC Creating SCOREBOARD TABLE

37. @riferrei | #kafkameetup | @CONFLUENTINC Querying SCOREBOARD TABLE

38. @riferrei | #kafkameetup | @CONFLUENTINC Playing with The data

39. @riferrei | #kafkameetup | @CONFLUENTINC Read from kafka Api gateway Lambda function scoreboardRedis cache push

40. @riferrei | #kafkameetup | @CONFLUENTINC Read from kafka Amazon alexa Lambda function scoreboardRedis cache push

41. @riferrei | #kafkameetup | @CONFLUENTINC Read from ksqldb Your code Ksqldb table pull Kafka topic

42. @riferrei | #kafkameetup | @CONFLUENTINC Creating highest_score TABLE

43. @riferrei | #kafkameetup | @CONFLUENTINC Low latency Pull queries 69920

44. @riferrei | #kafkameetup | @CONFLUENTINC how can I learn more?

45. @riferrei | #kafkameetup | @CONFLUENTINC Get kafka: confluent cloud Try free: https://cnfl.io/confluent-cloud

46. @riferrei | #kafkameetup | @CONFLUENTINC https://cnfl.io/tutorials Get examples: kafka tutorials

47. @riferrei | #kafkameetup | @CONFLUENTINC https://cnfl.io/books Get books: o’reilly bundle

48. @riferrei | #kafkameetup | @CONFLUENTINC https://kafka-summit.org/events/kafka-summit-austin-2020 join kafka summit https://myeventi.events/kafka20/aus Use 25% discount code: KSa20Meetup

49. @riferrei | #kafkameetup | @CONFLUENTINC Thank you

Learning How to Build Event Streaming Applications with Pac-Man

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