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#apachekafka — Public Fediverse posts

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  1. flask-confluent-kafka

    Extensão Flask totalmente tipada (PEP 561) e testada para integrar producers e consumers do Confluent Kafka em poucas linhas — configuração direto pelo app.config, autenticação SASL opcional e suporte a múltiplos clientes nomeados, independentes do par padrão. Publicada no PyPI, com CI ativo e changelog versionado.

    🔗 github.com/Riverfount/flask-co

    #Python #Flask #Kafka #ApacheKafka

  2. flask-confluent-kafka

    Extensão Flask totalmente tipada (PEP 561) e testada para integrar producers e consumers do Confluent Kafka em poucas linhas — configuração direto pelo app.config, autenticação SASL opcional e suporte a múltiplos clientes nomeados, independentes do par padrão. Publicada no PyPI, com CI ativo e changelog versionado.

    🔗 github.com/Riverfount/flask-co

    #Python #Flask #Kafka #ApacheKafka

  3. flask-confluent-kafka

    Extensão Flask totalmente tipada (PEP 561) e testada para integrar producers e consumers do Confluent Kafka em poucas linhas — configuração direto pelo app.config, autenticação SASL opcional e suporte a múltiplos clientes nomeados, independentes do par padrão. Publicada no PyPI, com CI ativo e changelog versionado.

    🔗 github.com/Riverfount/flask-co

    #Python #Flask #Kafka #ApacheKafka

  4. flask-confluent-kafka

    Extensão Flask totalmente tipada (PEP 561) e testada para integrar producers e consumers do Confluent Kafka em poucas linhas — configuração direto pelo app.config, autenticação SASL opcional e suporte a múltiplos clientes nomeados, independentes do par padrão. Publicada no PyPI, com CI ativo e changelog versionado.

    🔗 github.com/Riverfount/flask-co

    #Python #Flask #Kafka #ApacheKafka

  5. flask-confluent-kafka

    Extensão Flask totalmente tipada (PEP 561) e testada para integrar producers e consumers do Confluent Kafka em poucas linhas — configuração direto pelo app.config, autenticação SASL opcional e suporte a múltiplos clientes nomeados, independentes do par padrão. Publicada no PyPI, com CI ativo e changelog versionado.

    🔗 github.com/Riverfount/flask-co

    #Python #Flask #Kafka #ApacheKafka

  6. Kafka CLI options driving you crazy? AI might finally have a solution.
    Grzegorz Kocur tested Confluent’s mcp-confluent server with Claude Code on local Kafka clusters running mTLS and SASL/SCRAM.
    Read to find out what happened: softwaremill.com/managing-kafk

    #Kafka #ApacheKafka #AI #MCP #DevOps #ClaudeAI #Confluent

  7. Kafka CLI options driving you crazy? AI might finally have a solution.
    Grzegorz Kocur tested Confluent’s mcp-confluent server with Claude Code on local Kafka clusters running mTLS and SASL/SCRAM.
    Read to find out what happened: softwaremill.com/managing-kafk

    #Kafka #ApacheKafka #AI #MCP #DevOps #ClaudeAI #Confluent

  8. Kafka CLI options driving you crazy? AI might finally have a solution.
    Grzegorz Kocur tested Confluent’s mcp-confluent server with Claude Code on local Kafka clusters running mTLS and SASL/SCRAM.
    Read to find out what happened: softwaremill.com/managing-kafk

    #Kafka #ApacheKafka #AI #MCP #DevOps #ClaudeAI #Confluent

  9. Kafka CLI options driving you crazy? AI might finally have a solution.
    Grzegorz Kocur tested Confluent’s mcp-confluent server with Claude Code on local Kafka clusters running mTLS and SASL/SCRAM.
    Read to find out what happened: softwaremill.com/managing-kafk

    #Kafka #ApacheKafka #AI #MCP #DevOps #ClaudeAI #Confluent

  10. Kafka CLI options driving you crazy? AI might finally have a solution.
    Grzegorz Kocur tested Confluent’s mcp-confluent server with Claude Code on local Kafka clusters running mTLS and SASL/SCRAM.
    Read to find out what happened: softwaremill.com/managing-kafk

    #Kafka #ApacheKafka #AI #MCP #DevOps #ClaudeAI #Confluent

  11. 𝗞𝗮𝗳𝗸𝗮𝗛𝗤:

    #ApacheKafka #GUI #KafkaHQ

    thewhale.cc/posts/kafkahq

    KafkaHQ is an open source GUI for Apache Kafka. Kafka GUI for topics, topics data, consumers group and more...

  12. 𝗞𝗮𝗳𝗸𝗮𝗛𝗤:

    #ApacheKafka #GUI #KafkaHQ

    thewhale.cc/posts/kafkahq

    KafkaHQ is an open source GUI for Apache Kafka. Kafka GUI for topics, topics data, consumers group and more...

  13. 𝗞𝗮𝗳𝗸𝗮𝗛𝗤:

    #ApacheKafka #GUI #KafkaHQ

    thewhale.cc/posts/kafkahq

    KafkaHQ is an open source GUI for Apache Kafka. Kafka GUI for topics, topics data, consumers group and more...

  14. 𝗞𝗮𝗳𝗸𝗮𝗛𝗤:

    #ApacheKafka #GUI #KafkaHQ

    thewhale.cc/posts/kafkahq

    KafkaHQ is an open source GUI for Apache Kafka. Kafka GUI for topics, topics data, consumers group and more...

  15. Apache Kafka concepts can be tricky; visualizing them helps.

    The SoftwareMill Kafka Visualization tool breaks down the fundamentals interactively so you can see how things work in real time.

    🆕 Now updated to support Kafka Share Groups.

    Give it a spin: softwaremill.com/kafka-visuali

    #ApacheKafka #Kafka #DistributedSystems #SoftwareEngineering

  16. Apache Kafka concepts can be tricky; visualizing them helps.

    The SoftwareMill Kafka Visualization tool breaks down the fundamentals interactively so you can see how things work in real time.

    🆕 Now updated to support Kafka Share Groups.

    Give it a spin: softwaremill.com/kafka-visuali

    #ApacheKafka #Kafka #DistributedSystems #SoftwareEngineering

  17. Apache Kafka concepts can be tricky; visualizing them helps.

    The SoftwareMill Kafka Visualization tool breaks down the fundamentals interactively so you can see how things work in real time.

    🆕 Now updated to support Kafka Share Groups.

    Give it a spin: softwaremill.com/kafka-visuali

    #ApacheKafka #Kafka #DistributedSystems #SoftwareEngineering

  18. Apache Kafka concepts can be tricky; visualizing them helps.

    The SoftwareMill Kafka Visualization tool breaks down the fundamentals interactively so you can see how things work in real time.

    🆕 Now updated to support Kafka Share Groups.

    Give it a spin: softwaremill.com/kafka-visuali

    #ApacheKafka #Kafka #DistributedSystems #SoftwareEngineering

  19. Apache Kafka concepts can be tricky; visualizing them helps.

    The SoftwareMill Kafka Visualization tool breaks down the fundamentals interactively so you can see how things work in real time.

    🆕 Now updated to support Kafka Share Groups.

    Give it a spin: softwaremill.com/kafka-visuali

    #ApacheKafka #Kafka #DistributedSystems #SoftwareEngineering

  20. I need to work on an #apachekafka project. Thinking of doing something for #netlabelday or maybe @friendsofccmusic more generally.

    Any ideas on what would be useful for #ccmusic and #netlabels ?

  21. I need to work on an #apachekafka project. Thinking of doing something for #netlabelday or maybe @friendsofccmusic more generally.

    Any ideas on what would be useful for #ccmusic and #netlabels ?

  22. I need to work on an #apachekafka project. Thinking of doing something for #netlabelday or maybe @friendsofccmusic more generally.

    Any ideas on what would be useful for #ccmusic and #netlabels ?

  23. I need to work on an #apachekafka project. Thinking of doing something for #netlabelday or maybe @friendsofccmusic more generally.

    Any ideas on what would be useful for #ccmusic and #netlabels ?

  24. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    🔹 State management & partition limits
    🔹 Message deduplication
    🔹 JVM tuning challenges
    🔹 Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    🔗 Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  25. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    🔹 State management & partition limits
    🔹 Message deduplication
    🔹 JVM tuning challenges
    🔹 Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    🔗 Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  26. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    🔹 State management & partition limits
    🔹 Message deduplication
    🔹 JVM tuning challenges
    🔹 Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    🔗 Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  27. Event-driven architecture promises scalability, but the real tradeoffs in Java-based real-time systems only show up in production.

    Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, Sagar Deepak Joshi's new #InfoQ article explores exactly where things break down:
    🔹 State management & partition limits
    🔹 Message deduplication
    🔹 JVM tuning challenges
    🔹 Cascading consumer failures

    Discover the Redis-backed patterns used to solve them and keep the system resilient.

    🔗 Read now for more insights: bit.ly/4bmaRPb

    #Java #SpringBoot #ApacheKafka #Redis #Microservices #SoftwareArchitecture

  28. Are Apache Kafka follower replicas synchronous or asynchronous?
    The confusing answer is: both.
    This article clears it up:
    softwaremill.com/apache-kafka-

    #ApacheKafka #Confluent

  29. Are Apache Kafka follower replicas synchronous or asynchronous?
    The confusing answer is: both.
    This article clears it up:
    softwaremill.com/apache-kafka-

    #ApacheKafka #Confluent

  30. Are Apache Kafka follower replicas synchronous or asynchronous?
    The confusing answer is: both.
    This article clears it up:
    softwaremill.com/apache-kafka-

    #ApacheKafka #Confluent

  31. Are Apache Kafka follower replicas synchronous or asynchronous?
    The confusing answer is: both.
    This article clears it up:
    softwaremill.com/apache-kafka-

    #ApacheKafka #Confluent

  32. Повторная обработка сообщений в Kafka Consumer

    Привет! Меня зовут Дмитрий Михеев, я ведущий разработчик в MAGNIT OMNI — бизнес-группе ритейлера «Магнит», которая отвечает за развитие омниканального опыта для клиентов. В своих сервисах для межсервисных коммуникаций помимо gRPC-запросов мы используем брокер сообщений Kafka. Если описывать его в двух словах, Kafka — это распределённый журнал событий (event log), через который сервисы обмениваются данными в реальном времени. Не буду подробно останавливаться на устройстве Kafka — это хорошо описано в документации. В этой статье хочу подсветить один неочевидный момент, который может привести к проблемам при работе с consumer’ами — повторную обработку сообщений (retry).

    habr.com/ru/companies/magnit/a

    #kafka #apachekafka #kafkaconsumer #retry #повторнаяобработка #идемпотентность #высоконагруженныесистемы #java

  33. Повторная обработка сообщений в Kafka Consumer

    Привет! Меня зовут Дмитрий Михеев, я ведущий разработчик в MAGNIT OMNI — бизнес-группе ритейлера «Магнит», которая отвечает за развитие омниканального опыта для клиентов. В своих сервисах для межсервисных коммуникаций помимо gRPC-запросов мы используем брокер сообщений Kafka. Если описывать его в двух словах, Kafka — это распределённый журнал событий (event log), через который сервисы обмениваются данными в реальном времени. Не буду подробно останавливаться на устройстве Kafka — это хорошо описано в документации. В этой статье хочу подсветить один неочевидный момент, который может привести к проблемам при работе с consumer’ами — повторную обработку сообщений (retry).

    habr.com/ru/companies/magnit/a

    #kafka #apachekafka #kafkaconsumer #retry #повторнаяобработка #идемпотентность #высоконагруженныесистемы #java

  34. Повторная обработка сообщений в Kafka Consumer

    Привет! Меня зовут Дмитрий Михеев, я ведущий разработчик в MAGNIT OMNI — бизнес-группе ритейлера «Магнит», которая отвечает за развитие омниканального опыта для клиентов. В своих сервисах для межсервисных коммуникаций помимо gRPC-запросов мы используем брокер сообщений Kafka. Если описывать его в двух словах, Kafka — это распределённый журнал событий (event log), через который сервисы обмениваются данными в реальном времени. Не буду подробно останавливаться на устройстве Kafka — это хорошо описано в документации. В этой статье хочу подсветить один неочевидный момент, который может привести к проблемам при работе с consumer’ами — повторную обработку сообщений (retry).

    habr.com/ru/companies/magnit/a

    #kafka #apachekafka #kafkaconsumer #retry #повторнаяобработка #идемпотентность #высоконагруженныесистемы #java

  35. Schema proliferation builds slowly and gets expensive fast.

    One schema per event type seems reasonable - until you're:
    • Managing 10+ tables
    • Writing union queries across all of them
    • Propagating a single field rename everywhere

    The Alternative❓ Discriminator-based schema consolidation.

    It reduces schema count, simplifies downstream consumption, and makes schema evolution manageable. New variants become additive changes instead of breaking existing consumers.

    🔗 Check out the #InfoQ article for a practical look at the pattern and its trade-offs ⇨ bit.ly/4uC023n

    #Java #ApacheKafka #ApacheFlink

  36. Schema proliferation builds slowly and gets expensive fast.

    One schema per event type seems reasonable - until you're:
    • Managing 10+ tables
    • Writing union queries across all of them
    • Propagating a single field rename everywhere

    The Alternative❓ Discriminator-based schema consolidation.

    It reduces schema count, simplifies downstream consumption, and makes schema evolution manageable. New variants become additive changes instead of breaking existing consumers.

    🔗 Check out the #InfoQ article for a practical look at the pattern and its trade-offs ⇨ bit.ly/4uC023n

    #Java #ApacheKafka #ApacheFlink

  37. Schema proliferation builds slowly and gets expensive fast.

    One schema per event type seems reasonable - until you're:
    • Managing 10+ tables
    • Writing union queries across all of them
    • Propagating a single field rename everywhere

    The Alternative❓ Discriminator-based schema consolidation.

    It reduces schema count, simplifies downstream consumption, and makes schema evolution manageable. New variants become additive changes instead of breaking existing consumers.

    🔗 Check out the #InfoQ article for a practical look at the pattern and its trade-offs ⇨ bit.ly/4uC023n

    #Java #ApacheKafka #ApacheFlink

  38. Schema proliferation builds slowly and gets expensive fast.

    One schema per event type seems reasonable - until you're:
    • Managing 10+ tables
    • Writing union queries across all of them
    • Propagating a single field rename everywhere

    The Alternative❓ Discriminator-based schema consolidation.

    It reduces schema count, simplifies downstream consumption, and makes schema evolution manageable. New variants become additive changes instead of breaking existing consumers.

    🔗 Check out the #InfoQ article for a practical look at the pattern and its trade-offs ⇨ bit.ly/4uC023n

    #Java #ApacheKafka #ApacheFlink

  39. This #InfoQ article examines how #ApacheKafka is evolving toward a #CloudNative architecture through tiered storage, elastic consumers, virtual clusters, and diskless storage proposals.

    Read now: bit.ly/4u0hZr0

    #AI #CloudComputing #kafkastreams #softwarearchitecture

  40. This #InfoQ article examines how #ApacheKafka is evolving toward a #CloudNative architecture through tiered storage, elastic consumers, virtual clusters, and diskless storage proposals.

    Read now: bit.ly/4u0hZr0

    #AI #CloudComputing #kafkastreams #softwarearchitecture

  41. This #InfoQ article examines how #ApacheKafka is evolving toward a #CloudNative architecture through tiered storage, elastic consumers, virtual clusters, and diskless storage proposals.

    Read now: bit.ly/4u0hZr0

    #AI #CloudComputing #kafkastreams #softwarearchitecture

  42. This #InfoQ article examines how #ApacheKafka is evolving toward a #CloudNative architecture through tiered storage, elastic consumers, virtual clusters, and diskless storage proposals.

    Read now: bit.ly/4u0hZr0

    #AI #CloudComputing #kafkastreams #softwarearchitecture

  43. Kafka Streaming for Cyber Security 🔐🚀

    Built a multi-source streaming engine pushing to Kafka:

    • Network logs – CICIDS2017 style (500/sec, 5% attacks)
    • User activity – Insider threat patterns (50/sec)
    • System events – ADFA-LD host intrusions (200/sec)
    • Correlated alerts – Real-time threat detection

    Attack simulation: DDoS, Botnet, Web Shell, Rootkit

    Kafka = Perfect for SIEM data ingestion! 📊

    #ApacheKafka #CyberSecurity #ThreatDetection

  44. #Confluent introduces a new approach in #ApacheKafka that moves schema IDs from message payloads to record headers.

    ✅ Simplify schema governance & evolution.
    ✅ Improve compatibility across serialization formats
    ✅ Reduce coupling between data & metadata in event-driven architectures

    Read the deep dive on #InfoQbit.ly/4tF7Fot

    #ML #EventStreamProcessing #ProtocolBuffers #DataPipelines #DataAnalytics

  45. #Confluent introduces a new approach in #ApacheKafka that moves schema IDs from message payloads to record headers.

    ✅ Simplify schema governance & evolution.
    ✅ Improve compatibility across serialization formats
    ✅ Reduce coupling between data & metadata in event-driven architectures

    Read the deep dive on #InfoQbit.ly/4tF7Fot

    #ML #EventStreamProcessing #ProtocolBuffers #DataPipelines #DataAnalytics

  46. #Confluent introduces a new approach in #ApacheKafka that moves schema IDs from message payloads to record headers.

    ✅ Simplify schema governance & evolution.
    ✅ Improve compatibility across serialization formats
    ✅ Reduce coupling between data & metadata in event-driven architectures

    Read the deep dive on #InfoQbit.ly/4tF7Fot

    #ML #EventStreamProcessing #ProtocolBuffers #DataPipelines #DataAnalytics

  47. #Confluent introduces a new approach in #ApacheKafka that moves schema IDs from message payloads to record headers.

    ✅ Simplify schema governance & evolution.
    ✅ Improve compatibility across serialization formats
    ✅ Reduce coupling between data & metadata in event-driven architectures

    Read the deep dive on #InfoQbit.ly/4tF7Fot

    #ML #EventStreamProcessing #ProtocolBuffers #DataPipelines #DataAnalytics

  48. Grafana Labs announced #Grafana 13, introducing a new Loki Kafka-backed ingestion architecture and AI Observability in Grafana Cloud for real-time monitoring and evaluation of AI systems.

    The release also includes GCX, a new CLI designed to surface Grafana Cloud data inside agentic development environments.

    Details here 👉 bit.ly/4mPUqz8

    #Observability #AI #DevOps #ApacheKafka #InfoQ

  49. Grafana Labs announced #Grafana 13, introducing a new Loki Kafka-backed ingestion architecture and AI Observability in Grafana Cloud for real-time monitoring and evaluation of AI systems.

    The release also includes GCX, a new CLI designed to surface Grafana Cloud data inside agentic development environments.

    Details here 👉 bit.ly/4mPUqz8

    #Observability #AI #DevOps #ApacheKafka #InfoQ

  50. Grafana Labs announced #Grafana 13, introducing a new Loki Kafka-backed ingestion architecture and AI Observability in Grafana Cloud for real-time monitoring and evaluation of AI systems.

    The release also includes GCX, a new CLI designed to surface Grafana Cloud data inside agentic development environments.

    Details here 👉 bit.ly/4mPUqz8

    #Observability #AI #DevOps #ApacheKafka #InfoQ

  51. Grafana Labs announced #Grafana 13, introducing a new Loki Kafka-backed ingestion architecture and AI Observability in Grafana Cloud for real-time monitoring and evaluation of AI systems.

    The release also includes GCX, a new CLI designed to surface Grafana Cloud data inside agentic development environments.

    Details here 👉 bit.ly/4mPUqz8

    #Observability #AI #DevOps #ApacheKafka #InfoQ