Flink Forward 2019
StreamPipes is an open source self-service IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams
https://streampipes.apache.org/
ApacheCon @Home 2020
StreamPipes is an open source self-service IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams.
https://streampipes.apache.org/
ApacheCon North America 2019
StreamPipes is an open source self-service IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams
https://streampipes.apache.org/
INTERFACE, by apidays - Apache Cassandra now speaks developer with Stargate ...apidays
INTERFACE, by apidays 2021 - It’s APIs all the way down
June 30, July 1 & 2, 2021
Apache Cassandra now speaks developer with Stargate: Rethinking database APIs
Ash Ryan Arnwine, Developer Experience Architect at Datastax
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Automated Apache Kafka Mocking and Testing with AsyncAPI | Hugo Guerrero, Red...HostedbyConfluent
Apache Kafka is getting used as an event backbone in new organizations every day. We would love to send every byte of data through the event bus. Like traditional REST APIs, a contract-first approach is very useful when designing event-driven architectures. In the case of asynchronous APIs, we have the AsyncAPI specification to document the endpoints where the schema of the records become the main part of the contract payload. Microcks allows us to deploy a testing and mocking platform to have a unified view of the endpoints to speed-up application delivery.
In this session we will:
- Go over the evolution of API specifications
- Review the approach for contract-first design with Apache Kafka
- Introduce the AsyncAPI specification
- Take an overview of an implementation example for automated mocking and testing
Speed-Up Kafka Delivery with AsyncAPI & Microcks | Hugo Guerrero, Red HatHostedbyConfluent
Apache Kafka is getting used as an event backbone in new organizations everyday. We would love to send every byte of data through the event bus. Like traditional REST APIs a contract-first approach is very useful when designing event-driven architectures. In the case of asynchronous APIs, we have the AsynAPI specification to document the endpoints where the schema of the records become the main part of the contract payload. Microcks allows us to deploy a testing and mocking platform to have a unified view of the endpoints to speed-up application delivery.
Apache Kafka for Smart Grid, Utilities and Energy ProductionKai Wähner
The energy industry is changing from system-centric to smaller-scale and distributed smart grids and microgrids. A smart grid requires a flexible, scalable, elastic, and reliable cloud-native infrastructure for real-time data integration and processing. This post explores use cases, architectures, and real-world deployments of event streaming with Apache Kafka in the energy industry to implement smart grids and real-time end-to-end integration.
Blog Post with more details:
https://www.kai-waehner.de/apache-kafka-smart-grid-energy-production-edge-iot-oil-gas-green-renewable-sensor-analytics
Should we manage events like APIs? | Kim Clark, IBMHostedbyConfluent
APIs have become ubiquitous as a way of exposing the capabilities of the enterprise both internally and externally. However, are APIs alone enough? There is a strong resurgence in interest in asynchronous communication and event driven architecture. Applications want to receive events immediately so they can respond in real time, and furthermore they also want the benefit of being decoupled from the availability and performance characteristics of the systems providing that data. However, whilst the way that APIs are socialized, exposed, versioned etc. is well matured in the form of API management technology. We are now on the cusp of seeing first class support for event endpoint management to provide the same sophistication for discovering, exposing and consuming events.
ApacheCon @Home 2020
StreamPipes is an open source self-service IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams.
https://streampipes.apache.org/
ApacheCon North America 2019
StreamPipes is an open source self-service IoT toolbox to enable non-technical users to connect, analyze and explore IoT data streams
https://streampipes.apache.org/
INTERFACE, by apidays - Apache Cassandra now speaks developer with Stargate ...apidays
INTERFACE, by apidays 2021 - It’s APIs all the way down
June 30, July 1 & 2, 2021
Apache Cassandra now speaks developer with Stargate: Rethinking database APIs
Ash Ryan Arnwine, Developer Experience Architect at Datastax
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Automated Apache Kafka Mocking and Testing with AsyncAPI | Hugo Guerrero, Red...HostedbyConfluent
Apache Kafka is getting used as an event backbone in new organizations every day. We would love to send every byte of data through the event bus. Like traditional REST APIs, a contract-first approach is very useful when designing event-driven architectures. In the case of asynchronous APIs, we have the AsyncAPI specification to document the endpoints where the schema of the records become the main part of the contract payload. Microcks allows us to deploy a testing and mocking platform to have a unified view of the endpoints to speed-up application delivery.
In this session we will:
- Go over the evolution of API specifications
- Review the approach for contract-first design with Apache Kafka
- Introduce the AsyncAPI specification
- Take an overview of an implementation example for automated mocking and testing
Speed-Up Kafka Delivery with AsyncAPI & Microcks | Hugo Guerrero, Red HatHostedbyConfluent
Apache Kafka is getting used as an event backbone in new organizations everyday. We would love to send every byte of data through the event bus. Like traditional REST APIs a contract-first approach is very useful when designing event-driven architectures. In the case of asynchronous APIs, we have the AsynAPI specification to document the endpoints where the schema of the records become the main part of the contract payload. Microcks allows us to deploy a testing and mocking platform to have a unified view of the endpoints to speed-up application delivery.
Apache Kafka for Smart Grid, Utilities and Energy ProductionKai Wähner
The energy industry is changing from system-centric to smaller-scale and distributed smart grids and microgrids. A smart grid requires a flexible, scalable, elastic, and reliable cloud-native infrastructure for real-time data integration and processing. This post explores use cases, architectures, and real-world deployments of event streaming with Apache Kafka in the energy industry to implement smart grids and real-time end-to-end integration.
Blog Post with more details:
https://www.kai-waehner.de/apache-kafka-smart-grid-energy-production-edge-iot-oil-gas-green-renewable-sensor-analytics
Should we manage events like APIs? | Kim Clark, IBMHostedbyConfluent
APIs have become ubiquitous as a way of exposing the capabilities of the enterprise both internally and externally. However, are APIs alone enough? There is a strong resurgence in interest in asynchronous communication and event driven architecture. Applications want to receive events immediately so they can respond in real time, and furthermore they also want the benefit of being decoupled from the availability and performance characteristics of the systems providing that data. However, whilst the way that APIs are socialized, exposed, versioned etc. is well matured in the form of API management technology. We are now on the cusp of seeing first class support for event endpoint management to provide the same sophistication for discovering, exposing and consuming events.
INTERFACE, by apidays - C* made easy with Stargate APIs by Kirsten Hunter, D...apidays
INTERFACE, by apidays 2021 - It’s APIs all the way down
June 30, July 1 & 2, 2021
C* made easy with Stargate APIs
Kirsten Hunter, Author of "Irrestisible APIs", Developer Advocate at DataStax
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
Combining logs, metrics, and traces for unified observabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Apache Kafka in Financial Services - Use Cases and ArchitecturesKai Wähner
The Rise of Event Streaming in Financial Services - Use Cases, Architectures and Examples powered by Apache Kafka.
The New FinServ Enterprise Reality: Every company is a software company. Innovate OR be Disrupted. Learn how Event Streaming with Apache Kafka and its ecosystem help...
More details:
https://www.kai-waehner.de/apache-kafka-financial-services-industry-banking-finserv-payment-fraud-middleware-messaging-transactions
https://www.kai-waehner.de/blog/2020/04/15/apache-kafka-machine-learning-banking-finance-industry/
https://www.kai-waehner.de/blog/2020/04/24/mainframe-offloading-replacement-apache-kafka-connect-ibm-db2-mq-cdc-cobol/
The Rise Of Event Streaming – Why Apache Kafka Changes EverythingKai Wähner
Business digitalization trends like microservices, the Internet of Things or Machine Learning are driving the need to process events at a whole new scale, speed and efficiency. Traditional solutions like ETL/data integration or messaging are not build to serve these needs.
Today, the open source project Apache Kafka® is being used by thousands of companies including over 60% of the Fortune 100 to power and innovate their businesses by focusing their data strategies around event-driven architectures leveraging event streaming.We will discuss the market and technology changes that have given rise to Kafka and to Event Streaming, and we will introduce the audience to the key aspects of building an Event streaming platform with Kafka. Examples of productive use cases from the automotive, manufacturing and transportation sector will showcase the power of event streaming.
Event: https://www.meetup.com/de-DE/Vienna-Kafka-meetup/events/262314643/
Speaker: Patrik Kleindl (patrik.kleindl@bearingpoint.com)
Slides of the introduction to Apache Kafka and some popular use cases.
Slides were provided by Confluent (confluent.io)
PaNDA - a platform for Network Data Analytics: an overviewCisco DevNet
A session in the DevNet Zone at Cisco Live, Berlin. PaNDA is a platform for data aggregation and distribution which can be used for data analytics applications being developed at Cisco. PaNDA was incubated in Intercloud and is now being further developed for the Virtual Managed Services (VMS) solution and other Cisco solutions. The session will details why we need a platform for OSS analytics and then how we tackle this point.
IBM Cloud Pak for Integration with Confluent Platform powered by Apache KafkaKai Wähner
The Rise of Data in Motion powered by Event Streaming - Use Cases and Architecture for IBM Cloud Pak with Confluent Platform. Including screenshots of the live demo (integration between IBM and Kafka via Confluent Platform and Kafka Connect connectors).
Learn about the integration capabilities of IBM Cloud Pak for Integration, now with the industry’s leading event streaming platform from Confluent Platform powered by Apache Kafka.
Combinação de logs, métricas e rastreamentos para observabilidade unificadaElasticsearch
Saiba como o Elasticsearch combina com eficiência dados em um único armazenamento e como o Kibana é usado para analisá-los. Além disso, veja como os desenvolvimentos recentes ajudam a identificar e resolver problemas operacionais mais rapidamente.
Rethinking Geo-replication for the Cloud | Luke Knepper, ConfluentHostedbyConfluent
Geo-replication is an old problem that comes in many flavors: multi-region, multi-cloud, hybrid cloud, disaster recovery, and more. At Confluent, we’ve learned from our customers’ experiences executing these strategies over the years. Come hear how these learnings inspired our new product, Cluster Linking, to make geo-replication simple, consistent, and cloud-native.
Apache Kafka for Cybersecurity and SIEM / SOAR ModernizationKai Wähner
Data in Motion powered by the Apache Kafka ecosystem for Situational Awareness, Threat Detection, Forensics, Zero Trust Zones and Air-Gapped Environments.
Agenda:
1) Cybersecurity in 202X
2) Data in Motion as Cybersecurity Backbone
3) Situational Awareness
4) Threat Intelligence
5) Forensics
6) Air-Gapped and Zero Trust Environments
7) SIEM / SOAR Modernization
More details in the "Kafka for Cybersecurity" blog series:
https://www.kai-waehner.de/blog/2021/07/02/kafka-cybersecurity-siem-soar-part-1-of-6-data-in-motion-as-backbone/
Conclusion Code Cafe - Microcks for Mocking and Testing Async APIs (January 2...Lucas Jellema
Microcks is a tool for API Mocking and Testing. In this presentation an overview of the support in Microcks for asynchronous APIs - the event publishing and consuming behavior of services and applications
Application performance monitoring with Elastic APM and the ELK stackAlain Lompo
The recent technological improvements have make it really difficult to properly monitor application performances. Luckily great tools such as Elastic APM are taking care of the problem efficiently. Learn in this presentation how to diagnose, analyse and monitor your applications using Elastic APM.
R, Spark, Tensorflow, H20.ai Applied to Streaming AnalyticsKai Wähner
Slides from my talk at Codemotion Rome in March 2017. Development of analytic machine learning / deep learning models with R, Apache Spark ML, Tensorflow, H2O.ai, RapidMinder, KNIME and TIBCO Spotfire. Deployment to real time event processing / stream processing / streaming analytics engines like Apache Spark Streaming, Apache Flink, Kafka Streams, TIBCO StreamBase.
Flink London meetup 3 March 2016 - Flink basicsCyrus New
These are slides presented at our first Flink London meetup (http://www.meetup.com/Apache-Flink-London-Meetup/). They are a non-technical introduction to Flink and highlight some of the motivations for selecting this tool for streaming data processing. See the associated video recording here: https://youtu.be/Hgkmnuj1vUw
Flink for Everyone: Self Service Data Analytics with StreamPipes - Philipp Ze...Flink Forward
This talk presents StreamPipes (https://www.streampipes.org), an open source self-service data analytics solution leveraging existing big data technologies such as Apache Flink to provide non-technical users with an easy and intuitive way to connect, analyze and exploit a variety of different streaming data sources for their use.
Newly arising IoT-driven use cases in domains such as manufacturing, smart city or autonomous driving often demand for continuous integration and processing of sensor data in order to derive time-sensitive actions. One example is the optimization of maintenance processes based on the current condition of machines (condition-based maintenance). While this is technically already well supported by the existing big data tool landscape, building such applications still require a crucial set of expertise ranging from general domain expertise, programming skills to deep knowledge on distributed and scalable systems. Such skills are usually not present in hardware-focused manufacturing companies.
To mitigate these shortcomings, StreamPipes allows non-technical users to leverage a graphical editor to model and deploy analytical tasks as pipelines in a drag and drop manner. Pipelines are built based on a toolbox of reusable data adapters, processors and sinks. Toolbox elements encapsulate dedicated algorithms (e.g., filter, aggregation, machine learning classifiers) implemented in big data processing engines such as Apache Flink communicating over an internal distributed messaging system (e.g. Apache Kafka).
In this talk, we present technologies and tools enabling flexible modeling of real-time processing pipelines by domain experts. We motivate our talk by showing real-world examples we gathered from a number of industry projects during the past years in Industrial IoT domains such as manufacturing and supply chain management. For instance, we show how StreamPipes eases the accessibility of big data tools for non-technical users based on examples such as supervising a fleet of autonomous electric delivery vehicles as well as data analytics in one of the largest test areas for autonomous driving in Germany.
INTERFACE, by apidays - C* made easy with Stargate APIs by Kirsten Hunter, D...apidays
INTERFACE, by apidays 2021 - It’s APIs all the way down
June 30, July 1 & 2, 2021
C* made easy with Stargate APIs
Kirsten Hunter, Author of "Irrestisible APIs", Developer Advocate at DataStax
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
Combining logs, metrics, and traces for unified observabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Apache Kafka in Financial Services - Use Cases and ArchitecturesKai Wähner
The Rise of Event Streaming in Financial Services - Use Cases, Architectures and Examples powered by Apache Kafka.
The New FinServ Enterprise Reality: Every company is a software company. Innovate OR be Disrupted. Learn how Event Streaming with Apache Kafka and its ecosystem help...
More details:
https://www.kai-waehner.de/apache-kafka-financial-services-industry-banking-finserv-payment-fraud-middleware-messaging-transactions
https://www.kai-waehner.de/blog/2020/04/15/apache-kafka-machine-learning-banking-finance-industry/
https://www.kai-waehner.de/blog/2020/04/24/mainframe-offloading-replacement-apache-kafka-connect-ibm-db2-mq-cdc-cobol/
The Rise Of Event Streaming – Why Apache Kafka Changes EverythingKai Wähner
Business digitalization trends like microservices, the Internet of Things or Machine Learning are driving the need to process events at a whole new scale, speed and efficiency. Traditional solutions like ETL/data integration or messaging are not build to serve these needs.
Today, the open source project Apache Kafka® is being used by thousands of companies including over 60% of the Fortune 100 to power and innovate their businesses by focusing their data strategies around event-driven architectures leveraging event streaming.We will discuss the market and technology changes that have given rise to Kafka and to Event Streaming, and we will introduce the audience to the key aspects of building an Event streaming platform with Kafka. Examples of productive use cases from the automotive, manufacturing and transportation sector will showcase the power of event streaming.
Event: https://www.meetup.com/de-DE/Vienna-Kafka-meetup/events/262314643/
Speaker: Patrik Kleindl (patrik.kleindl@bearingpoint.com)
Slides of the introduction to Apache Kafka and some popular use cases.
Slides were provided by Confluent (confluent.io)
PaNDA - a platform for Network Data Analytics: an overviewCisco DevNet
A session in the DevNet Zone at Cisco Live, Berlin. PaNDA is a platform for data aggregation and distribution which can be used for data analytics applications being developed at Cisco. PaNDA was incubated in Intercloud and is now being further developed for the Virtual Managed Services (VMS) solution and other Cisco solutions. The session will details why we need a platform for OSS analytics and then how we tackle this point.
IBM Cloud Pak for Integration with Confluent Platform powered by Apache KafkaKai Wähner
The Rise of Data in Motion powered by Event Streaming - Use Cases and Architecture for IBM Cloud Pak with Confluent Platform. Including screenshots of the live demo (integration between IBM and Kafka via Confluent Platform and Kafka Connect connectors).
Learn about the integration capabilities of IBM Cloud Pak for Integration, now with the industry’s leading event streaming platform from Confluent Platform powered by Apache Kafka.
Combinação de logs, métricas e rastreamentos para observabilidade unificadaElasticsearch
Saiba como o Elasticsearch combina com eficiência dados em um único armazenamento e como o Kibana é usado para analisá-los. Além disso, veja como os desenvolvimentos recentes ajudam a identificar e resolver problemas operacionais mais rapidamente.
Rethinking Geo-replication for the Cloud | Luke Knepper, ConfluentHostedbyConfluent
Geo-replication is an old problem that comes in many flavors: multi-region, multi-cloud, hybrid cloud, disaster recovery, and more. At Confluent, we’ve learned from our customers’ experiences executing these strategies over the years. Come hear how these learnings inspired our new product, Cluster Linking, to make geo-replication simple, consistent, and cloud-native.
Apache Kafka for Cybersecurity and SIEM / SOAR ModernizationKai Wähner
Data in Motion powered by the Apache Kafka ecosystem for Situational Awareness, Threat Detection, Forensics, Zero Trust Zones and Air-Gapped Environments.
Agenda:
1) Cybersecurity in 202X
2) Data in Motion as Cybersecurity Backbone
3) Situational Awareness
4) Threat Intelligence
5) Forensics
6) Air-Gapped and Zero Trust Environments
7) SIEM / SOAR Modernization
More details in the "Kafka for Cybersecurity" blog series:
https://www.kai-waehner.de/blog/2021/07/02/kafka-cybersecurity-siem-soar-part-1-of-6-data-in-motion-as-backbone/
Conclusion Code Cafe - Microcks for Mocking and Testing Async APIs (January 2...Lucas Jellema
Microcks is a tool for API Mocking and Testing. In this presentation an overview of the support in Microcks for asynchronous APIs - the event publishing and consuming behavior of services and applications
Application performance monitoring with Elastic APM and the ELK stackAlain Lompo
The recent technological improvements have make it really difficult to properly monitor application performances. Luckily great tools such as Elastic APM are taking care of the problem efficiently. Learn in this presentation how to diagnose, analyse and monitor your applications using Elastic APM.
R, Spark, Tensorflow, H20.ai Applied to Streaming AnalyticsKai Wähner
Slides from my talk at Codemotion Rome in March 2017. Development of analytic machine learning / deep learning models with R, Apache Spark ML, Tensorflow, H2O.ai, RapidMinder, KNIME and TIBCO Spotfire. Deployment to real time event processing / stream processing / streaming analytics engines like Apache Spark Streaming, Apache Flink, Kafka Streams, TIBCO StreamBase.
Flink London meetup 3 March 2016 - Flink basicsCyrus New
These are slides presented at our first Flink London meetup (http://www.meetup.com/Apache-Flink-London-Meetup/). They are a non-technical introduction to Flink and highlight some of the motivations for selecting this tool for streaming data processing. See the associated video recording here: https://youtu.be/Hgkmnuj1vUw
Flink for Everyone: Self Service Data Analytics with StreamPipes - Philipp Ze...Flink Forward
This talk presents StreamPipes (https://www.streampipes.org), an open source self-service data analytics solution leveraging existing big data technologies such as Apache Flink to provide non-technical users with an easy and intuitive way to connect, analyze and exploit a variety of different streaming data sources for their use.
Newly arising IoT-driven use cases in domains such as manufacturing, smart city or autonomous driving often demand for continuous integration and processing of sensor data in order to derive time-sensitive actions. One example is the optimization of maintenance processes based on the current condition of machines (condition-based maintenance). While this is technically already well supported by the existing big data tool landscape, building such applications still require a crucial set of expertise ranging from general domain expertise, programming skills to deep knowledge on distributed and scalable systems. Such skills are usually not present in hardware-focused manufacturing companies.
To mitigate these shortcomings, StreamPipes allows non-technical users to leverage a graphical editor to model and deploy analytical tasks as pipelines in a drag and drop manner. Pipelines are built based on a toolbox of reusable data adapters, processors and sinks. Toolbox elements encapsulate dedicated algorithms (e.g., filter, aggregation, machine learning classifiers) implemented in big data processing engines such as Apache Flink communicating over an internal distributed messaging system (e.g. Apache Kafka).
In this talk, we present technologies and tools enabling flexible modeling of real-time processing pipelines by domain experts. We motivate our talk by showing real-world examples we gathered from a number of industry projects during the past years in Industrial IoT domains such as manufacturing and supply chain management. For instance, we show how StreamPipes eases the accessibility of big data tools for non-technical users based on examples such as supervising a fleet of autonomous electric delivery vehicles as well as data analytics in one of the largest test areas for autonomous driving in Germany.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
The Enterprise Guide to Building a Data Mesh - Introducing SpecMeshIanFurlong4
For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy.
We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh.
This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.
Data Ingestion in Big Data and IoT platformsGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
Enterprise guide to building a Data MeshSion Smith
Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.
Most data visualization solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualization capabilities. One option is to first persist the data into a data store and then use a traditional data visualization solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualization tools might already integrate with the specific data store. An other option is to use a Streaming Visualization solution. This talk presents different architecture blueprints for integrating data visualization into a fast data solutions.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One option is to first persist the data into a data store and then use a traditional data visualisation solution to present the data. If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solutions and then we show how the different blueprints can be implemented by mapping products onto the blueprints.
Independent of the source of data, the integration and analysis of event streams gets 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.
So far this mostly a development experience, with frameworks such as Oracle Event Processing, Apache Storm or Spark Streaming. With Oracle Stream Analytics, analytics on event streams can be put in the hands of the business analyst. It simplifies the implementation of event processing solutions so that every business analyst is able to graphically and decleratively define event stream processing pipelines, without having to write a single line of code or continous query language (CQL). Event Processing is no longer “complex”! This session presents Oracle Stream Analytics directly on some selected demo use cases.
Most data visualisation solutions today still work on data sources which are stored persistently in a data store, using the so called “data at rest” paradigms. More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. These data stream publish with high velocity and messages often have to be processed as quick as possible. For the processing and analytics on the data, so called stream processing solutions are available. But these only provide minimal or no visualisation capabilities. One was is to first persist the data into a data store and then use a traditional data visualisation solution to present the data.
If latency is not an issue, such a solution might be good enough. An other question is which data store solution is necessary to keep up with the high load on write and read. If it is not an RDBMS but an NoSQL database, then not all traditional visualisation tools might already integrate with the specific data store. An other option is to use a Streaming Visualisation solution. They are specially built for streaming data and often do not support batch data. A much better solution would be to have one tool capable of handling both, batch and streaming data. This talk presents different architecture blueprints for integrating data visualisation into a fast data solution and highlights some of the products available to implement these blueprints.
Stream Processing – Concepts and FrameworksGuido Schmutz
More and more data sources today provide a constant stream of data, from IoT devices to Social Media streams. It is one thing to collect these events in the velocity they arrive, without losing any single message. An Event Hub and a data flow engine can help here. It’s another thing to do some (complex) analytics on the data. There is always the option to first store in a data sink of choice and later analyze it. Storing even a high-volume event stream is feasible and not a challenge anymore. But this adds to the end-to-end latency and it takes minutes if not hours to present results. If you need to react fast, you simply can’t afford to first store the data. You need to do process it directly on the data stream. This is called Stream Processing or Stream Analytics. In this talk I will present the important concepts, a Stream Processing solution should support and then dive into some of the most popular frameworks available on the market and how they compare.
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated.
Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all standard resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner.
Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
Apache Apex brings you the power to quickly build and run big data batch and stream processing applications. But what about visualizing your data in real time as it flows through the Apache Apex applications? Together, we will review Apache Apex, and how it integrates with Apache Hadoop and Apache Kafka to process your big data with streaming computation. Then we will explore the options available to visualize Apex applications metrics and data, including open-source options like REST and PubSub mechanisms in StrAM, as well as features available in the RTS Console like real-time Dashboards and Widgets. We will also look into ways of packaging dashboards inside your Apache Apex applications.
A Journey to Building an Autonomous Streaming Data Platform—Scaling to Trilli...Databricks
We will present the design and evolution of Nvidia's 100% Self-Service Streaming Big-Data Platform (ETL, Analytics, AI Training & Inferencing) powered by Spark and Nvidia GPUs. We will discuss the architecture, major challenges that we faced, and lessons learned along the way. Nvidia's data platform processes 10's of billions of events per day, supporting several Nvidia products like GPU Cloud, GeForce NOW Cloud Gaming, AI Smart Cities, DriveSim for Self Driving cars etc. In this talk, we are going to deep dive on Nvidia's next generation data platform with new custom built frameworks, automation tools, and a monitoring system on top of Spark. Thus empowering our developers to build new Spark-powered applications at the speed of light (SOL) with full self-service unified data flows. We will showcase these new tools : a) Zero-engineering dashboards, b) Out-of-the box Spark Streaming applications with automated schema management, c) Custom Spark Streaming to Elastic search connector with enhanced security, d) GDPR compliant SQL access control and auditing with a new custom token management framework, e) Migration from logstash clusters to Spark Streaming for log parsing, etc. We will discuss how decoupling Data-Platform and Applications helped us achieve the next level of scale, self-service, and, security. Finally, we will demo our Platform's App-Store, where developers can shop for new Apps and deploy them with ease - with automated dashboards, streaming ETL, analytics, monitoring, AI training and inferencing. Extended Description: With structured telemetry events and unstructured logs growing at 1000% rate year-over-year, it is extremely important to handle this scale with strict SLAs and high reliability while maintaining extremely low latency. We will discuss how we handled these scaling & security concerns to solve business requirements. Additionally, we will be open-sourcing some of our custom spark frameworks during the talk.
Speakers: Satish Dandu, Rohit Kulkarni
Gimel and PayPal Notebooks @ TDWI Leadership Summit OrlandoRomit Mehta
This is my presentation at TDWI Leadership Summit. It talks about how products like Gimel, Unified Data Catalog and PayPal Notebooks help improve data scientist productivity and enable machine learning at scale at PayPal.
Similar to Flink for Everyone: Self-Service Data Analytics with StreamPipes (20)
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Large Language Models and the End of ProgrammingMatt Welsh
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AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
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✅No Complicated Setup: Get Up And Running In 2 Minutes
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
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(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
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Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
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Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Introduction to Pygame (Lecture 7 Python Game Development)
Flink for Everyone: Self-Service Data Analytics with StreamPipes
1. Flink for Everyone: Self-Service Data
Analytics with StreamPipes
Patrick Wiener, Philipp Zehnder
Flink Forward Europe 2019, Berlin, 2019-10-08
2. www.streampipes.org | @streampipes | github.com/streampipes
2
"A self-service IoT toolbox to enable non-technical users
to connect, analyze and explore IoT data streams"
What's StreamPipes?
3. www.streampipes.org | @streampipes | github.com/streampipes
3
What's StreamPipes?
Big Data / Edge
InfrastructureExecute
Reusable
algorithm toolbox
Install
Model pipelines
4. www.streampipes.org | @streampipes | github.com/streampipes
About us
4
Dominik Riemer
Senior Research Scientist
Philipp Zehnder
Research Scientist
Patrick Wiener
Research Scientist
FZI Research Center for Information Technology, Karlsruhe, Germany
Stream Processing, Data Management, Machine Learning
Non-profit research center for applied ICT research (250 employees)
Started StreamPipes in 2014, first OSS release 2018
5. www.streampipes.org | @streampipes | github.com/streampipes
Agenda
The need for self-service IoT data analytics1
StreamPipes: Technical Overview
Demo
2
Lessons Learned w/ Flink & Getting Started3
7. www.streampipes.org | @streampipes | github.com/streampipes
Conveyor Belts
Pressure
Oil temperature
Dust particles
Production plans
Environmental Data
Gear box drive
Energy consumption
Telematics
Industrial Internet of Things
Data streams everywhere
8. Continuous Monitoring Situational Awareness
Continuous Data
Harmonization
Flexible data integration
from heterogeneous
sources and monitoring
of current system states
Detect time-critical
situations, e.g., by
means of rules or ML
approaches
Continuous pre-
processing and
transformation of input
streams for third party
systems
Industrial Internet of Things
Typical application scenarios
9. www.streampipes.org | @streampipes | github.com/streampipes
StreamPipes
Open Source framework to easily manage IoT data
Data Access
Data analytics &
harmonization
Data exploration &
exploitation
Generic adapters
Specific adapters
Metadata
Data streams & sets
Pre-processing
Filter/Aggregation
Pattern Detection
ML
Situation detection
Harmonized data sets
Visualizations
Third-party systems
9
29. Demo
Condition monitoring + StreamPipes
Rule-based monitoring of flow rate measurements in a multi tank system
30. Demo
Condition monitoring + StreamPipes
Rule-based monitoring of flow rate measurements in a multi tank system
Flow
Sensor
Aggregate
data
Detect
Leakage
Notify
MQTT
IoTDB
StreamPipes Connect
Calculate
Statistics
32. www.streampipes.org | @streampipes | github.com/streampipes
Potentially huge stream of sensor data needs scalability
Remote Environment eased the implementation of Flink Wrapper
Clean & intuitive Flink API enables fast processor development
Simple setup for development (mini cluster) and deployment
Easy to configure & monitor
Good integration with Apache Kafka
Flink + StreamPipes
Lessons learned
33. www.streampipes.org | @streampipes | github.com/streampipes
How to start
Setting up StreamPipes
Docker-based installation
streampipes.org/en/download
Download installer from Github1
./streampipes start2
Finish installation in browser3
33
34. www.streampipes.org | @streampipes | github.com/streampipes
34
What's next?
Data Access
Data analytics &
harmonization
Data exploration &
exploitation
Metadata recognition
PLC4X
Flink fault tolerance
Python wrapper
AutoML
Historical data
explorer
New features: Current work-in-progress
Infrastructure (Edge / Fog)
35. Let's connect!
…and if you like StreamPipes, star us on Github
streampipes.org
docs.streampipes.org
github.com/streampipes/streampipes
twitter.com/streampipes
feedback@streampipes.org