StreamAnalytix 2.0 is a multi-engine streaming analytics platform that allows users to deploy multiple streaming engines depending on their use case requirements. It features an easy to use drag-and-drop UI, support for predictive analytics, machine learning, and real-time dashboards. The platform provides a level of abstraction that gives customers flexibility in choosing the best streaming engine for their needs.
In many database applications we first log data and then, a few hours or days later, we start analyzing it. But in a world that’s moving faster and faster, we sometimes need to analyze what is happening NOW.
Azure Stream Analytics allows you to analyze streams of data via a new Azure service. In this session you will see how to get started using this new service. From event hubs on the input side over temporal SQL queries: the demo’s in this session will show you end to end how to get started with Azure Stream Analytics.
SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration) Surendar S
Especially this document provide very useful and meaningful concepts about SnapLogic. Also this document will be more useful for beginner/intermediate level SnapLogic learners.
Monitoreo sencillo de la infraestructura, de la ingesta a la visualizaciónElasticsearch
La visibilidad sobre la infraestructura es un elemento esencial, independientemente de que sea en tus propias máquinas o en la nube, virtualizada, en contenedores, o en un entorno híbrido. El Elastic (ELK) Stack, históricamente conocido por sus capacidades de logging, permite también monitorear tus métricas con el mismo rendimiento Descubre cómo facilitamos la ingesta de datos mediante cientos de integraciones prediseñadas, mejoramos tu día a día con alertas y machine learning, y mejoramos tus visualizaciones con nuevas herramientas desarrolladas para los casos de uso de monitoreo.
One Azure Monitor to Rule Them All? - Marius ZahariaITCamp
After winding paths, the different Azure services finally harmonize into a unified monitoring strategy. Focus on Azure Monitor and its features, as well as the modalities of integration between Azure Monitor and complementary blocks, Application Insights, or Log Analytics.
In many database applications we first log data and then, a few hours or days later, we start analyzing it. But in a world that’s moving faster and faster, we sometimes need to analyze what is happening NOW.
Azure Stream Analytics allows you to analyze streams of data via a new Azure service. In this session you will see how to get started using this new service. From event hubs on the input side over temporal SQL queries: the demo’s in this session will show you end to end how to get started with Azure Stream Analytics.
SnapLogic- iPaaS (Elastic Integration Cloud and Data Integration) Surendar S
Especially this document provide very useful and meaningful concepts about SnapLogic. Also this document will be more useful for beginner/intermediate level SnapLogic learners.
Monitoreo sencillo de la infraestructura, de la ingesta a la visualizaciónElasticsearch
La visibilidad sobre la infraestructura es un elemento esencial, independientemente de que sea en tus propias máquinas o en la nube, virtualizada, en contenedores, o en un entorno híbrido. El Elastic (ELK) Stack, históricamente conocido por sus capacidades de logging, permite también monitorear tus métricas con el mismo rendimiento Descubre cómo facilitamos la ingesta de datos mediante cientos de integraciones prediseñadas, mejoramos tu día a día con alertas y machine learning, y mejoramos tus visualizaciones con nuevas herramientas desarrolladas para los casos de uso de monitoreo.
One Azure Monitor to Rule Them All? - Marius ZahariaITCamp
After winding paths, the different Azure services finally harmonize into a unified monitoring strategy. Focus on Azure Monitor and its features, as well as the modalities of integration between Azure Monitor and complementary blocks, Application Insights, or Log Analytics.
Kafka Migration for Satellite Event Streaming Data | Eric Velte, ASRC FederalHostedbyConfluent
ASRC Federal created the Mission Operator Assist (MOA) tool to extend human capabilities through AI/ML for NOAA. MOA ingests system log data from on-orbit satellite constellations and applies machine learning to greatly improve real-time situational awareness. MOA uses a collection of tools, including Kafka for multi-subscriber communications, all hosted through AWS Cloud Services and Kubernetes Containers for microservices. Like many traditional on-premises systems, satellite ground station operations are undergoing a renaissance as they increasingly become enabled by cloud.
During this session, the audience will learn about the satellite communications chain, and best practices and lessons learned in creating a data pipeline with Kafka for high throughput and scalability while displaying high quality situational awareness to mission operators. We will discuss our goals centered around establishing event-driven streaming for satellite logs so our machine learning becomes real-time and supporting a multi-subscriber approach for various Kafka topics. Listeners will also learn how a multi-subscriber approach using Kafka, helped us auto scale logstash based on how many messages are in the queue and other microservices.
Elastic APM : développez vos logs et vos indicateurs pour obtenir une vue com...Elasticsearch
Pour les organisations modernes, les applications sont souvent l'interface client principale, et influencent directement les résultats tels que le chiffre d'affaires et la fidélisation de la clientèle. Quelle que soit votre progression dans votre parcours vers les solutions cloud natives, Elastic APM peut vous aider à améliorer les expériences clients en détectant plus tôt les goulets d'étranglement des performances et en identifiant plus rapidement les régressions à partir des nouveaux déploiements. Découvrez comment obtenir une vue complète des services qui alimentent vos applications, du front-end au back-end, pour garantir un fonctionnement optimal.
From Legacy SQL Server to High Powered Confluent & Kafka Monitoring System at...HostedbyConfluent
In renewable energy, like many other businesses, customers have come to expect real time data feeding their applications, products, and services. And internally, businesses need real time data to facilitate how we monitor our products proactively, reduce customer support costs, and provide customers with features they didn’t previously have access to. But traditional, legacy databases can’t handle the real-time requirements nor scale up to handle increasing amounts of data, and cloud monoliths and tightly-coupled systems prevent building the desired features. At SunPower, we set out to improve our cloud-based platform using Confluent and Kafka to increase the velocity of product development and unlock new features for our customers. In this session, we will share our journey to build a real-time monitoring platform based on Confluent and Kafka and how we’ve been able to improve customer satisfaction ratings and boost referral-based sales as a result.
Keynote : évolution et vision d'Elastic ObservabilityElasticsearch
Elastic Observability aide les organisations à faire tendre vers zéro le temps moyen de résolution avec une visibilité complète de toutes les opérations technologiques sur une seule plateforme. Découvrez les dernières fonctionnalités et capacités à tous les niveaux, de l'ingestion aux données, tandis que les leaders de produits qui conçoivent Elastic Observability lèvent le voile sur son avenir.
Le monitoring d'infrastructure de l'ingestion aux données : un jeu d'enfants !Elasticsearch
La visibilité de votre infrastructure est essentielle, que ce soit sur le site ou dans le cloud, et qu'elle soit virtualisée, conteneurisée, ou basée sur un mélange hybride. La Suite Elastic (ELK), bien connue pour ses fonctionnalités de logging, a évolué pour intégrer un grand nombre de ces atouts à votre cas d'utilisation des indicateurs. Découvrez comment l'intégration simplifiée des données avec des centaines d'intégrations prédéfinies, l'automatisation des informations avec l'alerting et le machine learning, et les nouveaux outils visuels conçus pour explorer les indicateurs d'infrastructure permettent de rationaliser le cas d'utilisation de monitoring à l'échelle globale.
Fast Data – Fast Cars: Wie Apache Kafka die Datenwelt revolutioniertconfluent
Für die Automobilindustrie ist die digitale Transformation wie für jede andere Branche zugleich eine digitale Revolution: Neue Marktspieler, neue Technologien und die in immer größeren Mengen anfallenden Daten schaffen neue Chancen, aber auch neue Herausforderungen – und erfordern neben neuen IT-Architekturen auch völlig neue Denkansätze.
60% der Fortune500-Unternehmen setzen zur Umsetzung ihrer Daten-Streaming-Projekte auf die umfassende verteilte Streaming-Plattform Apache Kafka®, darunter auch die AUDI AG.
Erfahren Sie in diesem Webinar:
Wie Kafka als Grundlage sowohl für Daten-Pipelines als auch für Anwendungen dient, die Echtzeit-Datenströme konsumieren und verarbeiten.
Wie Kafka Connect und Kafka Streams geschäftskritische Anwendungen unterstützt
Wie Audi mithilfe von Kafka und Confluent eine Fast Data IoT-Plattform umgesetzt hat, die den Bereich „Connected Car“ revolutioniert
Sprecher:
David Schmitz, Principal Architect, Audi Electronics Venture GmbH
Kai Waehner, Technology Evangelist, Confluent
Big Data Expo 2015 - Microsoft Transform you data into intelligent actionBigDataExpo
Er zijn veel beloftes rondom Big Data. Iedereen praat erover maar hoe begin je zonder meteen een grote business case op te moeten stellen. Cortana Analytics Suite is laagdrempelig en een makkelijk toegankelijk Advanced Analytics platform om je ideeën op haalbaarheid te testen maar daarna ook door te groeien naar (grote) productie implementaties. In deze sessie krijg je een overzicht van de scenario’s die Cortana Analytics biedt. Denk daar bij aan IOT, Machine Learning maar ook Churn Analysis, Forecasting en Predictive Maintenance.
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
Full Stack Monitoring with Azure MonitorKnoldus Inc.
The full-stack monitoring solutions within Azure Monitor is a boon for DevOps & SRE professionals as they can achieve complete observability of all the applications at a centralized location. Be it troubleshooting issues within your application, infrastructure or network, a unified monitoring solution ensures that you can diagnose problems at one place and fix them within
This webinar talks about how Azure Monitor has eased the monitoring of complex modern applications, whether cloud-based or on-premise. It answers questions like -
~ How to quickly detect and diagnose issues across applications?
~ How to manage infrastructure concerns like those in VMs or containers?
~ How to gain insights from your monitoring data?
~ How to support operations at scale?
#UnifyCloud has participated in #Microsoft_Ignite 2017. #CloudAtlas® automates each phase of the migration (e.g. Assess, Migrate, and #Configuration_Management) lifecycle while ensuring the Security, #Compliance (GRC) and Cost Optimization of your apps and data. http://bit.ly/2auPCzi
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
Building the Next-gen Digital Meter Platform for FluviusDatabricks
Fluvius is the network operator for electricity and gas in Flanders, Belgium. Their goal is to modernize the way people look at energy consumption using a digital meter that captures consumption and injection data from any electrical installation in Flanders ranging from households to large companies. After full roll-out there will be roughly 7 million digital meters active in Flanders collecting up to terabytes of data per day. Combine this with regulation that Fluvius has to maintain a record of these reading for at least 3 years, we are talking petabyte scale. delaware BeLux was assigned by Fluvius to setup a modern data platform and did so on Azure using Databricks as the core component to collect, store, process and serve these volumes of data to every single consumer and beyond in Flanders. This enables the Belgian energy market to innovate and move forward. Maarten took up the role as project manager and solution architect.
Kafka Migration for Satellite Event Streaming Data | Eric Velte, ASRC FederalHostedbyConfluent
ASRC Federal created the Mission Operator Assist (MOA) tool to extend human capabilities through AI/ML for NOAA. MOA ingests system log data from on-orbit satellite constellations and applies machine learning to greatly improve real-time situational awareness. MOA uses a collection of tools, including Kafka for multi-subscriber communications, all hosted through AWS Cloud Services and Kubernetes Containers for microservices. Like many traditional on-premises systems, satellite ground station operations are undergoing a renaissance as they increasingly become enabled by cloud.
During this session, the audience will learn about the satellite communications chain, and best practices and lessons learned in creating a data pipeline with Kafka for high throughput and scalability while displaying high quality situational awareness to mission operators. We will discuss our goals centered around establishing event-driven streaming for satellite logs so our machine learning becomes real-time and supporting a multi-subscriber approach for various Kafka topics. Listeners will also learn how a multi-subscriber approach using Kafka, helped us auto scale logstash based on how many messages are in the queue and other microservices.
Elastic APM : développez vos logs et vos indicateurs pour obtenir une vue com...Elasticsearch
Pour les organisations modernes, les applications sont souvent l'interface client principale, et influencent directement les résultats tels que le chiffre d'affaires et la fidélisation de la clientèle. Quelle que soit votre progression dans votre parcours vers les solutions cloud natives, Elastic APM peut vous aider à améliorer les expériences clients en détectant plus tôt les goulets d'étranglement des performances et en identifiant plus rapidement les régressions à partir des nouveaux déploiements. Découvrez comment obtenir une vue complète des services qui alimentent vos applications, du front-end au back-end, pour garantir un fonctionnement optimal.
From Legacy SQL Server to High Powered Confluent & Kafka Monitoring System at...HostedbyConfluent
In renewable energy, like many other businesses, customers have come to expect real time data feeding their applications, products, and services. And internally, businesses need real time data to facilitate how we monitor our products proactively, reduce customer support costs, and provide customers with features they didn’t previously have access to. But traditional, legacy databases can’t handle the real-time requirements nor scale up to handle increasing amounts of data, and cloud monoliths and tightly-coupled systems prevent building the desired features. At SunPower, we set out to improve our cloud-based platform using Confluent and Kafka to increase the velocity of product development and unlock new features for our customers. In this session, we will share our journey to build a real-time monitoring platform based on Confluent and Kafka and how we’ve been able to improve customer satisfaction ratings and boost referral-based sales as a result.
Keynote : évolution et vision d'Elastic ObservabilityElasticsearch
Elastic Observability aide les organisations à faire tendre vers zéro le temps moyen de résolution avec une visibilité complète de toutes les opérations technologiques sur une seule plateforme. Découvrez les dernières fonctionnalités et capacités à tous les niveaux, de l'ingestion aux données, tandis que les leaders de produits qui conçoivent Elastic Observability lèvent le voile sur son avenir.
Le monitoring d'infrastructure de l'ingestion aux données : un jeu d'enfants !Elasticsearch
La visibilité de votre infrastructure est essentielle, que ce soit sur le site ou dans le cloud, et qu'elle soit virtualisée, conteneurisée, ou basée sur un mélange hybride. La Suite Elastic (ELK), bien connue pour ses fonctionnalités de logging, a évolué pour intégrer un grand nombre de ces atouts à votre cas d'utilisation des indicateurs. Découvrez comment l'intégration simplifiée des données avec des centaines d'intégrations prédéfinies, l'automatisation des informations avec l'alerting et le machine learning, et les nouveaux outils visuels conçus pour explorer les indicateurs d'infrastructure permettent de rationaliser le cas d'utilisation de monitoring à l'échelle globale.
Fast Data – Fast Cars: Wie Apache Kafka die Datenwelt revolutioniertconfluent
Für die Automobilindustrie ist die digitale Transformation wie für jede andere Branche zugleich eine digitale Revolution: Neue Marktspieler, neue Technologien und die in immer größeren Mengen anfallenden Daten schaffen neue Chancen, aber auch neue Herausforderungen – und erfordern neben neuen IT-Architekturen auch völlig neue Denkansätze.
60% der Fortune500-Unternehmen setzen zur Umsetzung ihrer Daten-Streaming-Projekte auf die umfassende verteilte Streaming-Plattform Apache Kafka®, darunter auch die AUDI AG.
Erfahren Sie in diesem Webinar:
Wie Kafka als Grundlage sowohl für Daten-Pipelines als auch für Anwendungen dient, die Echtzeit-Datenströme konsumieren und verarbeiten.
Wie Kafka Connect und Kafka Streams geschäftskritische Anwendungen unterstützt
Wie Audi mithilfe von Kafka und Confluent eine Fast Data IoT-Plattform umgesetzt hat, die den Bereich „Connected Car“ revolutioniert
Sprecher:
David Schmitz, Principal Architect, Audi Electronics Venture GmbH
Kai Waehner, Technology Evangelist, Confluent
Big Data Expo 2015 - Microsoft Transform you data into intelligent actionBigDataExpo
Er zijn veel beloftes rondom Big Data. Iedereen praat erover maar hoe begin je zonder meteen een grote business case op te moeten stellen. Cortana Analytics Suite is laagdrempelig en een makkelijk toegankelijk Advanced Analytics platform om je ideeën op haalbaarheid te testen maar daarna ook door te groeien naar (grote) productie implementaties. In deze sessie krijg je een overzicht van de scenario’s die Cortana Analytics biedt. Denk daar bij aan IOT, Machine Learning maar ook Churn Analysis, Forecasting en Predictive Maintenance.
Kappa vs Lambda Architectures and Technology ComparisonKai Wähner
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
Full Stack Monitoring with Azure MonitorKnoldus Inc.
The full-stack monitoring solutions within Azure Monitor is a boon for DevOps & SRE professionals as they can achieve complete observability of all the applications at a centralized location. Be it troubleshooting issues within your application, infrastructure or network, a unified monitoring solution ensures that you can diagnose problems at one place and fix them within
This webinar talks about how Azure Monitor has eased the monitoring of complex modern applications, whether cloud-based or on-premise. It answers questions like -
~ How to quickly detect and diagnose issues across applications?
~ How to manage infrastructure concerns like those in VMs or containers?
~ How to gain insights from your monitoring data?
~ How to support operations at scale?
#UnifyCloud has participated in #Microsoft_Ignite 2017. #CloudAtlas® automates each phase of the migration (e.g. Assess, Migrate, and #Configuration_Management) lifecycle while ensuring the Security, #Compliance (GRC) and Cost Optimization of your apps and data. http://bit.ly/2auPCzi
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
Building the Next-gen Digital Meter Platform for FluviusDatabricks
Fluvius is the network operator for electricity and gas in Flanders, Belgium. Their goal is to modernize the way people look at energy consumption using a digital meter that captures consumption and injection data from any electrical installation in Flanders ranging from households to large companies. After full roll-out there will be roughly 7 million digital meters active in Flanders collecting up to terabytes of data per day. Combine this with regulation that Fluvius has to maintain a record of these reading for at least 3 years, we are talking petabyte scale. delaware BeLux was assigned by Fluvius to setup a modern data platform and did so on Azure using Databricks as the core component to collect, store, process and serve these volumes of data to every single consumer and beyond in Flanders. This enables the Belgian energy market to innovate and move forward. Maarten took up the role as project manager and solution architect.
Puedes consultar de forma gratuita la guía con los mejores vinos y destilados 2017 que elabora Wine Up!.
En esta primera edición, cerca de 600 vinos y destilados, además de la sección de enoturismo, top 100 vinos tranquilos y espumosos, top 20 vinos generosos y top 10 destilados.
A través de sus links puedes reservar visitas a bodegas o comprar online directamente en la tienda de bodega. También podrás descargar las fichas de cata o ver videocatas de algunos de los vinos catados.
Si te gusta, no cuesta compartir y difundir. Entre todos se consigue más.
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder, DataTorrent - ...Dataconomy Media
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder of DataTorrent presented "Streaming Analytics with Apache Apex" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.
How to scale your PaaS with OVH infrastructure?OVHcloud
ForePaaS has developed an “as-a-service” platform which lets you automate an infrastructure designed for analytical applications. The company has formed a cloud partnership with OVH in order to deliver flexible solutions for containerised and high-performance tools, such as Kunernetes and Docker.
WSO2 Data Analytics Server is a comprehensive enterprise data analytics platform; it fuses batch and real-time analytics of any source of data with predictive analytics via machine learning.
Organizational success depends on our ability to sense the environment, grab opportunities and eliminate threats that are present in real-time. Such real-time processing is now available to all organizations (with or without a big data background) through the new WSO2 Stream Processor.
This slides presents WSO2 Stream Processor’s new features and improvements and explains how they make an organization excel in the current competitive marketplace. Some key features we will consider are:
* WSO2 Stream Processor’s highly productive developer environment, with graphical drag-and-drop, and the Streaming SQL query editor
* The ability to process real-time queries that span from seconds to years
* Its interactive visualization and dashboarding features with improved widget generation
* Its ability to processing at scale via distributed deployments with full observability
* Default support for HTTP analytics, distributed message trace analytics, and Twitter analytics
Time's Up! Getting Value from Big Data NowEric Kavanagh
The Briefing Room with Dr. Robin Bloor and CASK
We all know the promise of big data, but who gets the value? There are plenty of success stories already, and most of them involve one key ingredient: facilitated access to important data sets. Most research studies suggest that the Pareto principle applies: 80 percent goes to data integration, and only 20 to analysis. Inverting that balance is the Holy Grail.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why the time has finally come for turning the tables on the status quo in analytics. He'll be briefed by CASK CEO Jonathan Gray, who will showcase his company's big data integration platform, CDAP, which was specifically designed to expedite time-to-value for big data.
This slide deck explores WSO2 Stream Processor’s new features and improvements and explain how they make an organization excel in the current competitive marketplace.
IoT Architectures for Apache Kafka and Event Streaming - Industry 4.0, Digita...Kai Wähner
The Internet of Things (IoT) is getting more and more traction as valuable use cases come to light. Whether you are in Healthcare, Telecommunications, Manufacturing, Banking or Retail to name a few industries, there is one key challenge and that's the integration of backend IoT data logs and applications, business services and cloud services to process the data in real time and at scale.
In this talk, we will be sharing how Kafka has become the leading technology used throughout the business to provide Real Time Event Streaming. Explore real life use cases of Kafka Connect, Kafka Streams and KSQL independent of the data deployment be it on a private or public Cloud, On Premise or at the Edge.
Audi - Connected car infrastructure
Robert Bosch Power Tools - Track and Trace of devices and people at construction areas
Deutsche Bahn - Customer 360 for train timetable updates
E.ON - IoT Streaming Platform to integrate and build smart home, smart building and smart grid infrastructures
Cortana Analytics Workshop: The "Big Data" of the Cortana Analytics Suite, Pa...MSAdvAnalytics
Lance Olson. Cortana Analytics is a fully managed big data and advanced analytics suite that helps you transform your data into intelligent action. Come to this two-part session to learn how you can do "big data" processing and storage in Cortana Analytics. In the first part, we will provide an overview of the processing and storage services. We will then talk about the patterns and use cases which make up most big data solutions. In the second part, we will go hands-on, showing you how to get started today with writing batch/interactive queries, real-time stream processing, or NoSQL transactions all over the same repository of data. Crunch petabytes of data by scaling out your computation power to any sized cluster. Store any amount of unstructured data in its native format with no limits to file or account size. All of this can be done with no hardware to acquire or maintain and minimal time to setup giving you the value of "big data" within minutes. Go to https://channel9.msdn.com/ to find the recording of this session.
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.
1. Data Sheet
StreamAnalytix 2.0
Industry’s Only Multi-Engine Streaming
Analytics Platform
KEY FEATURES
• Easy drag-and drop UI
• Complex event processing
• Predictive Analytics and
Machine Learning
CLUSTER MANAGER
A web-based application
that creates, configures and
manages clusters of
StreamAnalytix. It also
provides graphical information
about the health of the cluster
and can configure alerts and
notifications
• Real-time Dashboards
StreamAnalytix 2.0 is architected to provide a level of abstraction that allows for
the deployment of multiple streaming engines depending on the use-case
requirements. This affords customers a new level of “best-of-breed” flexibility in their
real-time architecture.
With StreamAnalytix, you can use the visual IDE and an enhanced set of powerful
stream processing operators to easily construct data pipelines in a matter of minutes.
You can then deploy them to a stream processing engine of choice.
Enterprises are now rapidly moving to add real-time streaming analytics as a strategy for
becoming more agile and responsive to data available in real-time. StreamAnalytix is a
platform to build and deploy streaming analytics applications for any industry vertical, any
data format, and any use case.
Focus on your business logic. Leave the plumbing to StreamAnalytix
• Support for Spark Streaming
A rich array of drag-and-drop Spark data transformations including Machine Learning
operations to analyze data using SQL queries and save the query output in a data
store of choice. Built-in operators for predictive models with inline model-test feature
and graphs to visually analyze data for models like Neural Networks and Tree.
• Proven Open Source Stack
Ingest, store, and analyze millions of events per second with a pre-integrated package
of industry-preferred Open Source components: Hadoop, NoSQL, Kafka, RabbitMQ,
Apache Storm, Elastic Search, and Apache Solr.
• Visual Performance Monitoring
Monitor performance of running applications and their underlying compute components
visually through graphs. Set alerts to get real-time notification on threshold breaches.
• Rapid App Development
Integrate custom applications into the real-time data pipeline by visual drag and drop.
Rapidly port predictive analytics and machine learning models built in SAS or R via
PMML onto real-time data.
• Open, Flexible, & Extensible
Use any fast-ingest data store of your choice. Bring in any number of proprietary or
standard data sources. Integrate the real-time data pipeline with other existing
applications, based on configurable conditions.