Managing the Basho Data Platform with the Cloudsoft UX, including Riak blueprints in Apache Brooklyn and building up to tiered dynamic IoT analytics management
Unified Data Processing with Apache Flink and Apache Pulsar_Seth WiesmanStreamNative
Come learn how the combination of Apache Pulsar and Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable. The new world of applications and fast data architectures has broken up the database: Raw data persistence comes in the form of event logs, and the state of the world is computed by a stream processor. Apache Pulsar provides a strong solution for the event log, while Apache Flink forms a powerful foundation for the computation over the event streams.
We will discuss the key concepts behind Apache Flink's approach to stream processing and how it is a powerful abstraction for stateful event-driven applications. We will then see how to use Flink in conjunction with Apache Pulsar to creates a unified data processing platform.
Ten reasons to choose Apache Pulsar over Apache Kafka for Event Sourcing_Robe...StreamNative
More and more developer want to build cloud-native distributed application or microservices by making use of high performing, cloud-agnostic messaging technology for maximum decoupling. The only thing we do not want is the hassle of managing the complex message infrasturcture needed for the job, or the risk of getting into a vendor lock-in. Generally developers know Apache Kafka, but for event sourcing or the CQRS pattern Kafka is not really suitable. In this talk I will give you at least ten reasons why to choose Pulsar over Kafka for event sourcing and data consensus.
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies?Kai Wähner
Microservices became the new black in enterprise architectures. APIs provide functions to other applications or end users. Even if your architecture uses another pattern than microservices, like SOA (Service-Oriented Architecture) or Client-Server communication, APIs are used between the different applications and end users.
Apache Kafka plays a key role in modern microservice architectures to build open, scalable, flexible and decoupled real time applications. API Management complements Kafka by providing a way to implement and govern the full life cycle of the APIs.
This session explores how event streaming with Apache Kafka and API Management (including API Gateway and Service Mesh technologies) complement and compete with each other depending on the use case and point of view of the project team. The session concludes exploring the vision of event streaming APIs instead of RPC calls.
Understand how event streaming with Kafka and Confluent complements tools and frameworks such as Kong, Mulesoft, Apigee, Envoy, Istio, Linkerd, Software AG, TIBCO Mashery, IBM, Axway, etc.
A Streaming API Data Exchangeprovides streaming replication between business units and companies. API Management with REST/HTTP is not appropriate for streaming data.
MongoDB .local London 2019: Streaming Data on the Shoulders of GiantsLisa Roth, PMP
Life doesn't happen in batch mode which is why application engineers and data architects need to closely cooperate to get the best out of streaming platforms like Apache Kafka and NoSQL data stores such as MongoDB. This session explores ways and means to integrate both worlds in a streaming fashion.
From data stream management to distributed dataflows and beyondVasia Kalavri
Recent efforts by academia and open-source communities have established stream processing as a principal data analysis technology across industry. All major cloud vendors offer streaming dataflow pipelines and online analytics as managed services. Notable use-cases include real-time fault detection in space networks, city traffic management, dynamic pricing for car-sharing, and anomaly detection in financial transactions. At the same time, streaming dataflow systems are increasingly being used for event-driven applications beyond analytics, such as orchestrating microservices and model serving. In the past decades, streaming technology has evolved significantly, however, emerging applications are once more challenging the design decisions of modern streaming systems. In this talk, I will discuss the evolution of stream processing and bring current trends and open problems to the attention of our community.
Kafka is evolving to remove its dependency on Zookeeper. The Kafka Improvement Proposal 500 (KIP-500) aims to manage Kafka's metadata log with a self-managed Raft consensus algorithm and controller quorum rather than relying on Zookeeper. This will improve scalability, robustness, and make deployment easier. It will take multiple releases to fully implement KIP-500, beginning with removing Zookeeper from clients and ending with a release where Zookeeper is no longer required.
This document discusses cloud-native Apache Kafka and Kubernetes integrations. It introduces Apache Kafka as a proven technology for real-time data processing and describes how it is used for digital experiences, microservices applications, streaming ETL, and real-time analytics. It then makes the case for using a cloud-native, managed Kafka platform like Red Hat OpenShift Streams, which provides a reduced complexity solution with a managed Apache Kafka cluster. Finally, it provides an overview of Red Hat OpenShift Streams and demonstrates how to use Kamelets to easily integrate and develop stream-based applications on Kubernetes.
Redis and Kafka - Advanced Microservices Design Patterns SimplifiedAllen Terleto
The adoption and popularity of the microservices architecture continues to grow across a spectrum of enterprises in every industry. Although a consensus on an implementation standard has yet to be reached, advanced design patterns and lessons learned about the complexities and pitfalls of deploying microservices at scale have been established by thought leaders and the development community. With Redis and Kafka becoming de facto standards across most microservices architectures, we will discuss how their combination can be used to simplify the implementation of event-driven design patterns that will provide real-time performance, scalability, resiliency, traceability to ensure compliance, observability, reduced technology sprawl, and scale to thousands of services. In this discussion, we will decompose a real-time event-driven payment-processing microservices workflow to explore capturing telemetry data, event sourcing, CQRS, orchestrated SAGA workflows, inter-service communication, state machines, and more.
Unified Data Processing with Apache Flink and Apache Pulsar_Seth WiesmanStreamNative
Come learn how the combination of Apache Pulsar and Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable. The new world of applications and fast data architectures has broken up the database: Raw data persistence comes in the form of event logs, and the state of the world is computed by a stream processor. Apache Pulsar provides a strong solution for the event log, while Apache Flink forms a powerful foundation for the computation over the event streams.
We will discuss the key concepts behind Apache Flink's approach to stream processing and how it is a powerful abstraction for stateful event-driven applications. We will then see how to use Flink in conjunction with Apache Pulsar to creates a unified data processing platform.
Ten reasons to choose Apache Pulsar over Apache Kafka for Event Sourcing_Robe...StreamNative
More and more developer want to build cloud-native distributed application or microservices by making use of high performing, cloud-agnostic messaging technology for maximum decoupling. The only thing we do not want is the hassle of managing the complex message infrasturcture needed for the job, or the risk of getting into a vendor lock-in. Generally developers know Apache Kafka, but for event sourcing or the CQRS pattern Kafka is not really suitable. In this talk I will give you at least ten reasons why to choose Pulsar over Kafka for event sourcing and data consensus.
Apache Kafka and API Management / API Gateway – Friends, Enemies or Frenemies?Kai Wähner
Microservices became the new black in enterprise architectures. APIs provide functions to other applications or end users. Even if your architecture uses another pattern than microservices, like SOA (Service-Oriented Architecture) or Client-Server communication, APIs are used between the different applications and end users.
Apache Kafka plays a key role in modern microservice architectures to build open, scalable, flexible and decoupled real time applications. API Management complements Kafka by providing a way to implement and govern the full life cycle of the APIs.
This session explores how event streaming with Apache Kafka and API Management (including API Gateway and Service Mesh technologies) complement and compete with each other depending on the use case and point of view of the project team. The session concludes exploring the vision of event streaming APIs instead of RPC calls.
Understand how event streaming with Kafka and Confluent complements tools and frameworks such as Kong, Mulesoft, Apigee, Envoy, Istio, Linkerd, Software AG, TIBCO Mashery, IBM, Axway, etc.
A Streaming API Data Exchangeprovides streaming replication between business units and companies. API Management with REST/HTTP is not appropriate for streaming data.
MongoDB .local London 2019: Streaming Data on the Shoulders of GiantsLisa Roth, PMP
Life doesn't happen in batch mode which is why application engineers and data architects need to closely cooperate to get the best out of streaming platforms like Apache Kafka and NoSQL data stores such as MongoDB. This session explores ways and means to integrate both worlds in a streaming fashion.
From data stream management to distributed dataflows and beyondVasia Kalavri
Recent efforts by academia and open-source communities have established stream processing as a principal data analysis technology across industry. All major cloud vendors offer streaming dataflow pipelines and online analytics as managed services. Notable use-cases include real-time fault detection in space networks, city traffic management, dynamic pricing for car-sharing, and anomaly detection in financial transactions. At the same time, streaming dataflow systems are increasingly being used for event-driven applications beyond analytics, such as orchestrating microservices and model serving. In the past decades, streaming technology has evolved significantly, however, emerging applications are once more challenging the design decisions of modern streaming systems. In this talk, I will discuss the evolution of stream processing and bring current trends and open problems to the attention of our community.
Kafka is evolving to remove its dependency on Zookeeper. The Kafka Improvement Proposal 500 (KIP-500) aims to manage Kafka's metadata log with a self-managed Raft consensus algorithm and controller quorum rather than relying on Zookeeper. This will improve scalability, robustness, and make deployment easier. It will take multiple releases to fully implement KIP-500, beginning with removing Zookeeper from clients and ending with a release where Zookeeper is no longer required.
This document discusses cloud-native Apache Kafka and Kubernetes integrations. It introduces Apache Kafka as a proven technology for real-time data processing and describes how it is used for digital experiences, microservices applications, streaming ETL, and real-time analytics. It then makes the case for using a cloud-native, managed Kafka platform like Red Hat OpenShift Streams, which provides a reduced complexity solution with a managed Apache Kafka cluster. Finally, it provides an overview of Red Hat OpenShift Streams and demonstrates how to use Kamelets to easily integrate and develop stream-based applications on Kubernetes.
Redis and Kafka - Advanced Microservices Design Patterns SimplifiedAllen Terleto
The adoption and popularity of the microservices architecture continues to grow across a spectrum of enterprises in every industry. Although a consensus on an implementation standard has yet to be reached, advanced design patterns and lessons learned about the complexities and pitfalls of deploying microservices at scale have been established by thought leaders and the development community. With Redis and Kafka becoming de facto standards across most microservices architectures, we will discuss how their combination can be used to simplify the implementation of event-driven design patterns that will provide real-time performance, scalability, resiliency, traceability to ensure compliance, observability, reduced technology sprawl, and scale to thousands of services. In this discussion, we will decompose a real-time event-driven payment-processing microservices workflow to explore capturing telemetry data, event sourcing, CQRS, orchestrated SAGA workflows, inter-service communication, state machines, and more.
Cloud Native London 2019 Faas composition using Kafka and cloud-eventsNeil Avery
Serverless functions or FaaS are all the rage.
By leveraging well established event-driven microservice design principles and applying them to serverless functions you can build a homogenous ecosystem to run FaaS applications. Kafka’s natural ability to store and replay events means serverless functions can not only be replayed, but they can also be used to choreograph call chains or driven using orchestration. Kafka also means you can democratize and organize FaaS environments in a way that scales across the enterprise. Underpinning this mantra is the use of Cloud Events by the CNCF serverless working group (of which Confluent is an active member).
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
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.
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
Apache Kafka can act as both an enemy and a friend to traditional middleware like message queues, ETL tools, and enterprise service buses. As an enemy, Kafka replaces many of the individual components and provides a single scalable platform for messaging, storage, and processing. However, Kafka can also integrate with traditional middleware as a friend through connectors and client APIs, allowing certain use cases to still leverage existing tools. In complex environments with both new and legacy systems, Kafka acts as a "frenemy" - replacing some functions but integrating with other existing technologies to provide a bridge to new architectures.
Axway presented on its API Management Plus solution. The presentation covered Axway's vision of digital transformation and customer experience networks. It then demonstrated API Management Plus's full lifecycle API management capabilities. This includes API creation, governance, consumption, and measurement. The solution aims to streamline digital innovation and increase ecosystem engagement.
Build a Bridge to Cloud with Apache Kafka® for Data Analytics Cloud Servicesconfluent
Build a Bridge to Cloud with Apache Kafka® for Data Analytics Cloud Services, Perry Krol, Head of Systems Engineering, CEMEA, Confluent
https://www.meetup.com/Frankfurt-Apache-Kafka-Meetup-by-Confluent/events/269751169/
Making Kafka Cloud Native | Jay Kreps, Co-Founder & CEO, ConfluentHostedbyConfluent
A talk discussing the rise of Apache Kafka and data in motion plus the impact of cloud native data systems. This talk will cover how Kafka needs to evolve to keep up with the future of cloud, what this means for distributed systems engineers, and what work is being done to truly make Kafka Cloud Native
Modern Cloud-Native Streaming Platforms: Event Streaming Microservices with K...confluent
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value. Kafka is providing developers a critically important component as they build and modernize applications to cloud-native architecture. This talk will explore:
• Why cloud-native platforms and why run Kafka on Kubernetes?
• What kind of workloads are best suited for this combination?
• Tips to determine the path forward for legacy monoliths in your application portfolio
• Running Kafka as a Streaming Platform on Container Orchestration
Concepts and Patterns for Streaming Services with KafkaQAware GmbH
Cloud Native Night March 2020, Mainz: Talk by Perry Krol (@perkrol, Confluent)
=== Please download slides if blurred! ===
Abstract: Proven approaches such as service-oriented and event-driven architectures are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but they provide a more holistic and compelling approach when applied together. In this session Confluent will provide insights how service-based architectures and stream processing tools such as Apache Kafka® can help you build business-critical systems. You will learn why streaming beats request-response based architectures in complex, contemporary use cases, and explain why replayable logs such as Kafka provide a backbone for both service communication and shared datasets.
Based on these principles, we will explore how event collaboration and event sourcing patterns increase safety and recoverability with functional, event-driven approaches, apply patterns including Event Sourcing and CQRS, and how to build multi-team systems with microservices and SOA using patterns such as “inside out databases” and “event streams as a source of truth”.
Why Cloud-Native Kafka Matters: 4 Reasons to Stop Managing it YourselfDATAVERSITY
The document discusses 4 reasons to use a cloud-native Kafka service like Confluent Cloud instead of managing Kafka yourself. It notes that managing Kafka requires significant investment of time and resources for tasks like architecture planning, cluster sizing, software upgrades, and more. A cloud-native service handles all operational overhead automatically so you can focus on your core business. Confluent Cloud specifically offers elastic scaling, infinite data retention, global access across clouds, and integrations to make it a complete data streaming platform.
Express Scripts: Driving Digital Transformation from Mainframe to Microservicesconfluent
Watch this talk here: https://www.confluent.io/online-talks/express-scripts-digital-transformation-from-mainframe-to-microservices
Speakers: Ankur Kaneria, Principal Architect, Express Scripts + Kevin Petrie, Senior Director, Attunity + Alan Hsia, Group Manager, Product Marketing, Confluent
Express Scripts is reimagining its data architecture to bring best-in-class user experience and provide the foundation of next-generation applications. The challenge lies in the ability to efficiently and cost-effectively access the ever-increasing amount of data.
This online talk will showcase how Apache Kafka® plays a key role within Express Scripts’ transformation from mainframe to a microservices-based ecosystem, ensuring data integrity between two worlds. It will discuss how change data capture (CDC) technology is leveraged to stream data changes to Confluent Platform, allowing a low-latency data pipeline to be built.
Watch now to learn:
-Why Apache Kafka is an ideal data integration platform for microservices
-How Express Scripts is building cloud-based microservices when the system of record is a relational database residing on an on-premise mainframe
-How Confluent Platform allows for data integrity between disparate platforms and meets real time SLAs and low-latency requirements
-How Attunity Replicate software is leveraged to stream data changes to Apache Kafka, allowing you to build a low-latency data pipeline
IoT Sensor Analytics with Kafka, ksqlDB and TensorFlowKai Wähner
Use cases and architectures for IoT projects leveraging Apache Kafka, ksqlDB, machine Learning / deep Learning frameworks like TensorFlow, and cloud infrastructure.
Large numbers of IoT devices lead to big data and the need for further processing and analysis. Apache Kafka is a highly scalable and distributed open source streaming platform, which can connect to MQTT and other IoT standards. Kafka ingests, stores, processes and forwards high volumes of data from thousands of IoT devices.
The rapidly expanding world of stream processing can be daunting, with new concepts such as various types of time semantics, windowed aggregates, changelogs, and programming frameworks to master. KSQL is the streaming SQL engine on top of Apache Kafka which simplifies all this and make stream processing available to everyone without the need to write source code.
This talk shows how to leverage Kafka and KSQL in an IoT sensor analytics scenario for predictive maintenance and integration with real time monitoring systems. A live demo shows how to embed and deploy Machine Learning models - built with frameworks like TensorFlow, DeepLearning4J or H2O - into mission-critical and scalable real time applications.
GCP for Apache Kafka® Users: Stream Ingestion and Processingconfluent
Watch this talk here: https://www.confluent.io/online-talks/gcp-for-apache-kafka-users-stream-ingestion-processing
In private and public clouds, stream analytics commonly means stateless processing systems organized around Apache Kafka® or a similar distributed log service. GCP took a somewhat different tack, with Cloud Pub/Sub, Dataflow, and BigQuery, distributing the responsibility for processing among ingestion, processing and database technologies.
We compare the two approaches to data integration and show how Dataflow allows you to join and transform and deliver data streams among on-prem and cloud Apache Kafka clusters, Cloud Pub/Sub topics and a variety of databases. The session will have a mix of architectural discussions and practical code reviews of Dataflow-based pipelines.
New Features in Confluent Platform 6.0 / Apache Kafka 2.6Kai Wähner
New Features in Confluent Platform 6.0 / Apache Kafka 2.6, including REST Proxy and API, Tiered Storage for AWS S3 and GCP GCS, Cluster Linking (On-Premise, Edge, Hybrid, Multi-Cloud), Self-Balancing Clusters), ksqlDB.
Advanced Stream Processing with Flink and Pulsar - Pulsar Summit NA 2021 KeynoteStreamNative
In this talk, Till Rohrmann and Addison Higham discuss how Flink allows for ambitious stream processing workflows and how Pulsar and Flink enable new capabilities that push forward the state-of-the-art in streaming. They will also share upcoming features and new capabilities in the integrations between Flink and Pulsar and how these two communities are working together to truly advance the power of stream processing.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
Introducing Dapr.io - the open source personal assistant to microservices and...Lucas Jellema
Dapr.io is an open source product, originated from Microsoft and embraced by a broad coalition of cloud suppliers (part of CNFC) and open source projects. Dapr is a runtime framework that can support any application and that especially shines with distributed applications - for example microservices - that run in containers, spread over clouds and / or edge devices.
With Dapr you give an application a "sidecar" - a kind of personal assistant that takes care of all kinds of common responsibilities. Capturing and retrieving state, publishing and consuming messages or events. Reading secrets and configuration data. Shielding and load balancing over service endpoints. Calling and subscribing to all kinds of SaaS and PaaS facilities. Logging traces across all kinds of application components and logically routing calls between microservices and other application components. Dapr provides generic APIs to the application (HTTP and gRPC) for calling all these generic services – and provides implementations of these APIs for all public clouds and dozens of technology components. This means that your application can easily make use of a wide range of relevant features - with a strict separation between the language the application uses for this (generic, simple) and the configuration of the specific technology (e.g. Redis, MySQL, CosmosDB, Cassandra, PostgreSQL, Oracle Database, MongoDB, Azure SQL etc) that the Dapr sidecar uses. Changing technology does not affect the application, but affects the configuration of the Sidecar. Dapr can be used from applications in any technology - from Java and C#/.NET to Go, Python, Node, Rust and PHP. Or whatever can talk HTTP (or gRPC).
In this Code Café I will introduce you to Dapr.io. I will show you what Dapr can do for you (application) and how you can Dapr-izen an application. I'll show you how an asynchronously collaborative system of microservices - implemented in different technologies - can be easily connected to Dapr, first to Redis as a Pub/Sub mechanism and then also to Apache Kafka without modifications. Then we do - with the interested parties - also a hands-on in which you will apply Dapr yourself . In a short time you get a good feel for how you can use Dapr for different aspects of your applications. And if nothing else, Dapr is a very easy way to get your code with Kafka, S3, Redis, Azure EventGrid, HashiCorp Consul, Twillio, Pulsar, RabbitMQ, HashiCorp Vault, AWS Secret Manager, Azure KeyVault, Cron, SMTP, Twitter, AWS SQS & SNS, GCP Pub/Sub and dozens of other technology components talk.
A guide through the Azure Messaging services - Update ConferenceEldert Grootenboer
https://www.updateconference.net/en/2019/session/a-guide-through-the-azure-messaging-services
A guide through the Azure Messaging services - Update Conference
Spark (Structured) Streaming vs. Kafka Streams - two stream processing platfo...Guido Schmutz
Spark Streaming and Kafka Streams are two popular stream processing platforms. Spark Streaming uses micro-batching and allows for code reuse between batch and streaming jobs. Kafka Streams is embedded directly into Apache Kafka and leverages Kafka as its internal messaging layer. Both platforms support stateful stream processing operations like windowing, aggregations, and joins through distributed state stores. A demo application is shown that detects dangerous driving by joining truck position data with driver data using different streaming techniques.
Dropbox is a free service that automatically syncs and saves files across a user's devices. Any file saved to the Dropbox folder on one device is instantly available on all other linked devices. The Dropbox folder works just like any other folder but syncs file changes in real-time. Users can drag and drop files into their Dropbox folder to upload them or access files from any device.
Cloud Native London 2019 Faas composition using Kafka and cloud-eventsNeil Avery
Serverless functions or FaaS are all the rage.
By leveraging well established event-driven microservice design principles and applying them to serverless functions you can build a homogenous ecosystem to run FaaS applications. Kafka’s natural ability to store and replay events means serverless functions can not only be replayed, but they can also be used to choreograph call chains or driven using orchestration. Kafka also means you can democratize and organize FaaS environments in a way that scales across the enterprise. Underpinning this mantra is the use of Cloud Events by the CNCF serverless working group (of which Confluent is an active member).
Kafka for Real-Time Replication between Edge and Hybrid CloudKai Wähner
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.
Apache Kafka vs. Integration Middleware (MQ, ETL, ESB) - Friends, Enemies or ...confluent
Apache Kafka can act as both an enemy and a friend to traditional middleware like message queues, ETL tools, and enterprise service buses. As an enemy, Kafka replaces many of the individual components and provides a single scalable platform for messaging, storage, and processing. However, Kafka can also integrate with traditional middleware as a friend through connectors and client APIs, allowing certain use cases to still leverage existing tools. In complex environments with both new and legacy systems, Kafka acts as a "frenemy" - replacing some functions but integrating with other existing technologies to provide a bridge to new architectures.
Axway presented on its API Management Plus solution. The presentation covered Axway's vision of digital transformation and customer experience networks. It then demonstrated API Management Plus's full lifecycle API management capabilities. This includes API creation, governance, consumption, and measurement. The solution aims to streamline digital innovation and increase ecosystem engagement.
Build a Bridge to Cloud with Apache Kafka® for Data Analytics Cloud Servicesconfluent
Build a Bridge to Cloud with Apache Kafka® for Data Analytics Cloud Services, Perry Krol, Head of Systems Engineering, CEMEA, Confluent
https://www.meetup.com/Frankfurt-Apache-Kafka-Meetup-by-Confluent/events/269751169/
Making Kafka Cloud Native | Jay Kreps, Co-Founder & CEO, ConfluentHostedbyConfluent
A talk discussing the rise of Apache Kafka and data in motion plus the impact of cloud native data systems. This talk will cover how Kafka needs to evolve to keep up with the future of cloud, what this means for distributed systems engineers, and what work is being done to truly make Kafka Cloud Native
Modern Cloud-Native Streaming Platforms: Event Streaming Microservices with K...confluent
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value. Kafka is providing developers a critically important component as they build and modernize applications to cloud-native architecture. This talk will explore:
• Why cloud-native platforms and why run Kafka on Kubernetes?
• What kind of workloads are best suited for this combination?
• Tips to determine the path forward for legacy monoliths in your application portfolio
• Running Kafka as a Streaming Platform on Container Orchestration
Concepts and Patterns for Streaming Services with KafkaQAware GmbH
Cloud Native Night March 2020, Mainz: Talk by Perry Krol (@perkrol, Confluent)
=== Please download slides if blurred! ===
Abstract: Proven approaches such as service-oriented and event-driven architectures are joined by newer techniques such as microservices, reactive architectures, DevOps, and stream processing. Many of these patterns are successful by themselves, but they provide a more holistic and compelling approach when applied together. In this session Confluent will provide insights how service-based architectures and stream processing tools such as Apache Kafka® can help you build business-critical systems. You will learn why streaming beats request-response based architectures in complex, contemporary use cases, and explain why replayable logs such as Kafka provide a backbone for both service communication and shared datasets.
Based on these principles, we will explore how event collaboration and event sourcing patterns increase safety and recoverability with functional, event-driven approaches, apply patterns including Event Sourcing and CQRS, and how to build multi-team systems with microservices and SOA using patterns such as “inside out databases” and “event streams as a source of truth”.
Why Cloud-Native Kafka Matters: 4 Reasons to Stop Managing it YourselfDATAVERSITY
The document discusses 4 reasons to use a cloud-native Kafka service like Confluent Cloud instead of managing Kafka yourself. It notes that managing Kafka requires significant investment of time and resources for tasks like architecture planning, cluster sizing, software upgrades, and more. A cloud-native service handles all operational overhead automatically so you can focus on your core business. Confluent Cloud specifically offers elastic scaling, infinite data retention, global access across clouds, and integrations to make it a complete data streaming platform.
Express Scripts: Driving Digital Transformation from Mainframe to Microservicesconfluent
Watch this talk here: https://www.confluent.io/online-talks/express-scripts-digital-transformation-from-mainframe-to-microservices
Speakers: Ankur Kaneria, Principal Architect, Express Scripts + Kevin Petrie, Senior Director, Attunity + Alan Hsia, Group Manager, Product Marketing, Confluent
Express Scripts is reimagining its data architecture to bring best-in-class user experience and provide the foundation of next-generation applications. The challenge lies in the ability to efficiently and cost-effectively access the ever-increasing amount of data.
This online talk will showcase how Apache Kafka® plays a key role within Express Scripts’ transformation from mainframe to a microservices-based ecosystem, ensuring data integrity between two worlds. It will discuss how change data capture (CDC) technology is leveraged to stream data changes to Confluent Platform, allowing a low-latency data pipeline to be built.
Watch now to learn:
-Why Apache Kafka is an ideal data integration platform for microservices
-How Express Scripts is building cloud-based microservices when the system of record is a relational database residing on an on-premise mainframe
-How Confluent Platform allows for data integrity between disparate platforms and meets real time SLAs and low-latency requirements
-How Attunity Replicate software is leveraged to stream data changes to Apache Kafka, allowing you to build a low-latency data pipeline
IoT Sensor Analytics with Kafka, ksqlDB and TensorFlowKai Wähner
Use cases and architectures for IoT projects leveraging Apache Kafka, ksqlDB, machine Learning / deep Learning frameworks like TensorFlow, and cloud infrastructure.
Large numbers of IoT devices lead to big data and the need for further processing and analysis. Apache Kafka is a highly scalable and distributed open source streaming platform, which can connect to MQTT and other IoT standards. Kafka ingests, stores, processes and forwards high volumes of data from thousands of IoT devices.
The rapidly expanding world of stream processing can be daunting, with new concepts such as various types of time semantics, windowed aggregates, changelogs, and programming frameworks to master. KSQL is the streaming SQL engine on top of Apache Kafka which simplifies all this and make stream processing available to everyone without the need to write source code.
This talk shows how to leverage Kafka and KSQL in an IoT sensor analytics scenario for predictive maintenance and integration with real time monitoring systems. A live demo shows how to embed and deploy Machine Learning models - built with frameworks like TensorFlow, DeepLearning4J or H2O - into mission-critical and scalable real time applications.
GCP for Apache Kafka® Users: Stream Ingestion and Processingconfluent
Watch this talk here: https://www.confluent.io/online-talks/gcp-for-apache-kafka-users-stream-ingestion-processing
In private and public clouds, stream analytics commonly means stateless processing systems organized around Apache Kafka® or a similar distributed log service. GCP took a somewhat different tack, with Cloud Pub/Sub, Dataflow, and BigQuery, distributing the responsibility for processing among ingestion, processing and database technologies.
We compare the two approaches to data integration and show how Dataflow allows you to join and transform and deliver data streams among on-prem and cloud Apache Kafka clusters, Cloud Pub/Sub topics and a variety of databases. The session will have a mix of architectural discussions and practical code reviews of Dataflow-based pipelines.
New Features in Confluent Platform 6.0 / Apache Kafka 2.6Kai Wähner
New Features in Confluent Platform 6.0 / Apache Kafka 2.6, including REST Proxy and API, Tiered Storage for AWS S3 and GCP GCS, Cluster Linking (On-Premise, Edge, Hybrid, Multi-Cloud), Self-Balancing Clusters), ksqlDB.
Advanced Stream Processing with Flink and Pulsar - Pulsar Summit NA 2021 KeynoteStreamNative
In this talk, Till Rohrmann and Addison Higham discuss how Flink allows for ambitious stream processing workflows and how Pulsar and Flink enable new capabilities that push forward the state-of-the-art in streaming. They will also share upcoming features and new capabilities in the integrations between Flink and Pulsar and how these two communities are working together to truly advance the power of stream processing.
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. Storing such huge event streams into HDFS or a NoSQL datastore is feasible and not such a challenge anymore. But if you want to be able to react fast, with minimal latency, you can not afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics right after you consume the data streams. Products for doing event processing, such as Oracle Event Processing or Esper, are avaialble for quite a long time and used to be called Complex Event Processing (CEP). In the past few years, another family of products appeared, mostly out of the Big Data Technology space, called Stream Processing or Streaming Analytics. These are mostly open source products/frameworks such as Apache Storm, Spark Streaming, Flink, Kafka Streams as well as supporting infrastructures such as Apache Kafka. In this talk I will present the theoretical foundations for Stream Processing, discuss the core properties a Stream Processing platform should provide and highlight what differences you might find between the more traditional CEP and the more modern Stream Processing solutions.
Introducing Dapr.io - the open source personal assistant to microservices and...Lucas Jellema
Dapr.io is an open source product, originated from Microsoft and embraced by a broad coalition of cloud suppliers (part of CNFC) and open source projects. Dapr is a runtime framework that can support any application and that especially shines with distributed applications - for example microservices - that run in containers, spread over clouds and / or edge devices.
With Dapr you give an application a "sidecar" - a kind of personal assistant that takes care of all kinds of common responsibilities. Capturing and retrieving state, publishing and consuming messages or events. Reading secrets and configuration data. Shielding and load balancing over service endpoints. Calling and subscribing to all kinds of SaaS and PaaS facilities. Logging traces across all kinds of application components and logically routing calls between microservices and other application components. Dapr provides generic APIs to the application (HTTP and gRPC) for calling all these generic services – and provides implementations of these APIs for all public clouds and dozens of technology components. This means that your application can easily make use of a wide range of relevant features - with a strict separation between the language the application uses for this (generic, simple) and the configuration of the specific technology (e.g. Redis, MySQL, CosmosDB, Cassandra, PostgreSQL, Oracle Database, MongoDB, Azure SQL etc) that the Dapr sidecar uses. Changing technology does not affect the application, but affects the configuration of the Sidecar. Dapr can be used from applications in any technology - from Java and C#/.NET to Go, Python, Node, Rust and PHP. Or whatever can talk HTTP (or gRPC).
In this Code Café I will introduce you to Dapr.io. I will show you what Dapr can do for you (application) and how you can Dapr-izen an application. I'll show you how an asynchronously collaborative system of microservices - implemented in different technologies - can be easily connected to Dapr, first to Redis as a Pub/Sub mechanism and then also to Apache Kafka without modifications. Then we do - with the interested parties - also a hands-on in which you will apply Dapr yourself . In a short time you get a good feel for how you can use Dapr for different aspects of your applications. And if nothing else, Dapr is a very easy way to get your code with Kafka, S3, Redis, Azure EventGrid, HashiCorp Consul, Twillio, Pulsar, RabbitMQ, HashiCorp Vault, AWS Secret Manager, Azure KeyVault, Cron, SMTP, Twitter, AWS SQS & SNS, GCP Pub/Sub and dozens of other technology components talk.
A guide through the Azure Messaging services - Update ConferenceEldert Grootenboer
https://www.updateconference.net/en/2019/session/a-guide-through-the-azure-messaging-services
A guide through the Azure Messaging services - Update Conference
Spark (Structured) Streaming vs. Kafka Streams - two stream processing platfo...Guido Schmutz
Spark Streaming and Kafka Streams are two popular stream processing platforms. Spark Streaming uses micro-batching and allows for code reuse between batch and streaming jobs. Kafka Streams is embedded directly into Apache Kafka and leverages Kafka as its internal messaging layer. Both platforms support stateful stream processing operations like windowing, aggregations, and joins through distributed state stores. A demo application is shown that detects dangerous driving by joining truck position data with driver data using different streaming techniques.
Dropbox is a free service that automatically syncs and saves files across a user's devices. Any file saved to the Dropbox folder on one device is instantly available on all other linked devices. The Dropbox folder works just like any other folder but syncs file changes in real-time. Users can drag and drop files into their Dropbox folder to upload them or access files from any device.
The document discusses different approaches for describing and deploying applications in the cloud, including declarative and procedural options. It provides examples of AWS CloudFormation templates, OpenStack Heat templates, Apache Whirr specifications, and Brooklyn application blueprints. It also outlines ongoing work to standardize application modeling through the OASIS TOSCA specification.
Deploying and managing docker clusters, and applications in the cloud, and application on managed docker clusters, using Apache Brooklyn and Cloudsoft AMP
Cloud Application Blueprints with Apache Brooklyn by Alex Henevaldbuildacloud
So you have your cloud running, what now? Extend the devops agility from infrastructure to applications by learning how to use Brooklyn, the Apache-incubating project for application management. Create blueprints for applications to enable one-click deployment into Cloudstack, Docker, localhost, or other targets. Leverage your favourite server management tools, from Bash to Chef. Automatically change the deployment after it's deployed. Attach policies to support scaling, failover, and alerting in the way your application needs.
In this session we'll show how with just a few lines of YAML, you can build powerful application blueprints by composing pre-existing components, from polyglot web stacks to big data tools such as Riak. We'll also cover defining new blueprints using custom scripts, configuring machine selection and runtime policies, and managing new locations such as Clocker -- the cloud of docker.
About Alex Henevald
Alex brings twenty years experience designing software solutions in the enterprise, start-up, and academic sectors. Most recently Alex was with Enigmatec Corporation where he led the development of what is now the Monterey® Middleware Platform™. Previous to that, he founded PocketWatch Systems, commercialising results from his doctoral research. Alex holds a PhD (Informatics) and an MSc (Cognitive Science) from the University of Edinburgh and an AB (Mathematics) from Princeton University. Alex was both a USA Today Academic All-Star and a Marshall Scholar.
La Unión Europea ha acordado un embargo petrolero contra Rusia en respuesta a la invasión de Ucrania. El embargo prohibirá las importaciones marítimas de petróleo ruso a la UE y pondrá fin a las entregas a través de oleoductos dentro de seis meses. Esta medida forma parte de un sexto paquete de sanciones de la UE destinadas a aumentar la presión económica sobre Moscú y privar al Kremlin de fondos para financiar su guerra.
Un dominio es un nombre alfanumérico único que identifica sitios web y servidores en Internet en lugar de direcciones numéricas. Los controladores de dominio almacenan información de usuarios y computadoras en una red y distribuyen tareas para mejorar el rendimiento. Existen dominios genéricos y regionales con diferentes restricciones de contenido.
Este documento proporciona una introducción a los componentes hardware de una computadora personal. Describe los componentes principales como la CPU, memoria, placa base, tarjetas de video y sonido, almacenamiento como discos duros, y periféricos como pantallas, impresoras y ratones. También discute brevemente la historia del desarrollo del hardware y los principales fabricantes de componentes.
Documentos originales de "La prensa que se vendió"EdicionesCarena
El documento contiene 31 puntos que describen varias comunicaciones entre funcionarios del gobierno español y propietarios de medios de comunicación durante la transición a la democracia. Las comunicaciones discuten temas como la publicación de artículos, quejas sobre la cobertura de medios, solicitudes de ayuda financiera, y reuniones sobre la postura política de diferentes publicaciones. El documento proporciona una mirada interna a las relaciones y la influencia del gobierno en los medios durante este período histórico.
Daniel Pennac fue un mal alumno que se convirtió en profesor para ayudar a otros estudiantes que pasaban por lo mismo. En la entrevista, Pennac describe a los "malos alumnos" como llenos de miedo y falta de confianza, y explica que es importante que los profesores den apoyo y no juzguen a los estudiantes solo por sus notas. También enfatiza que los comentarios de los padres y profesores pueden motivar o desmotivar a los estudiantes. Por último, Pennac ofrece consejos para los profesores
Visita la página del Diplomado Internacional en Gestión Comercial:
http://www.esan.edu.pe/diplomados/gestion-comercial/
Este diplomado, tiene por objetivo brindar a los participantes los fundamentos y conceptos de de la Gestión Comercial bajo un enfoque internacional, tomando como eje el estudio de los procesos comerciales de cada empresa como son las ventas, la atención al cliente, la definición del mercado objetivo y las finanzas para el área comercial, asimismo este diplomado busca preparar y capacitar a los ejecutivos en la adecuada aplicación de las políticas y estrategias comerciales, conocimientos que podrán ser aplicados en sus empresas con el fin de incrementar su rentabilidad.
Este documento presenta alternativas para apoyar a los jóvenes de Guerrero en México en materia de educación y empleo. Propone programas como "Manos a la obra" y "Jóvenes trabajadores" para capacitar a los jóvenes y ayudarlos a insertarse en el mercado laboral a través de entrenamiento técnico, talleres de habilidades blandas y apoyo para emprender negocios. También enfatiza la importancia de una educación de calidad e inclusiva que permita a los jóvenes desarrollarse pl
La capa de ozono es una fina capa de la atmósfera que nos protege de los rayos ultravioletas del sol. Al contaminar la capa de ozono, se forman grietas que permiten que los rayos UV impacten directamente la superficie causando cáncer de piel y reducción de algas. Para proteger la capa de ozono debemos reducir el uso de combustibles fósiles, utilizar gases no contaminantes y promover la reforestación.
Gijón es una ciudad costera del norte de España ubicada en el Principado de Asturias. Es la capital y ciudad más grande de Asturias, con una población de más de 270,000 habitantes. La ciudad tiene una larga historia que se remonta a la época romana y es conocida por su patrimonio arquitectónico, cultura y playas.
A empresa de tecnologia anunciou um novo smartphone com câmera aprimorada, maior tela e melhor processador. O novo aparelho custará US$ 100 a mais que o modelo anterior e estará disponível para pré-venda em 1 mês. Analistas esperam que o novo smartphone ajude a empresa a aumentar suas vendas e receita no próximo trimestre.
Este documento describe varios dispositivos multimedia como computadoras, monitores, escáneres, impresoras, proyectores, cámaras, memorias USB, discos externos, reproductores MP3, iPhone, Bluetooth, cámaras web y digitales, y reproductores de audio y video. Explica los diferentes tipos de cada dispositivo y sus características principales. También identifica los puertos y cables de conexión más comunes como USB, Firewire, VGA, RCA y sus usos.
Padre José Rached, recibe los Reyes Magos dic 26 2013, en Guayama, Puerto Ri...Sebastian Gomez
VISITA DE LOS REYES MAGOS, A LA PARROQUIA SAN ANTONIO DE PADUA DE GUAYAMA EL 26 DE DICIEMBRE DE 2013. FUE UNA EXPERIENCIA DE MUCHA ALEGRÍA PARA TODOS LOS PRESENTES.
Serverless APIs, the Good, the Bad and the Ugly (2019-09-19)Paco de la Cruz
This document outlines Paco de la Cruz's presentation on serverless APIs. The presentation covers an introduction to serverless computing and functions as a service (FaaS), major serverless platforms like AWS Lambda, Azure Functions, and Google Cloud Functions, use cases for serverless, benefits and downsides, strategies for mitigating cold starts, and demos of building serverless applications on Azure Functions.
This document discusses RAD Server, a back-end platform from Embarcadero Technologies for building multi-tier applications with Delphi and C++Builder. RAD Server provides automated REST/JSON API publishing of server-side Delphi and C++ code. It also includes integration middleware, built-in application services, and tools for managing APIs, users and analytics. RAD Server allows developers to quickly develop and deploy modern multi-tier applications with Delphi and C++. Pricing options are provided on a per user or unlimited user basis.
Spring Boot & Spring Cloud on Pivotal Application Service - Alexandre RomanVMware Tanzu
- The document discusses how Pivotal Cloud Foundry (PCF) helps developers run Spring applications at scale through features like the Java Buildpack, Spring deployment profiles, Spring Cloud Connector, and Spring Cloud Services for service discovery, configuration, and circuit breaking.
- It also outlines the ecosystem of services on PCF for Spring apps, including Pivotal Cloud Cache, MySQL for PCF, RabbitMQ for PCF, and Redis for PCF.
- The presentation concludes with a demo of pushing a Spring Boot app to PCF, observing logs, binding services, and using Spring Cloud features.
Resilient Microservices with Spring CloudVMware Tanzu
This document discusses building resilient microservices with Spring Cloud. It defines criteria for cloud native, cloud resilient, cloud friendly, and cloud ready applications. It also describes Spring Cloud features for service discovery, circuit breakers, and gateway routing that help meet these criteria. The presentation includes demos of Spring Cloud service discovery and circuit breakers, and a Kubernetes demo using Spring Cloud Kubernetes.
microXchg 2019, Berlin: Talk by Mario-Leander Reimer (@LeanderReimer, Principal Software Architect at QAware)
=== Please download slides if blurred! ===
Abstract: Only a few years ago the move towards microservice architecture was the first big disruption in software engineering: instead of running monoliths, systems were now build, composed and run as autonomous services. But this came at the price of added development and infrastructure complexity. Serverless and FaaS seem to be the next disruption, they are the logical evolution trying to address some of the inherent technology complexity we are currently faced when building cloud native apps.
FaaS frameworks are currently popping up like mushrooms: Knative, Kubeless, OpenFn, Fission, OpenFaas or Open Whisk are just a few to name. But which one of these is safe to pick and use in your next project? Let's find out. This session will start off by briefly explaining the essence of Serverless application architecture. We will then define a criteria catalog for FaaS frameworks and continue by comparing and showcasing the most promising ones.
The Enterprise Service Bus is Dead! Long live the Enterprise Service Bus, Rim...confluent
The document discusses how Heroku leveraged Apache Kafka to realize the vision of an enterprise service bus (ESB). It defines what an ESB is according to analysts and vendors. Heroku defined the API as its ESB but faced bottlenecks and reliability issues. It transitioned to using Kafka with a pull-based architecture for independent development, scalability, and avoiding single points of failure. Heroku now uses Kafka for operational data pipelines and metrics aggregation. It provides examples of using Kafka topics and discusses next steps of implementing a schema registry and security.
Bring Service Mesh To Cloud Native-appsThang Chung
The presentation shows out what is Service Mesh, how is it work, and important concepts what is cloud-native apps. The event organized at Hanoi Oct 2018.
Explore Advanced CA Release Automation Configuration TopicsCA Technologies
In this session, we will cover configuring SSL/TLS communications within your environment, integrating with Microsoft Active Directory® via LDAP/LDAPS and review the usage of user roles and permissions. We will also cover how to manage deployments using REST, complex architects, security, communications, scalability and troubleshooting.
For more information, please visit http://cainc.to/Nv2VOe
Serverless Computing 2019, November 2019, London: Talk by Mario-Leander Reimer (@LeanderReimer, Principal Software Architect at QAware)
=== Please download slides if blurred! ===
Abstract: Not long ago, the advent of microservice architectures was a big disruption in software engineering: systems were now build, composed and run as autonomous services. But this came at the price of added complexity. Serverless and FaaS seem to be the next disruption, they are a logical evolution addressing the inherent technology complexity we are faced when building cloud native applications.
FaaS frameworks and platforms are currently popping up like mushrooms: Knative, OpenFaaS, Fission or Nuclio are just a few to name. But which one of these is safe to pick and use in your next project? And is it an all or nothing decision or is it suitable to build hybrid architectures? Let’s find out.
Pivoting Spring XD to Spring Cloud Data Flow with Sabby AnandanPivotalOpenSourceHub
Pivoting Spring XD to Spring Cloud Data Flow: A microservice based architecture for stream processing
Microservice based architectures are not just for distributed web applications! They are also a powerful approach for creating distributed stream processing applications. Spring Cloud Data Flow enables you to create and orchestrate standalone executable applications that communicate over messaging middleware such as Kafka and RabbitMQ that when run together, form a distributed stream processing application. This allows you to scale, version and operationalize stream processing applications following microservice based patterns and practices on a variety of runtime platforms such as Cloud Foundry, Apache YARN and others.
About Sabby Anandan
Sabby Anandan is a Product Manager at Pivotal. Sabby is focused on building products that eliminate the barriers between application development, cloud, and big data.
Presentation on Presto (http://prestodb.io) basics, design and Teradata's open source involvement. Presented on Sept 24th 2015 by Wojciech Biela and Łukasz Osipiuk at the #20 Warsaw Hadoop User Group meetup http://www.meetup.com/warsaw-hug/events/224872317
今までデスクトップアプリや Web アプリケーションだった社内システムの開発は、クラウド化と働き方改革という二つのキーワードと共に現場の情シスを悩ませてきました。
今や社内業務アプリにも、場所を問わずアクセスできるようなスマートフォンやタブレット対応のアーキテクチャが求められる時代です。
本セッションでは、そのようなモダンなエンタープライズ向け社内業務アプリを API バックエンドで開発する方法と、その開発現場で戦い続ける情シスの声、そして開発を加速する Azure の様々な API 向けサービスの活用方法を解説します。
RUCK 2017 R에 날개 달기 - Microsoft R과 클라우드 머신러닝 소개r-kor
Microsoft R can be used with Spark to perform advanced analytics on big data in the cloud or on-premises. Key features include the ability to choose between Spark and other compute contexts, easily deploy analytic models as web services, and process data at scale on HDInsight clusters with hundreds of nodes. R enables building end-to-end AI solutions from data preparation and modeling to operationalizing models for production using services like SQL Server, HDInsight, and Azure.
Serverless Kafka and Spark in a Multi-Cloud Lakehouse ArchitectureKai Wähner
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.
Slides from the May 20th workshop at the Seattle Node.js Meetup presented by Shubhra Kar titled: "Develop, Deploy, Monitor and Hyper-scale REST APIs Built in Node.js"
Using Databases and Containers From Development to DeploymentAerospike, Inc.
This document discusses using containers and databases together from development to production. It addresses challenges like data redundancy, dynamic cluster formation and healing when containers start and stop. It proposes that existing architectures are broken and presents Aerospike as a solution, being self-organizing, self-healing and optimized for flash storage. It demonstrates building an app with Python, Aerospike and Docker, deploying to a Swarm cluster, and scaling the database and web tiers through containers.
This document provides an overview of WSO2's cloud strategy, including their platform as a service (PaaS) offerings. It discusses the current state of cloud computing and introduces WSO2 Cloud, which includes their public cloud, managed cloud, and PaaS offerings like Apache Stratos and Kubernetes. It provides details on core PaaS features, WSO2's Docker images, and how their products can be deployed on Kubernetes to provide scalability, high availability, and multi-region support.
Cloud is a style of computing where scalable and elastic IT-related capabilities are provided as a service using Internet technologies. WSO2 delivers one of the best Public Cloud, Managed Cloud and Private Cloud offerings with world renowned WSO2 middleware platform. WSO2 middleware stack is built from ground up with an open architecture for supporting cloud native features such as multi-tenancy, cluster discovery, artifact distribution, dynamic load balancing, autoscaling & monitoring to be able to run on any PaaS. WSO2 is now innovating on delivering a lightweight, ultra fast Gateway and a Microservices Framework for providing unprecedented agility and scalability in the cloud with Docker and Kubernetes.
In this session Imesh will walk you through WSO2 Cloud strategy on delivering heterogeneous PaaS offerings, managed and public cloud platforms for building on-premise, public and hybrid cloud solutions.
Similar to 2015-11-cloudsoft-basho-brooklyn-riak (20)
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
2015-11-cloudsoft-basho-brooklyn-riak
1. Managing the Basho Data Platform with the Cloudsoft UX
RICON Nov 2015
Alex Heneveld @ahtweetin
Mike Zaccardo @ItsMeMikeZ
Cuyler Jones (Basho)
@cuylerjones
2. @ahtweetin
Recap from 2014
Riak Blueprints in Apache Brooklyn
The easiest way to deploy and manage Riak anywhere!
Blueprints for Riak nodes and clusters
available off-the-shelf in Apache Brooklyn
Node and cluster metrics & auto-scaling policies
Supporting AWS, Azure, Openstack, GCE, Softlayer,
Docker, VMware, Virtustream, Bluebox, and more
3. @ahtweetin
What is a Brooklyn Blueprint?
location: aws-ec2
services:
- type: riak-cluster
initialSize: 5
4. @ahtweetin
What is a Brooklyn Blueprint?
location:
byon:
hosts: [ 10.11.12.{101-105} ]
services:
- type: riak-cluster
initialSize: 5
12. @ahtweetin
New for 2015 (and 2016)
Bring your own data tools blueprints
Blueprints for the Basho Data Platform
Consistent across many infrastructures,
now including Docker and Mesos
Riak Enterprise support in Cloudsoft AMP
13. @ahtweetin
The Basho Data Platform
BDP: Combines Riak with other analytics tools to give a simpler solution to a
much larger class of analysis challenges, including Riak, Cache-Proxy, and Spark
Coming soon: Riak TS and range scan queries
14. @ahtweetin
The Basho Data Platform
Redis and Cache-Proxy: Enables natural use of Redis as a read cache for Riak KV
Coming soon: Write-through support for SET, DEL, and PEXPIRE to invalidate cache
BDP: Combines Riak with other analytics tools to give a simpler solution to a
much larger class of analysis challenges, including Riak, Cache-Proxy, and Spark
Coming soon: Riak TS and range scan queries
15. @ahtweetin
The Basho Data Platform
Redis and Cache-Proxy: Enables natural use of Redis as a read cache for Riak KV
Coming soon: Write-through support for SET, DEL, and PEXPIRE to invalidate cache
Spark: Streaming optimization for Riak KV, with 1000 key-object pairs per request.
BDP Leader Election Service allowing HA without ZK, simplifying cluster operation.
BDP: Combines Riak with other analytics tools to give a simpler solution to a
much larger class of analysis challenges, including Riak, Cache-Proxy, and Spark
Coming soon: Riak TS and range scan queries
17. @ahtweetin
The Basho Data Platform
BDP typically runs with a secondary Riak cluster, alongside a primary Riak cluster.
After installing BDP on nodes, start services by running:
data-platform-admin start-service »node« »group« »service«
18. @ahtweetin
The Basho Data Platform
BDP typically runs with a secondary Riak cluster, alongside a primary Riak cluster.
After installing BDP on nodes, start services by running:
data-platform-admin start-service »node« »group« »service«
Considerations for Spark
Spark workers should be run on different nodes from Riak to avoid interference.
Start with a one-to-one correspondence between Riak nodes and Spark nodes.
Follow general Spark provisioning guidelines for Disk, Memory, CPU, and Network.
Considerations for Redis and Cache-Proxy
Redis should be run on different nodes from Riak and Spark to avoid interference.
Cache Proxy and Redis should be run together to create a mesh of cache servers.
Set CACHE_TTL to meet the Eventual Consistency SLA of the application.
Monitor usage/allocation via Redis INFO (used_memory and maxmemory).
35. @ahtweetin
The BDP and Cloudsoft AMP
One-click deployment of BDP, following best practices
Scale each sub-system, manually or automatically
Consistency across bare metal, many clouds, containers, and more
Handle replication, migration and DR
Blueprints extensible for the systems you need today
and the flexibility and agility you will need in the future