Smart Mapper

Oct 11, 20181 like22 views

Bas van Oudenaarde

SPIN Meetup Data Driven part 3

• 17.15hr - axonX.ai by Rubix
• 17.20hr -Introductie Data Driven
• 18.00hr - Kafka Platform
• 18.45hr - Dinner
• 19.30hr - Smart Mapper
• 20.00hr - Discussie / Borrel
• 21.00hr - Einde

Adapter x
Adapter y
Systeem
Y
Systeem
X
Adapter x
Adapter y
Systeem
Y
Systeem
X
migration
Cloud, different vendor
• Verouderd
• Documentatie onvolledig
• Legacy stack
• Effort -> reduce 3hr per mapping dev/test
• Error prone
• “Dom werk”

computer Output
Data
Program
Traditioneel programmeren -> Code (periode 1950-…)

computer Output
Data
Little Code / config
Traditioneel programmeren -> Low-Code (2007-…)
“Citizen Developer”

computer Program
Data
Output
Machine Learning -> No-Code (heden-…)

Adapter x
Adapter y
Systeem
Y
Systeem
X
Machine Learning
CDM
• Replay op topic
• Learn by example
• Model == Mapping

Mappings in de ESB:
• Kijk in eerste instantie naar mapping en skip transformaties (lookup tabel, berekeningen ect)
• Per adapter keten scenario, dus alles om X met Y te laten praten
• Graag mapping tabel genereren
• Besparing in tijd ? Hoeveel moet Developer nog doen ?
• Iets slims met AI/ML

XML -> Feature Vector
<a>
<b>
<c> 1234 </c>
</b>
<d>AB345</d>
</a>
A-b-c 1234
A-d AB345
• Plat slaan can de structuur, naar feature vector input voor ML
• Check in de brei van berichten of je alle optionele tags hebt, hier zit dus een risico bij te kleine sets
• Alleen tags meenemen, waar echt waardes in staan, lege structuur geen extra waarden

Machine Learning met Tensorflow:
• Alles omzetten naar getallen
• Alles terug schalen tussen waarden 0 en 1
• Opletten overleren
• Leerfout
• Mapping vs model
• Alleen: getallen
• Alleen: Letters
• Mix: getallen en letters
• Uiteindelijk alles een label ?!
ABCD,123, 1000AB, 12,95
…
…

DEMO -> Tensorflow met STRING mapping
CSV-file (750 lines):
a,b,c,d,e,x,y,z
KYD,HPN,NUI,TPP,KLX,KLX,HPN,KYD
TEQ,MYF,LMX,TOY,CAK,CAK,MYF,TEQ
EIW,OWW,OAM,PVX,DUS,DUS,OWW,EIW
TTT,IFH,YAB,WPI,WQE,WQE,IFH,TTT
…
…
(Mapping: a => h ; b => g ; e => f)

Adapter x
Adapter y
Systeem
Y
Systeem
X
Machine Learning
CDM
migration
alternatief
ML
model
Systeem X
Systeem Y
Adapter x
Adapter y
Systeem Y
Systeem X
ML
model ??

Machine Learning met Inductieve
algoritme:
• Uiteindelijk alles een label ?!
• Model vs mapping
• Based on research papers - > rule-engine-learners + Levenshtein distance
ABCD,123, 1000AB, 12,95
…
…

Inductieve algoritme concept:
F G H
A 1 2 0
B 1 0 1
C
D
E
Mapping: a => h ; b => g ; e => f
<<< per rij, pak kolom met kleinste afstand
F G H
A 1/n
B
C
D
E
Probability matrix aka mapping:Event distance calculatie:
Ook per kolom meenemen naar kansmodel

DEMO -> Homebrew Heuristic Learner
met STRING mapping
Mapping only!
$ python inductive_mapper.py
CSV-file (750 lines):
a,b,c,d,e,x,y,z
KYD,HPN,NUI,TPP,KLX,KLX,HPN,KYD
TEQ,MYF,LMX,TOY,CAK,CAK,MYF,TEQ
EIW,OWW,OAM,PVX,DUS,DUS,OWW,EIW
TTT,IFH,YAB,WPI,WQE,WQE,IFH,TTT
…
…
(Mapping: a => h ; b => g ; e => f)

This document discusses using Apache Kafka, Kafka Connect, and KSQL to perform streaming ETL from a PostgreSQL database. It describes how to integrate PostgreSQL with Kafka using Kafka Connect and the Debezium PostgreSQL connector. It then shows how to use KSQL to perform streaming transformations on the PostgreSQL data in Kafka, including joining, filtering, and aggregating data streams in real-time. The document promotes using this approach for applications like real-time monitoring, anomaly detection, and analytics to enable streaming data pipelines and transform data once for multiple uses.

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This document outlines an agenda for an SPIN Meetup event on October 11th featuring presentations from Bas van Oudenaarde of axonX.ai and Mic Hussey of Confluent. The axonX.ai presentation will introduce the company and its focus on integration using streaming data platforms and event sourcing. It will also discuss axonX.ai's customer use cases and building blocks. The Confluent presentation will discuss the Kafka streaming platform.

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Smart Mapper

1. Bas van Oudenaarde, axonX.ai (by RubiX) Mic Hussey, Confluent axonX.ai

2. • 17.15hr - axonX.ai by Rubix • 17.20hr -Introductie Data Driven • 18.00hr - Kafka Platform • 18.45hr - Dinner • 19.30hr - Smart Mapper • 20.00hr - Discussie / Borrel • 21.00hr - Einde

3. Adapter x Adapter y Systeem Y Systeem X Adapter x Adapter y Systeem Y Systeem X migration Cloud, different vendor • Verouderd • Documentatie onvolledig • Legacy stack • Effort -> reduce 3hr per mapping dev/test • Error prone • “Dom werk”

4. Mapper-de-Map

5. computer Output Data Program Traditioneel programmeren -> Code (periode 1950-…)

6. computer Output Data Little Code / config Traditioneel programmeren -> Low-Code (2007-…) “Citizen Developer”

7. computer Program Data Output Machine Learning -> No-Code (heden-…)

8. Adapter x Adapter y Systeem Y Systeem X Machine Learning CDM • Replay op topic • Learn by example • Model == Mapping

9. Mappings in de ESB: • Kijk in eerste instantie naar mapping en skip transformaties (lookup tabel, berekeningen ect) • Per adapter keten scenario, dus alles om X met Y te laten praten • Graag mapping tabel genereren • Besparing in tijd ? Hoeveel moet Developer nog doen ? • Iets slims met AI/ML

10. XML -> Feature Vector <a> <b> <c> 1234 </c> </b> <d>AB345</d> </a> A-b-c 1234 A-d AB345 • Plat slaan can de structuur, naar feature vector input voor ML • Check in de brei van berichten of je alle optionele tags hebt, hier zit dus een risico bij te kleine sets • Alleen tags meenemen, waar echt waardes in staan, lege structuur geen extra waarden

11. Machine Learning met Tensorflow: • Alles omzetten naar getallen • Alles terug schalen tussen waarden 0 en 1 • Opletten overleren • Leerfout • Mapping vs model • Alleen: getallen • Alleen: Letters • Mix: getallen en letters • Uiteindelijk alles een label ?! ABCD,123, 1000AB, 12,95 … …

12. DEMO -> Tensorflow met STRING mapping CSV-file (750 lines): a,b,c,d,e,x,y,z KYD,HPN,NUI,TPP,KLX,KLX,HPN,KYD TEQ,MYF,LMX,TOY,CAK,CAK,MYF,TEQ EIW,OWW,OAM,PVX,DUS,DUS,OWW,EIW TTT,IFH,YAB,WPI,WQE,WQE,IFH,TTT … … (Mapping: a => h ; b => g ; e => f)

13. Adapter x Adapter y Systeem Y Systeem X Machine Learning CDM migration alternatief ML model Systeem X Systeem Y Adapter x Adapter y Systeem Y Systeem X ML model ??

14. Machine Learning met Inductieve algoritme: • Uiteindelijk alles een label ?! • Model vs mapping • Based on research papers - > rule-engine-learners + Levenshtein distance ABCD,123, 1000AB, 12,95 … …

15. Inductieve algoritme concept: F G H A 1 2 0 B 1 0 1 C D E Mapping: a => h ; b => g ; e => f <<< per rij, pak kolom met kleinste afstand F G H A 1/n B C D E Probability matrix aka mapping:Event distance calculatie: Ook per kolom meenemen naar kansmodel

16. DEMO -> Homebrew Heuristic Learner met STRING mapping Mapping only! $ python inductive_mapper.py CSV-file (750 lines): a,b,c,d,e,x,y,z KYD,HPN,NUI,TPP,KLX,KLX,HPN,KYD TEQ,MYF,LMX,TOY,CAK,CAK,MYF,TEQ EIW,OWW,OAM,PVX,DUS,DUS,OWW,EIW TTT,IFH,YAB,WPI,WQE,WQE,IFH,TTT … … (Mapping: a => h ; b => g ; e => f)

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