Let’s Do Data Lineage in Kafka, Flink and Druid by Tracking Aircraft!

Agenda ● ● ● ● ● ● ● ● Streaming Data Governance in Streaming Analytics Kafka Headers: A lesser known feature Stream ETL with Flink using Kafka Headers Quick intro to Apache Druid - A Streaming Analytics Database Tracking Aircraft Radar data with Raspberry Pi Let’s put it all together Live Demo Q&A ©2022, Imply

Streaming Data Governance Data governance is a collection of standards, processes, roles, and metrics that ensure that data is usable, accessible, and effective. Data governance with respect to streaming is about ● ● ● Stream Quality Stream Catalog Stream Lineage Today, let’s shine a light on the Lineage aspect! ©2022, Imply

KFD Stack - Kafka Flink Druid Data sources Stream ETL Stream Processor µService Data/Event driven Apps Custom visualizations Streams Database CDC Event Streaming Infrastructure Event Analytics Infrastructure Files Realtime Analytics Messaging BI tools App data Databases Data Lake Root-cause analysis Dashboards & reports

Kafka Headers: A lesser known feature Record headers give you the ability to add some metadata about the Kafka record. They are collections of arbitrary key/value pairs. Kafka views headers as Kafka headers are key-value pairs, where the header key is a java.lang.String type and the header value is a byte array. Flink models headers as a MAP<BYTES, BYTES> virtual (metadata) column. Use DECODE/ENCODE with known character set (usually UTF-8) if you need to process the string values Druid can read Kafka record headers natively: ● Autogenerate table columns from header keys. For ingestion, you specify a prefix to create column names ● For decoding the values, specify the character set ©2022, Imply

Stream ETL with Flink using Kafka Headers Flink views Kafka headers as metadata columns CREATE TABLE adsb-raw ( kafka_timestamp TIMESTAMP_LTZ(3) METADATA FROM ‘timestamp’, kafka_headers MAP<BYTES, BYTES> NOT NULL METADATA FROM ‘headers’, kafka_key STRING, val STRING ) WITH ( ‘connector’ = ‘kafka’, ‘topic’ = ‘adsb-raw’, ‘key.format’ = ‘csv’, ‘key.fields’ = ‘kafka_key’, ‘key.fields-prefix’ = ‘kafka_’, ‘value.format’ = ‘raw’, … ); Metadata columns can be read and written (unless declared virtual) ©2022, Imply

Apache Druid - A Streaming Analytics Database For analytics applications that require: 1 Sub-second queries at any scale 2 High concurrency at the lowest cost 3 Real-time and historical insights Interactive analytics on TB-PBs of data 100s to 1000s QPS via a highly eﬃcient engine True stream ingestion for Kafka and Kinesis Plus, non-stop reliability with automated fault tolerance and continuous backup ©2022, Imply

KFD Stack - Kafka Flink Druid Data sources Stream ETL Stream Processor µService Data/Event driven Apps Custom visualizations Streams Database CDC Event Streaming Infrastructure Event Analytics Infrastructure Files Realtime Analytics Messaging BI tools App data Databases Data Lake Root-cause analysis Dashboards & reports

Tracking Aircraft Radar data with Raspberry Pi #!/bin/bash CC_BOOTSTRAP=”<confluent cloud bootstrap server>” CC_APIKEY=”<api key>” CC_SECRET=”<secret>” CC_SECURE=”-X security.protocol=SASL_SSL -X sasl.mechanism=PLAIN -X sasl.username=${CC_APIKEY} -X sasl.password=${CC_SECRET}” CLIENT_ID=”<client id>” CLIENT_TIMEZONE=$(date +”%Z”) LON=”0.0” LAT=”0.0” TOPIC_NAME=”adsb-raw” nc localhost 30003 \ | awk -F “,” ‘{ print $5 “|” $0 }’ \ | kcat -P \ -t ${TOPIC_NAME} \ -b ${CC_BOOTSTRAP} \ -H “ClientID=${CLIENT_ID}” \ -H “ClientTimezone=${CLIENT_TIMEZONE}” \ -H “ReceiverLon=${LON}” \ -H “ReceiverLat=${LAT}” \ -K “|” \ ${CC_SECURE} ©2022, Imply 11

Let’s put it all together Delivery Processing Storage Query Visualisation Analytics Pipeline Data Production Aircraft transponder data (ADS-B) 12 Kafka as an event streaming platform Preprocessing with Apache Flink:

Track provenance using header fields
Filter - Enrich - Transform - Process header fields Apache Druid Highly scalable, built in DR - Native Kafka connectivity - Analyze Kafka header fields Apache Druid Imply Pivot - Analytical queries against realtime, detail data a data exploration and adhoc analytics GUI for Druid ©2022, Imply

Learnings ● ● ● ● ● Lineage is a crucial part of Data Governance We can track lineage by adding metadata to each data record One way to do this is by using Kafka record headers The KFD (Kafka Flink Druid) stack supports tracking data lineage with Kafka headers end-to-end Don’t be scared of “enterprisey” things, you can try this at home with public data sources such as aircraft data! ©2022, Imply 14

Resources ● ● ● ● Streaming Governance training by Confluent: https://developer.confluent.io/courses/governing-data-streams/overview/ Catalogs in Flink SQL (Decodable blog): https://www.decodable.co/blog/catalogs-in-flink-sql-a-primer Kafka headers: https://www.confluent.io/blog/5-things-every-kafka-developer-should-know/#tip -5-record-headers Github repo: https://github.com/hellmarbecker/plt-airt-2000 ©2022, Imply 17

Let’s Do Data Lineage in Kafka, Flink and Druid by Tracking Aircraft!

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