The Changing Face of ETL: Event-Driven Architectures for Data Engineers

A presentation at Confluent VUG in July 2020 in by Robin Moffatt

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The Changing Face of ETL Event-Driven Architectures for Data Engineers Photo by rmoff @rmoff

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Photo by Samuel Sianipar on Unsplash

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Photo by Khai Sze Ong on Unsplash

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Photo by Rainier Ridao on Unsplash

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Photo by Rohit Tandon on Unsplash

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Photo by Theodore Moore on Unsplash

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Photo by Cristian Grecu on Unsplash

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Photo by Patrick Fore on Unsplash It used to be so simple @rmoff | #ConfluentVUG | @confluentinc

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Photo by Eugenio Mazzone on Unsplash More More Sources Sources @rmoff | #ConfluentVUG | @confluentinc

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Photo by Tom Barrett on Unsplash More Targets More Targets @rmoff | #ConfluentVUG | @confluentinc

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Photo by Kirill on Unsplash More Data More Data @rmoff | #ConfluentVUG | @confluentinc

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Batches and Buckets @rmoff | #ConfluentVUG | @confluentinc

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[paint a better picture - technology] zoom Analytics Applications Tell Us What Happened Respond Photo by Deva Darshan from Pexels → an order was placed! → how many orders were placed @rmoff | #ConfluentVUG | @confluentinc

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@rmoff | #ConfluentVUG | @confluentinc

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[paint a better picture - technology] zoom Photo by NASA on Unsplash • <city view from above> • It’s the same thing that happened. It’s the same piece of data. we just want different things from it • apps -> respond to something happening (an order was placed!) • analytics -> tell us what happened (how many orders were placed?) • Historically, technology was such you had to. OLTP/OLAP was a compromise; you can have quick data in or quick data out : choose one. • Batch ETL was the inevitable sticking plaster on top of that. Whilst you only had a few systems inhouse from which to get data and one to write it to this didn’t matter. But that’s no longer the case • This isn’t about a compromise, about crowbaring everything into a new shiny technology that I’ve found • this is about adopting a unified platform that enables BOTH apps and analytics to be better (lower latency, more flexible architecture, more scalable) • this is all enabled through events, implemented on a highly scalable, distributed technology with huge integration capabilities and universally-supported API @rmoff | #ConfluentVUG | @confluentinc

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$ whoami • Robin Moffatt (@rmoff) • Senior Developer Advocate at Confluent (Apache Kafka, not Wikis 😉) • Working in data & analytics since 2001 • Oracle ACE Director (Alumnus) http://rmoff.dev/talks · http://rmoff.dev/blog · http://rmoff.dev/youtube @rmoff | #ConfluentVUG | @confluentinc

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Photo by Mark Kamalov on Unsplash Events

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“ An event is both: ✴ Notification ✴ State transfer @rmoff | #ConfluentVUG | @confluentinc

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A Customer Experience @rmoff | #ConfluentVUG | @confluentinc

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A Sensor Reading @rmoff | #ConfluentVUG | @confluentinc

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Databases @rmoff | #ConfluentVUG | @confluentinc

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Table Time The Stream/Table Duality Stream Account ID Balance 12345 €50 Account ID Amount 12345 + €50 12345

€25 12345 -€60 Account ID Balance 12345 €75 Account ID Balance 12345 €15 @rmoff | #ConfluentVUG | @confluentinc

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The truth is the log. The database is a cache of a subset of the log. —Pat Helland Immutability Changes Everything http://cidrdb.org/cidr2015/Papers/CIDR15_Paper16.pdf Photo by Bobby Burch on Unsplash @rmoff | #ConfluentVUG | @confluentinc

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Events Basket Bread Tinned Spaghetti @rmoff | #ConfluentVUG | @confluentinc

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Events Basket Bread ItemAdd Bread @rmoff | #ConfluentVUG | @confluentinc

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Events Basket Bread ItemAdd ItemAdd Bread Baked Beans Baked Beans @rmoff | #ConfluentVUG | @confluentinc

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Events Basket Bread ItemAdd ItemAdd ItemRemove Bread Baked Beans Baked Beans @rmoff | #ConfluentVUG | @confluentinc

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Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc

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Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc

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Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc

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Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc

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What is an Event Streaming Platform? Producer Connectors Consumer The Log Connectors Streaming Engine @rmoff | #ConfluentVUG | @confluentinc

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Immutable Event Log New Old Messages are added at the end of the log @rmoff | #ConfluentVUG | @confluentinc

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Topics Clicks Orders Customers Topics are similar in concept to tables in a database @rmoff | #ConfluentVUG | @confluentinc

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Partitions Clicks p0 P1 P2 Messages are guaranteed to be strictly ordered within a partition @rmoff | #ConfluentVUG | @confluentinc

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Messages are just K/V bytes plus headers + timestamp Clicks Header Timestamp Key Value @rmoff | #ConfluentVUG | @confluentinc

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Serialisation & Schemas JSON Avro Protobuf Schema JSON CSV 👍 👍 👍 😬 https://rmoff.dev/qcon-schemas @rmoff | #DataXDays | @confluentinc

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Consumers have a position all of their own New Old Sally is here @rmoff | Scan #ConfluentVUG | @confluentinc

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Consumers have a position all of their own New Old Fred is here Sally is here Scan @rmoff | Scan #ConfluentVUG | @confluentinc

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Consumers have a position all of their own George is here Scan New Old Fred is here Sally is here Scan @rmoff | Scan #ConfluentVUG | @confluentinc

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The Connect API Producer Connectors Consumer The Log Connectors Streaming Engine @rmoff | #ConfluentVUG | @confluentinc

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Streaming Integration with Kafka Connect syslog Sources Tasks Workers @rmoff | Kafka Connect Kafka Brokers #ConfluentVUG | @confluentinc

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Streaming Integration with Kafka Connect Amazon S3 Google BigQuery Sinks Tasks Workers @rmoff | Kafka Connect Kafka Brokers #ConfluentVUG | @confluentinc

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Streaming Integration with Kafka Connect Amazon S3 syslog Google BigQuery Tasks Workers @rmoff | Kafka Connect Kafka Brokers #ConfluentVUG | @confluentinc

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Stream Processing in Kafka Producer Connectors Consumer The Log Connectors Streaming Engine @rmoff | #ConfluentVUG | @confluentinc

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Kafka Streams API final StreamsBuilder builder = new StreamsBuilder() .stream(“orders”, Consumed.with(stringSerde, ordersSerde)) .filter( (key, order) -> order.getStatus().equals(“COMPLETE”) ) .to(“complete_orders”, Produced.with(stringSerde, ordersSerde)); @rmoff | #ConfluentVUG | @confluentinc

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Stream Processing with ksqlDB CREATE STREAM completedOrders AS SELECT * FROM orders WHERE status=’COMPLETE’; @rmoff | #ConfluentVUG | @confluentinc

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Photo by Ash from Modern Afflatus on Unsplash This is Something New @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Review events reviews @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Review events reviews Operational dashboard @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Review events reviews Operational dashboard Data lake @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Review events CREATE STREAM reviews_clean AS SELECT * FROM reviews WHERE id IS NOT NULL; reviews reviews_clean Operational dashboard Data lake Filter out bad data @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Existing apps User data users Kafka Connect RDBMS txn log Kafka @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Review events reviews users reviews_clean Operational dashboard User data Data lake @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Review events CREATE CREATE SELECT SELECT STREAM enriched_reviews AS STREAM reviews_clean AS ** FROM reviews_clean r FROM reviews INNER JOIN users u WHERE id IS NOT NULL ON r.userid=u.userid; reviews users reviews_clean enriched_reviews Operational dashboard User data Data lake Join events to users, and filter @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Notification service Review events Operational dashboard User data Data lake @rmoff | #ConfluentVUG | @confluentinc

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Events in Action Review events CREATE STREAM unhappy_vips AS SELECT * FROM enriched_reviews WHERE rating Notification< 3 AND status = ‘Platinum’; service reviews users reviews_clean enriched_reviews Operational dashboard unhappy_vips User data Data lake Join events to users, and filter @rmoff | #ConfluentVUG | @confluentinc

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Photo by rmoff The Power of an Event-Driven Architecture

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Not Everything is a Nail Events RDBMS @rmoff | #ConfluentVUG | @confluentinc

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Not Everything is a Nail Events RDBMS @rmoff | #ConfluentVUG | @confluentinc

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Not Everything is a Nail Events Elasticsearch RDBMS @rmoff | #ConfluentVUG | @confluentinc

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Not Everything is a Nail Graph Events Elasticsearch RDBMS @rmoff | #ConfluentVUG | @confluentinc

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Side-by-Side Tech Evaluation Events HDFS @rmoff | #ConfluentVUG | @confluentinc

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Side-by-Side Tech Evaluation Events BiqQuery HDFS @rmoff | #ConfluentVUG | @confluentinc

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Side-by-Side Tech Evaluation Snowflake Events BiqQuery HDFS @rmoff | #ConfluentVUG | @confluentinc

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Evolve Data Sources Producer Consuming App A Onpremises Consuming App B @rmoff | #ConfluentVUG | @confluentinc

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Evolve Data Sources Producer Consuming App A Onpremises Consuming App B Producer Cloud @rmoff | #ConfluentVUG | @confluentinc

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Evolve Data Sources Consuming App A Consuming App B Producer Cloud @rmoff | #ConfluentVUG | @confluentinc

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Tight Coupling != Flexible Orders RDBMS @rmoff | #ConfluentVUG | @confluentinc

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Tight Coupling != Flexible Orders RDBMS @rmoff HDFS | #ConfluentVUG | @confluentinc

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Tight Coupling != Flexible Orders RDBMS HDFS App @rmoff | #ConfluentVUG | @confluentinc

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Loose Coupling == Freedom to Evolve RDBMS Orders @rmoff | #ConfluentVUG | @confluentinc

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Loose Coupling == Freedom to Evolve RDBMS Orders HDFS @rmoff | #ConfluentVUG | @confluentinc

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Loose Coupling == Freedom to Evolve RDBMS Orders App HDFS @rmoff | #ConfluentVUG | @confluentinc

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Transform Once, Use Many: Data Cleansing temp_raw App IoT App RDBMS @rmoff | #ConfluentVUG | @confluentinc

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Transform Once, Use Many: Data Cleansing sensor_id time_epoch 42 1551136074 42 1551136125 1551136125 42 1551138129 reading 13.05 13.11 13.11 13.04 temp_raw App IoT App RDBMS @rmoff | #ConfluentVUG | @confluentinc

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Transform Once, Use Many: Data Cleansing sensor_id time_epoch 42 1551136074 42 1551136125 1551136125 42 1551138129 reading 13.05 13.11 13.11 13.04 temp_raw Cleanse App IoT App Cleanse RDBMS Cleanse @rmoff | #ConfluentVUG | @confluentinc

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Transform Once, Use Many: Data Cleansing sensor_id time_epoch 42 1551136074 42 1551136125 1551136125 42 1551138129 reading 13.05 13.11 13.11 13.04 temp_clean sensor_id 42 42 42 App IoT time_epoch 1551136074 1551136125 1551138129 reading 13.05 13.11 13.04 App RDBMS temp_raw SENSOR_ID IS NOT NULL @rmoff | #ConfluentVUG | @confluentinc

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Transform Once, Use Many: Data Enrichment RDBMS App 01 Events Join @rmoff | #ConfluentVUG | @confluentinc

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Transform Once, Use Many: Data Enrichment RDBMS App 01 Events Join Elasticsearch App 02 Join @rmoff | #ConfluentVUG | @confluentinc

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Transform Once, Use Many: Data Enrichment App 01 Events Elasticsearch RDBMS Join @rmoff | #ConfluentVUG | @confluentinc

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Message Payload Compatibility Producer Consuming App @rmoff | #ConfluentVUG | @confluentinc

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Message Payload Compatibility Producer Consuming App Producer @rmoff | #ConfluentVUG | @confluentinc

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Message Payload Compatibility Producer Consuming App Producer Triangles to Squares @rmoff | #ConfluentVUG | @confluentinc

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Build Resilient Pipelines with Schemas COL1 ID INT COL2 NAME VARCHAR sales_csv Apply schema App 01 COL1 ID INT COL2 NAME VARCHAR Producer App 02 @rmoff | Apply schema #ConfluentVUG | @confluentinc

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Build Resilient Pipelines with Schemas Schema Registry sales App 01 Producer App 02 sales_csv COL1 ID INT Apply schema COL2 NAME VARCHAR @rmoff | #ConfluentVUG | @confluentinc

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Photo by rmoff Say NO to brittle pipelines

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App App App App cache monitoring cache MQ MQ security DWH search Hadoop @rmoff | #ConfluentVUG | @confluentinc

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App App App App request-response changelogs App App KAFKA App App DWH Hadoop @rmoff | messaging OR stream processing streaming data pipelines #ConfluentVUG | @confluentinc

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Photo by rmoff Events model the real world

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Event streaming platform Photo by rmoff Native stream processing Data when you need it Data persistence Flexibility & scalability

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on Photo by Want to learn more? CTAs, not CATs (sorry, not sorry)

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Free Books! https://rmoff.dev/q2m @rmoff | #ConfluentVUG | @confluentinc

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60 DE VA DV $50 USD off your bill each calendar month for the first three months when you sign up https://rmoff.dev/ccloud Free money! (additional $60 towards your bill 😄 ) Fully Managed Kafka as a Service * Limited availability. Activate by 11th September 2020. Expires after 90 days of activation. Any unused promo value on the expiration date will be forfeited.

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Learn Kafka. Start building with Apache Kafka at Confluent Developer. developer.confluent.io

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Confluent Community Slack group cnfl.io/slack @rmoff | #ConfluentVUG | @confluentinc

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Further reading / watching • Kafka as a Platform: the Ecosystem from the Ground Up http://rmoff.dev/youtube • https://rmoff.dev/kafka101 • Apache Kafka and ksqlDB in Action: Let’s Build a Streaming Data Pipeline! • https://rmoff.dev/ljc-kafka-01 • From Zero to Hero with Kafka Connect • https://rmoff.dev/ljc-kafka-02 • Introduction to ksqlDB • https://rmoff.dev/ljc-kafka-03 • Integrating Oracle and Kafka • https://rmoff.dev/oracle-and-kafka • The Changing Face of ETL: Event-Driven Architectures for Data Engineers • https://rmoff.dev/oredev19-changing-face-of-etl • 🚂On Track with Apache Kafka: Building a Streaming Platform solution with Rail Data • https://rmoff.dev/oredev19-on-track-with-kafka @rmoff | #ConfluentVUG | @confluentinc

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Resources #EOF • CDC Spreadsheet • Blog: No More Silos: How to Integrate your Databases with Apache Kafka and CDC • #partner-engineering on Slack for questions • BD team (#partners / [email protected]) can help with introductions on a given sales op @rmoff | #ConfluentVUG | @confluentinc