Kai Waehner kaiwaehner

Kai Wähner works as Technolgy Evangelist at Confluent. He is regular speaker at international IT conferences. References: www.kai-waehner.de

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Confluent
Munich, Germany
www.kai-waehner.de
@KaiWaehner

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kaiwaehner / gist:209280e2d69e93c3b1bd5f9d5fc00b0c

Created October 9, 2017 06:18

	java.lang.NoClassDefFoundError: Could not initialize class com.fasterxml.jackson.databind.SerializationConfig
	at com.fasterxml.jackson.databind.ObjectMapper.<init>(ObjectMapper.java:558)
	at com.fasterxml.jackson.databind.ObjectMapper.<init>(ObjectMapper.java:474)
	at io.confluent.rest.Application.getJsonMapper(Application.java:407)
	at io.confluent.rest.Application.configureBaseApplication(Application.java:380)
	at io.confluent.rest.Application.createServer(Application.java:148)
	at io.confluent.kafka.schemaregistry.RestApp.start(RestApp.java:59)
	at com.github.megachucky.kafka.streams.machinelearning.test.utils.EmbeddedSingleNodeKafkaCluster.start(EmbeddedSingleNodeKafkaCluster.java:68)
	at com.github.megachucky.kafka.streams.machinelearning.test.utils.EmbeddedSingleNodeKafkaCluster.before(EmbeddedSingleNodeKafkaCluster.java:83)
	at org.junit.rules.ExternalResource$1.evaluate(ExternalResource.java:46)

kaiwaehner / KSQL UDF Query

Created August 15, 2018 07:53

SELECT STRINGTOTIMESTAMP(timestamp, 'yyyy-MM-dd HH:mm:ss.SSS') FROM TimestampStream;

kaiwaehner / KSQL UDF Method Interface

Created August 15, 2018 07:55

	@Udf(description = "apply analytic model to sensor input")
	public String anomaly(String sensorinput){ "YOUR LOGIC" }

kaiwaehner / KSQL UDF Java Implementation (Deep Learning for Anomaly Detection)

Created August 15, 2018 07:56

	package com.github.megachucky.kafka.streams.machinelearning;

	import java.util.Arrays;

	import hex.genmodel.GenModel;
	import hex.genmodel.easy.EasyPredictModelWrapper;
	import hex.genmodel.easy.RowData;
	import hex.genmodel.easy.exception.PredictException;
	import hex.genmodel.easy.prediction.AutoEncoderModelPrediction;
	import io.confluent.ksql.function.udf.Udf;

kaiwaehner / KSQL UDF Query for Anomaly Detection

Created August 15, 2018 07:58

select eventid, anomaly(SENSORINPUT) from carsensor;

kaiwaehner / KSQL Stream with UDF for Anomaly Detection

Created August 15, 2018 07:58

create stream AnomalyDetectionWithFilter as select rowtime, eventid, anomaly(sensorinput) as Anomaly from carsensor where anomaly(sensorinput) > 5;

kaiwaehner / Preprocessing with KSQL

Created January 9, 2019 08:52

CREATE STREAM creditcardfraud_preprocessed_avro WITH (VALUE_FORMAT='AVRO', KAFKA_TOPIC='creditcardfraud_preprocessed_avro') AS SELECT Time,  V1 , V2 , V3 , V4 , V5 , V6 , V7 , V8 , V9 , V10 , V11 , V12 , V13 , V14 , V15 , V16 , V17 , V18 , V19 , V20 , V21 , V22 , V23 , V24 , V25 , V26 , V27 , V28 , Amount , Class FROM creditcardfraud_source WHERE Class IS NOT NULL;

kaiwaehner / gist:46ec08597794bb827162751365f52695

Created January 9, 2019 08:55

Data Anonymisation in KSQL

SELECT Id, MASK_LEFT(User, 2) FROM creditcardfraud_source;

kaiwaehner / gist:128053794064d7709028449ca42b8c21

Created January 9, 2019 08:56

Data Augmentation with KSQL

SELECT Id, IFNULL(Class, -1) FROM creditcardfraud_source;

kaiwaehner / gist:e79749a8232a1d7bc3268ca71b8fa6f6

Created January 9, 2019 08:57

KSQL Stream Table Join

CREATE STREAM creditcardfraud_per_user WITH (VALUE_FORMAT='AVRO', KAFKA_TOPIC='creditcardfraud_preprocessed_avro') AS SELECT Time,  V1 , V2 , V3 , V4 , V5 , V6 , V7 , V8 , V9 , V10 , V11 , V12 , V13 , V14 , V15 , V16 , V17 , V18 , V19 , V20 , V21 , V22 , V23 , V24 , V25 , V26 , V27 , V28 , Amount , Class FROM creditcardfraud_enahnced c INNER JOIN USERS u on c.userid = u.userid WHERE V1 > 5 AND V2 IS NOT NULL AND u.CITY LIKE 'Premium%';

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