SlideShare a Scribd company logo
1 of 15
Download to read offline
StreamProcessing.be
Brussels, May 27, 2015
Theme: hosted solutions for
Stream Processing and ML
#StreamProcessingBe
Agenda
15’ Intro (Peter)
35’ Azure Stream Analytics and ML (Jan)
5’ short break
35’ Google Cloud DataFlow (Alex)
35’ Amazon AWS ML (Nils)
Many thanks to
Microsoft Belux
Jan, Alex, Nils
@maasg, @svendfx
BigData.be, DataScience.be, AWS Belgium
you !
Next StreamProcessing.be Meetup
Thu, June 25, 2015, near Mechelen station
(looking for a location +/- 50 ppl)
● Introduction to Apache Kafka (Svend)
● Akka Streams and Kinesis (Peter)
● Understanding Spark Streaming (Gerard)
whoami : Peter Vandenabeele @peter_v
All Things Data (my consultancy)
current clients:
Real Impact Analytics
Telecom Analytics (emerging markets)
“Green” start-up (stealth mode)
IoT project (see next Meetup)
Why ?
(before anything else)
Why Stream Processing ?
(a personal view)
E.g. collaborative research (2013)
UniProt
(180 GB)
monthly update
consumer
update cost
≅
freq (1/month)
*
size (180 GB)
*
# consumers (5)
fetch + load + index
FULL data set
solution: Stream of updates (CDC)
Users table
continuous
updates
consumer
update cost
≅
Rate of Change
(10% / month)
*
size * # consumers
fetch + load
ONLY updates
stream
3M entries
300k updates/month
(independent of consumer update frequency)
Why Stream Processing ?
Real-time
*
Big Data
*
Distributed processing
(“many collaborators”)
Stream becomes the “master data”
● see stream as the master data (not the DB)
● allows real-time, distributed processing
● allows unification between:
○ operational teams
○ analytics teams
○ security, ...
● e.g. Kafka at LinkedIn (Kappa architecture)
Kafka (LinkedIn) : Martin Kleppmann
source : Martin Kleppmann
at strata Hadoop London
Kafka (LinkedIn) : Jay Kreps
source: Jay Kreps
on slideshare
“I ♥ Log”
Real-time Data and Apache Kafka
Why Stream Processing ?
Peter : real-time * (big data * distributed proc.)
Nathan Marz : recovery from human error + ...
Jay Kreps : organizational scalability + ...
Martin Kleppmann : data agility + …
YOU : ??? let’s discuss at beer ...
Speakers for today
● Jan Tielens (Microsoft) @jantielens
● Alex Van Boxel (Vente-Exclusive.com)
@alexvb
● Nils De Moor (Woorank) @ndemoor

More Related Content

What's hot

Reactive streams and components on OSGi - C Schneider
Reactive streams and components on OSGi - C SchneiderReactive streams and components on OSGi - C Schneider
Reactive streams and components on OSGi - C Schneidermfrancis
 
VulcanJS Austin Presentation
VulcanJS Austin PresentationVulcanJS Austin Presentation
VulcanJS Austin PresentationJustin Reynolds
 
Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...
Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...
Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...iguazio
 
Akka Streams - From Zero to Kafka
Akka Streams - From Zero to KafkaAkka Streams - From Zero to Kafka
Akka Streams - From Zero to KafkaMark Harrison
 
Cross company issue tracking slides
Cross company issue tracking   slidesCross company issue tracking   slides
Cross company issue tracking slidesExalate
 
Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018
Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018
Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018iguazio
 
Project FiFo - Architecture
Project FiFo - ArchitectureProject FiFo - Architecture
Project FiFo - ArchitectureLicenser
 
A (XPages) developers guide to Cloudant - MeetIT
A (XPages) developers guide to Cloudant - MeetITA (XPages) developers guide to Cloudant - MeetIT
A (XPages) developers guide to Cloudant - MeetITFrank van der Linden
 
Pipes & Filters Architectural Pattern
Pipes & Filters Architectural PatternPipes & Filters Architectural Pattern
Pipes & Filters Architectural PatternFredrik Kivi
 
Exalate - Issue Sync for Jira and More
Exalate - Issue Sync for Jira and MoreExalate - Issue Sync for Jira and More
Exalate - Issue Sync for Jira and MoreExalate
 
Ansible: Infrastructure as Code for OpenShift
Ansible: Infrastructure as Code for OpenShiftAnsible: Infrastructure as Code for OpenShift
Ansible: Infrastructure as Code for OpenShiftIgnacio Sánchez Ginés
 
Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...
Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...
Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...Frank van der Linden
 
Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017
Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017
Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017Codemotion
 
Сергей Калинец "Не SQL-ом единым..."
Сергей Калинец "Не SQL-ом единым..."Сергей Калинец "Не SQL-ом единым..."
Сергей Калинец "Не SQL-ом единым..."Fwdays
 
Scaling infrastructure beyond containers
Scaling infrastructure beyond containersScaling infrastructure beyond containers
Scaling infrastructure beyond containersallegro.tech
 
Reactive Database Access With Slick 3
Reactive Database Access With Slick 3Reactive Database Access With Slick 3
Reactive Database Access With Slick 3Igor Mielientiev
 
Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...
Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...
Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...Lightbend
 
Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...
Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...
Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...HostedbyConfluent
 
Node.js server side render in the Age of APIs - Full Stack Toronto 2017
 Node.js server side render in the Age of APIs - Full Stack Toronto 2017 Node.js server side render in the Age of APIs - Full Stack Toronto 2017
Node.js server side render in the Age of APIs - Full Stack Toronto 2017Ruy Adorno
 

What's hot (20)

Reactive streams and components on OSGi - C Schneider
Reactive streams and components on OSGi - C SchneiderReactive streams and components on OSGi - C Schneider
Reactive streams and components on OSGi - C Schneider
 
VulcanJS Austin Presentation
VulcanJS Austin PresentationVulcanJS Austin Presentation
VulcanJS Austin Presentation
 
Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...
Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...
Building the Serverless Container Experience: Kevin McGrath, Spotinst, Server...
 
Akka Streams - From Zero to Kafka
Akka Streams - From Zero to KafkaAkka Streams - From Zero to Kafka
Akka Streams - From Zero to Kafka
 
Cross company issue tracking slides
Cross company issue tracking   slidesCross company issue tracking   slides
Cross company issue tracking slides
 
Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018
Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018
Serverless and AI: Orit Nissan-Messing, Iguazio, Serverless NYC 2018
 
Project FiFo - Architecture
Project FiFo - ArchitectureProject FiFo - Architecture
Project FiFo - Architecture
 
A (XPages) developers guide to Cloudant - MeetIT
A (XPages) developers guide to Cloudant - MeetITA (XPages) developers guide to Cloudant - MeetIT
A (XPages) developers guide to Cloudant - MeetIT
 
Pipes & Filters Architectural Pattern
Pipes & Filters Architectural PatternPipes & Filters Architectural Pattern
Pipes & Filters Architectural Pattern
 
Exalate - Issue Sync for Jira and More
Exalate - Issue Sync for Jira and MoreExalate - Issue Sync for Jira and More
Exalate - Issue Sync for Jira and More
 
Ansible: Infrastructure as Code for OpenShift
Ansible: Infrastructure as Code for OpenShiftAnsible: Infrastructure as Code for OpenShift
Ansible: Infrastructure as Code for OpenShift
 
Welcome Keynote
Welcome KeynoteWelcome Keynote
Welcome Keynote
 
Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...
Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...
Wcs-1785 How Watson, Bluemix, Cloudant and XPages can work together in a real...
 
Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017
Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017
Unreal Engine 4 Blueprints: Odio e amore Roberto De Ioris - Codemotion Rome 2017
 
Сергей Калинец "Не SQL-ом единым..."
Сергей Калинец "Не SQL-ом единым..."Сергей Калинец "Не SQL-ом единым..."
Сергей Калинец "Не SQL-ом единым..."
 
Scaling infrastructure beyond containers
Scaling infrastructure beyond containersScaling infrastructure beyond containers
Scaling infrastructure beyond containers
 
Reactive Database Access With Slick 3
Reactive Database Access With Slick 3Reactive Database Access With Slick 3
Reactive Database Access With Slick 3
 
Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...
Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...
Akka A to Z: A Guide To The Industry’s Best Toolkit for Fast Data and Microse...
 
Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...
Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...
Delivering Cloud-Native Data Pipelines with Kafka Connect on Kubernetes | Vik...
 
Node.js server side render in the Age of APIs - Full Stack Toronto 2017
 Node.js server side render in the Age of APIs - Full Stack Toronto 2017 Node.js server side render in the Age of APIs - Full Stack Toronto 2017
Node.js server side render in the Age of APIs - Full Stack Toronto 2017
 

Viewers also liked

Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization
Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time PersonalizationUsing Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization
Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time PersonalizationPatrick Di Loreto
 
AWSでスケールアウト&スケールアップ
AWSでスケールアウト&スケールアップAWSでスケールアウト&スケールアップ
AWSでスケールアウト&スケールアップHiroyasu Suzuki
 
AWS+でスケールアウト&スケールアップ
AWS+でスケールアウト&スケールアップAWS+でスケールアウト&スケールアップ
AWS+でスケールアウト&スケールアップHiroyasu Suzuki
 
Flink Case Study: Amadeus
Flink Case Study: AmadeusFlink Case Study: Amadeus
Flink Case Study: AmadeusFlink Forward
 
Azure Stream Analytics
Azure Stream AnalyticsAzure Stream Analytics
Azure Stream AnalyticsMarco Parenzan
 
PopcornFlow: Continuous Evolution Through Ultra-Rapid Experimentation
PopcornFlow: Continuous Evolution Through Ultra-Rapid ExperimentationPopcornFlow: Continuous Evolution Through Ultra-Rapid Experimentation
PopcornFlow: Continuous Evolution Through Ultra-Rapid ExperimentationClaudio Perrone
 
Asynchronous API in Java8, how to use CompletableFuture
Asynchronous API in Java8, how to use CompletableFutureAsynchronous API in Java8, how to use CompletableFuture
Asynchronous API in Java8, how to use CompletableFutureJosé Paumard
 
Flink Case Study: Capital One
Flink Case Study: Capital OneFlink Case Study: Capital One
Flink Case Study: Capital OneFlink Forward
 
A dive into akka streams: from the basics to a real-world scenario
A dive into akka streams: from the basics to a real-world scenarioA dive into akka streams: from the basics to a real-world scenario
A dive into akka streams: from the basics to a real-world scenarioGioia Ballin
 
Apache Flink: Real-World Use Cases for Streaming Analytics
Apache Flink: Real-World Use Cases for Streaming AnalyticsApache Flink: Real-World Use Cases for Streaming Analytics
Apache Flink: Real-World Use Cases for Streaming AnalyticsSlim Baltagi
 
7 key recipes for data engineering
7 key recipes for data engineering7 key recipes for data engineering
7 key recipes for data engineeringunivalence
 
Exploring Reactive Integrations With Akka Streams, Alpakka And Apache Kafka
Exploring Reactive Integrations With Akka Streams, Alpakka And Apache KafkaExploring Reactive Integrations With Akka Streams, Alpakka And Apache Kafka
Exploring Reactive Integrations With Akka Streams, Alpakka And Apache KafkaLightbend
 
OpenSource Big Data Platform : Flamingo Project
OpenSource Big Data Platform : Flamingo ProjectOpenSource Big Data Platform : Flamingo Project
OpenSource Big Data Platform : Flamingo ProjectBYOUNG GON KIM
 
The Snail Entrepreneur: The 7-year-old kid every startup should learn from
The Snail Entrepreneur: The 7-year-old kid every startup should learn fromThe Snail Entrepreneur: The 7-year-old kid every startup should learn from
The Snail Entrepreneur: The 7-year-old kid every startup should learn fromClaudio Perrone
 
The Moneyball Approach to Recruitment: Big Data = Big Changes
The Moneyball Approach to Recruitment: Big Data = Big ChangesThe Moneyball Approach to Recruitment: Big Data = Big Changes
The Moneyball Approach to Recruitment: Big Data = Big ChangesGlen Cathey
 
並行処理初心者のためのAkka入門
並行処理初心者のためのAkka入門並行処理初心者のためのAkka入門
並行処理初心者のためのAkka入門Yoshimura Soichiro
 
Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)
Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)
Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)Codit
 
Customer Success Plan Template
Customer Success Plan TemplateCustomer Success Plan Template
Customer Success Plan TemplateOpsPanda
 

Viewers also liked (20)

Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization
Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time PersonalizationUsing Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization
Using Spark, Kafka, Cassandra and Akka on Mesos for Real-Time Personalization
 
AWSでスケールアウト&スケールアップ
AWSでスケールアウト&スケールアップAWSでスケールアウト&スケールアップ
AWSでスケールアウト&スケールアップ
 
AWS+でスケールアウト&スケールアップ
AWS+でスケールアウト&スケールアップAWS+でスケールアウト&スケールアップ
AWS+でスケールアウト&スケールアップ
 
Flink Case Study: Amadeus
Flink Case Study: AmadeusFlink Case Study: Amadeus
Flink Case Study: Amadeus
 
Azure Stream Analytics
Azure Stream AnalyticsAzure Stream Analytics
Azure Stream Analytics
 
PopcornFlow: Continuous Evolution Through Ultra-Rapid Experimentation
PopcornFlow: Continuous Evolution Through Ultra-Rapid ExperimentationPopcornFlow: Continuous Evolution Through Ultra-Rapid Experimentation
PopcornFlow: Continuous Evolution Through Ultra-Rapid Experimentation
 
Asynchronous API in Java8, how to use CompletableFuture
Asynchronous API in Java8, how to use CompletableFutureAsynchronous API in Java8, how to use CompletableFuture
Asynchronous API in Java8, how to use CompletableFuture
 
Flink Case Study: Capital One
Flink Case Study: Capital OneFlink Case Study: Capital One
Flink Case Study: Capital One
 
A dive into akka streams: from the basics to a real-world scenario
A dive into akka streams: from the basics to a real-world scenarioA dive into akka streams: from the basics to a real-world scenario
A dive into akka streams: from the basics to a real-world scenario
 
Apache Flink: Real-World Use Cases for Streaming Analytics
Apache Flink: Real-World Use Cases for Streaming AnalyticsApache Flink: Real-World Use Cases for Streaming Analytics
Apache Flink: Real-World Use Cases for Streaming Analytics
 
7 key recipes for data engineering
7 key recipes for data engineering7 key recipes for data engineering
7 key recipes for data engineering
 
Exploring Reactive Integrations With Akka Streams, Alpakka And Apache Kafka
Exploring Reactive Integrations With Akka Streams, Alpakka And Apache KafkaExploring Reactive Integrations With Akka Streams, Alpakka And Apache Kafka
Exploring Reactive Integrations With Akka Streams, Alpakka And Apache Kafka
 
OpenSource Big Data Platform : Flamingo Project
OpenSource Big Data Platform : Flamingo ProjectOpenSource Big Data Platform : Flamingo Project
OpenSource Big Data Platform : Flamingo Project
 
The Snail Entrepreneur: The 7-year-old kid every startup should learn from
The Snail Entrepreneur: The 7-year-old kid every startup should learn fromThe Snail Entrepreneur: The 7-year-old kid every startup should learn from
The Snail Entrepreneur: The 7-year-old kid every startup should learn from
 
The Moneyball Approach to Recruitment: Big Data = Big Changes
The Moneyball Approach to Recruitment: Big Data = Big ChangesThe Moneyball Approach to Recruitment: Big Data = Big Changes
The Moneyball Approach to Recruitment: Big Data = Big Changes
 
並行処理初心者のためのAkka入門
並行処理初心者のためのAkka入門並行処理初心者のためのAkka入門
並行処理初心者のためのAkka入門
 
Sprinting to Value in Industry 4.0
Sprinting to Value in Industry 4.0Sprinting to Value in Industry 4.0
Sprinting to Value in Industry 4.0
 
Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)
Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)
Azure event hubs, Stream Analytics & Power BI (by Sam Vanhoutte)
 
Flink vs. Spark
Flink vs. SparkFlink vs. Spark
Flink vs. Spark
 
Customer Success Plan Template
Customer Success Plan TemplateCustomer Success Plan Template
Customer Success Plan Template
 

Similar to Intro stream processing.be meetup #1

Dissemination and Community Building
Dissemination and Community BuildingDissemination and Community Building
Dissemination and Community BuildingAGILE IoT
 
Prescriptive Analytics
Prescriptive AnalyticsPrescriptive Analytics
Prescriptive AnalyticsŁukasz Grala
 
Internet of Things (IoT) and Big Data
Internet of Things (IoT) and Big DataInternet of Things (IoT) and Big Data
Internet of Things (IoT) and Big DataGuido Schmutz
 
Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...
Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...
Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...serge luca
 
Microsoft Flow Advanced: tips, pitfalls, problems
Microsoft Flow Advanced: tips, pitfalls, problemsMicrosoft Flow Advanced: tips, pitfalls, problems
Microsoft Flow Advanced: tips, pitfalls, problemsBIWUG
 
AI Solutions with Macnica.ai - AI Expo 2018 Tokyo Japan
AI Solutions with Macnica.ai - AI Expo 2018 Tokyo JapanAI Solutions with Macnica.ai - AI Expo 2018 Tokyo Japan
AI Solutions with Macnica.ai - AI Expo 2018 Tokyo JapanAvkash Chauhan
 
Scalable Open-Source IoT Solutions on Microsoft Azure
Scalable Open-Source IoT Solutions on Microsoft AzureScalable Open-Source IoT Solutions on Microsoft Azure
Scalable Open-Source IoT Solutions on Microsoft AzureMaxim Ivannikov
 
Democratizing Artificial Intelligence
Democratizing Artificial IntelligenceDemocratizing Artificial Intelligence
Democratizing Artificial IntelligenceDmitry Petukhov
 
Upgrading from Full Trust Code to Add-In Model and SharePoint Framework
Upgrading from Full Trust Code to Add-In Model and SharePoint FrameworkUpgrading from Full Trust Code to Add-In Model and SharePoint Framework
Upgrading from Full Trust Code to Add-In Model and SharePoint FrameworkBIWUG
 
Bhadale group of companies projects portfolio
Bhadale group of companies  projects portfolioBhadale group of companies  projects portfolio
Bhadale group of companies projects portfolioVijayananda Mohire
 
Stephan Bisser Collab365 Smart Meeting Room
Stephan Bisser Collab365 Smart Meeting RoomStephan Bisser Collab365 Smart Meeting Room
Stephan Bisser Collab365 Smart Meeting RoomStephan Bisser
 
NashTech - Azure IoT Solutions on Microsoft Azure
NashTech - Azure IoT Solutions on Microsoft AzureNashTech - Azure IoT Solutions on Microsoft Azure
NashTech - Azure IoT Solutions on Microsoft AzurePhi Huynh
 
Data analytics at a petabyte scale final
Data analytics at a petabyte scale   finalData analytics at a petabyte scale   final
Data analytics at a petabyte scale finalOri Reshef
 
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...Guido Schmutz
 
Building big data solutions on azure
Building big data solutions on azureBuilding big data solutions on azure
Building big data solutions on azureEyal Ben Ivri
 
Introduction to Azure Synapse Webinar
Introduction to Azure Synapse WebinarIntroduction to Azure Synapse Webinar
Introduction to Azure Synapse WebinarPeter Ward
 
Data Culture Series - Keynote - 16th September 2014
Data Culture Series - Keynote - 16th September 2014Data Culture Series - Keynote - 16th September 2014
Data Culture Series - Keynote - 16th September 2014Jonathan Woodward
 
Powerful Google developer tools for immediate impact! (2023-24 B)
Powerful Google developer tools for immediate impact! (2023-24 B)Powerful Google developer tools for immediate impact! (2023-24 B)
Powerful Google developer tools for immediate impact! (2023-24 B)wesley chun
 
DSDT Meetup June 2018
DSDT Meetup June 2018DSDT Meetup June 2018
DSDT Meetup June 2018DSDT_MTL
 

Similar to Intro stream processing.be meetup #1 (20)

Dissemination and Community Building
Dissemination and Community BuildingDissemination and Community Building
Dissemination and Community Building
 
Prescriptive Analytics
Prescriptive AnalyticsPrescriptive Analytics
Prescriptive Analytics
 
Internet of Things (IoT) and Big Data
Internet of Things (IoT) and Big DataInternet of Things (IoT) and Big Data
Internet of Things (IoT) and Big Data
 
Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...
Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...
Microsoft Flow Advanced : tips, pitfalls, problems to be known before staring...
 
Microsoft Flow Advanced: tips, pitfalls, problems
Microsoft Flow Advanced: tips, pitfalls, problemsMicrosoft Flow Advanced: tips, pitfalls, problems
Microsoft Flow Advanced: tips, pitfalls, problems
 
AI Solutions with Macnica.ai - AI Expo 2018 Tokyo Japan
AI Solutions with Macnica.ai - AI Expo 2018 Tokyo JapanAI Solutions with Macnica.ai - AI Expo 2018 Tokyo Japan
AI Solutions with Macnica.ai - AI Expo 2018 Tokyo Japan
 
Scalable Open-Source IoT Solutions on Microsoft Azure
Scalable Open-Source IoT Solutions on Microsoft AzureScalable Open-Source IoT Solutions on Microsoft Azure
Scalable Open-Source IoT Solutions on Microsoft Azure
 
Data Driven
Data DrivenData Driven
Data Driven
 
Democratizing Artificial Intelligence
Democratizing Artificial IntelligenceDemocratizing Artificial Intelligence
Democratizing Artificial Intelligence
 
Upgrading from Full Trust Code to Add-In Model and SharePoint Framework
Upgrading from Full Trust Code to Add-In Model and SharePoint FrameworkUpgrading from Full Trust Code to Add-In Model and SharePoint Framework
Upgrading from Full Trust Code to Add-In Model and SharePoint Framework
 
Bhadale group of companies projects portfolio
Bhadale group of companies  projects portfolioBhadale group of companies  projects portfolio
Bhadale group of companies projects portfolio
 
Stephan Bisser Collab365 Smart Meeting Room
Stephan Bisser Collab365 Smart Meeting RoomStephan Bisser Collab365 Smart Meeting Room
Stephan Bisser Collab365 Smart Meeting Room
 
NashTech - Azure IoT Solutions on Microsoft Azure
NashTech - Azure IoT Solutions on Microsoft AzureNashTech - Azure IoT Solutions on Microsoft Azure
NashTech - Azure IoT Solutions on Microsoft Azure
 
Data analytics at a petabyte scale final
Data analytics at a petabyte scale   finalData analytics at a petabyte scale   final
Data analytics at a petabyte scale final
 
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...
IoT Architecture - Are Traditional Architectures Good Enough or do we Need Ne...
 
Building big data solutions on azure
Building big data solutions on azureBuilding big data solutions on azure
Building big data solutions on azure
 
Introduction to Azure Synapse Webinar
Introduction to Azure Synapse WebinarIntroduction to Azure Synapse Webinar
Introduction to Azure Synapse Webinar
 
Data Culture Series - Keynote - 16th September 2014
Data Culture Series - Keynote - 16th September 2014Data Culture Series - Keynote - 16th September 2014
Data Culture Series - Keynote - 16th September 2014
 
Powerful Google developer tools for immediate impact! (2023-24 B)
Powerful Google developer tools for immediate impact! (2023-24 B)Powerful Google developer tools for immediate impact! (2023-24 B)
Powerful Google developer tools for immediate impact! (2023-24 B)
 
DSDT Meetup June 2018
DSDT Meetup June 2018DSDT Meetup June 2018
DSDT Meetup June 2018
 

Recently uploaded

Test Automation Design Patterns_ A Comprehensive Guide.pdf
Test Automation Design Patterns_ A Comprehensive Guide.pdfTest Automation Design Patterns_ A Comprehensive Guide.pdf
Test Automation Design Patterns_ A Comprehensive Guide.pdfkalichargn70th171
 
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-Cloud
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-CloudAlluxio Monthly Webinar | Simplify Data Access for AI in Multi-Cloud
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-CloudAlluxio, Inc.
 
GraphSummit Milan - Neo4j: The Art of the Possible with Graph
GraphSummit Milan - Neo4j: The Art of the Possible with GraphGraphSummit Milan - Neo4j: The Art of the Possible with Graph
GraphSummit Milan - Neo4j: The Art of the Possible with GraphNeo4j
 
UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida
UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale IbridaUNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida
UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale IbridaNeo4j
 
GraphSummit Milan - Visione e roadmap del prodotto Neo4j
GraphSummit Milan - Visione e roadmap del prodotto Neo4jGraphSummit Milan - Visione e roadmap del prodotto Neo4j
GraphSummit Milan - Visione e roadmap del prodotto Neo4jNeo4j
 
What Goes Wrong with Language Definitions and How to Improve the Situation
What Goes Wrong with Language Definitions and How to Improve the SituationWhat Goes Wrong with Language Definitions and How to Improve the Situation
What Goes Wrong with Language Definitions and How to Improve the SituationJuha-Pekka Tolvanen
 
A Deep Dive into Secure Product Development Frameworks.pdf
A Deep Dive into Secure Product Development Frameworks.pdfA Deep Dive into Secure Product Development Frameworks.pdf
A Deep Dive into Secure Product Development Frameworks.pdfICS
 
BusinessGPT - Security and Governance for Generative AI
BusinessGPT  - Security and Governance for Generative AIBusinessGPT  - Security and Governance for Generative AI
BusinessGPT - Security and Governance for Generative AIAGATSoftware
 
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...drm1699
 
Lessons Learned from Building a Serverless Notifications System.pdf
Lessons Learned from Building a Serverless Notifications System.pdfLessons Learned from Building a Serverless Notifications System.pdf
Lessons Learned from Building a Serverless Notifications System.pdfSrushith Repakula
 
Modern binary build systems - PyCon 2024
Modern binary build systems - PyCon 2024Modern binary build systems - PyCon 2024
Modern binary build systems - PyCon 2024Henry Schreiner
 
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...Flutter Agency
 
[GRCPP] Introduction to concepts (C++20)
[GRCPP] Introduction to concepts (C++20)[GRCPP] Introduction to concepts (C++20)
[GRCPP] Introduction to concepts (C++20)Dimitrios Platis
 
Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024
Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024
Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024MulesoftMunichMeetup
 
Weeding your micro service landscape.pdf
Weeding your micro service landscape.pdfWeeding your micro service landscape.pdf
Weeding your micro service landscape.pdftimtebeek1
 

Recently uploaded (20)

Test Automation Design Patterns_ A Comprehensive Guide.pdf
Test Automation Design Patterns_ A Comprehensive Guide.pdfTest Automation Design Patterns_ A Comprehensive Guide.pdf
Test Automation Design Patterns_ A Comprehensive Guide.pdf
 
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-Cloud
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-CloudAlluxio Monthly Webinar | Simplify Data Access for AI in Multi-Cloud
Alluxio Monthly Webinar | Simplify Data Access for AI in Multi-Cloud
 
GraphSummit Milan - Neo4j: The Art of the Possible with Graph
GraphSummit Milan - Neo4j: The Art of the Possible with GraphGraphSummit Milan - Neo4j: The Art of the Possible with Graph
GraphSummit Milan - Neo4j: The Art of the Possible with Graph
 
UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida
UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale IbridaUNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida
UNI DI NAPOLI FEDERICO II - Il ruolo dei grafi nell'AI Conversazionale Ibrida
 
GraphSummit Milan - Visione e roadmap del prodotto Neo4j
GraphSummit Milan - Visione e roadmap del prodotto Neo4jGraphSummit Milan - Visione e roadmap del prodotto Neo4j
GraphSummit Milan - Visione e roadmap del prodotto Neo4j
 
What Goes Wrong with Language Definitions and How to Improve the Situation
What Goes Wrong with Language Definitions and How to Improve the SituationWhat Goes Wrong with Language Definitions and How to Improve the Situation
What Goes Wrong with Language Definitions and How to Improve the Situation
 
Abortion Pill Prices Germiston ](+27832195400*)[ 🏥 Women's Abortion Clinic in...
Abortion Pill Prices Germiston ](+27832195400*)[ 🏥 Women's Abortion Clinic in...Abortion Pill Prices Germiston ](+27832195400*)[ 🏥 Women's Abortion Clinic in...
Abortion Pill Prices Germiston ](+27832195400*)[ 🏥 Women's Abortion Clinic in...
 
A Deep Dive into Secure Product Development Frameworks.pdf
A Deep Dive into Secure Product Development Frameworks.pdfA Deep Dive into Secure Product Development Frameworks.pdf
A Deep Dive into Secure Product Development Frameworks.pdf
 
Abortion Pill Prices Jane Furse ](+27832195400*)[🏥Women's Abortion Clinic in ...
Abortion Pill Prices Jane Furse ](+27832195400*)[🏥Women's Abortion Clinic in ...Abortion Pill Prices Jane Furse ](+27832195400*)[🏥Women's Abortion Clinic in ...
Abortion Pill Prices Jane Furse ](+27832195400*)[🏥Women's Abortion Clinic in ...
 
Abortion Pill Prices Polokwane ](+27832195400*)[ 🏥 Women's Abortion Clinic in...
Abortion Pill Prices Polokwane ](+27832195400*)[ 🏥 Women's Abortion Clinic in...Abortion Pill Prices Polokwane ](+27832195400*)[ 🏥 Women's Abortion Clinic in...
Abortion Pill Prices Polokwane ](+27832195400*)[ 🏥 Women's Abortion Clinic in...
 
BusinessGPT - Security and Governance for Generative AI
BusinessGPT  - Security and Governance for Generative AIBusinessGPT  - Security and Governance for Generative AI
BusinessGPT - Security and Governance for Generative AI
 
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
Abortion Pills For Sale WhatsApp[[+27737758557]] In Birch Acres, Abortion Pil...
 
Lessons Learned from Building a Serverless Notifications System.pdf
Lessons Learned from Building a Serverless Notifications System.pdfLessons Learned from Building a Serverless Notifications System.pdf
Lessons Learned from Building a Serverless Notifications System.pdf
 
Modern binary build systems - PyCon 2024
Modern binary build systems - PyCon 2024Modern binary build systems - PyCon 2024
Modern binary build systems - PyCon 2024
 
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
Navigation in flutter – how to add stack, tab, and drawer navigators to your ...
 
[GRCPP] Introduction to concepts (C++20)
[GRCPP] Introduction to concepts (C++20)[GRCPP] Introduction to concepts (C++20)
[GRCPP] Introduction to concepts (C++20)
 
Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024
Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024
Anypoint Code Builder - Munich MuleSoft Meetup - 16th May 2024
 
Weeding your micro service landscape.pdf
Weeding your micro service landscape.pdfWeeding your micro service landscape.pdf
Weeding your micro service landscape.pdf
 
微信号购买
微信号购买微信号购买
微信号购买
 
Abortion Pill Prices Aliwal North ](+27832195400*)[ 🏥 Women's Abortion Clinic...
Abortion Pill Prices Aliwal North ](+27832195400*)[ 🏥 Women's Abortion Clinic...Abortion Pill Prices Aliwal North ](+27832195400*)[ 🏥 Women's Abortion Clinic...
Abortion Pill Prices Aliwal North ](+27832195400*)[ 🏥 Women's Abortion Clinic...
 

Intro stream processing.be meetup #1

  • 1. StreamProcessing.be Brussels, May 27, 2015 Theme: hosted solutions for Stream Processing and ML #StreamProcessingBe
  • 2. Agenda 15’ Intro (Peter) 35’ Azure Stream Analytics and ML (Jan) 5’ short break 35’ Google Cloud DataFlow (Alex) 35’ Amazon AWS ML (Nils)
  • 3. Many thanks to Microsoft Belux Jan, Alex, Nils @maasg, @svendfx BigData.be, DataScience.be, AWS Belgium you !
  • 4. Next StreamProcessing.be Meetup Thu, June 25, 2015, near Mechelen station (looking for a location +/- 50 ppl) ● Introduction to Apache Kafka (Svend) ● Akka Streams and Kinesis (Peter) ● Understanding Spark Streaming (Gerard)
  • 5. whoami : Peter Vandenabeele @peter_v All Things Data (my consultancy) current clients: Real Impact Analytics Telecom Analytics (emerging markets) “Green” start-up (stealth mode) IoT project (see next Meetup)
  • 7. Why Stream Processing ? (a personal view)
  • 8. E.g. collaborative research (2013) UniProt (180 GB) monthly update consumer update cost ≅ freq (1/month) * size (180 GB) * # consumers (5) fetch + load + index FULL data set
  • 9. solution: Stream of updates (CDC) Users table continuous updates consumer update cost ≅ Rate of Change (10% / month) * size * # consumers fetch + load ONLY updates stream 3M entries 300k updates/month (independent of consumer update frequency)
  • 10. Why Stream Processing ? Real-time * Big Data * Distributed processing (“many collaborators”)
  • 11. Stream becomes the “master data” ● see stream as the master data (not the DB) ● allows real-time, distributed processing ● allows unification between: ○ operational teams ○ analytics teams ○ security, ... ● e.g. Kafka at LinkedIn (Kappa architecture)
  • 12. Kafka (LinkedIn) : Martin Kleppmann source : Martin Kleppmann at strata Hadoop London
  • 13. Kafka (LinkedIn) : Jay Kreps source: Jay Kreps on slideshare “I ♥ Log” Real-time Data and Apache Kafka
  • 14. Why Stream Processing ? Peter : real-time * (big data * distributed proc.) Nathan Marz : recovery from human error + ... Jay Kreps : organizational scalability + ... Martin Kleppmann : data agility + … YOU : ??? let’s discuss at beer ...
  • 15. Speakers for today ● Jan Tielens (Microsoft) @jantielens ● Alex Van Boxel (Vente-Exclusive.com) @alexvb ● Nils De Moor (Woorank) @ndemoor