A presentation at Confluent VUG by Robin Moffatt
The Changing Face of ETL Event-Driven Architectures for Data Engineers Photo by rmoff @rmoff
Photo by Samuel Sianipar on Unsplash
Photo by Khai Sze Ong on Unsplash
Photo by Rainier Ridao on Unsplash
Photo by Rohit Tandon on Unsplash
Photo by Theodore Moore on Unsplash
Photo by Cristian Grecu on Unsplash
Photo by Patrick Fore on Unsplash It used to be so simple @rmoff | #ConfluentVUG | @confluentinc
Photo by Eugenio Mazzone on Unsplash More More Sources Sources @rmoff | #ConfluentVUG | @confluentinc
Photo by Tom Barrett on Unsplash More Targets More Targets @rmoff | #ConfluentVUG | @confluentinc
Photo by Kirill on Unsplash More Data More Data @rmoff | #ConfluentVUG | @confluentinc
Batches and Buckets @rmoff | #ConfluentVUG | @confluentinc
[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
@rmoff | #ConfluentVUG | @confluentinc
[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
$ 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
Photo by Mark Kamalov on Unsplash Events
“ An event is both: ✴ Notification ✴ State transfer @rmoff | #ConfluentVUG | @confluentinc
A Customer Experience @rmoff | #ConfluentVUG | @confluentinc
A Sensor Reading @rmoff | #ConfluentVUG | @confluentinc
Databases @rmoff | #ConfluentVUG | @confluentinc
Table Time The Stream/Table Duality Stream Account ID Balance 12345 €50 Account ID Amount 12345 + €50 12345
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
Events Basket Bread Tinned Spaghetti @rmoff | #ConfluentVUG | @confluentinc
Events Basket Bread ItemAdd Bread @rmoff | #ConfluentVUG | @confluentinc
Events Basket Bread ItemAdd ItemAdd Bread Baked Beans Baked Beans @rmoff | #ConfluentVUG | @confluentinc
Events Basket Bread ItemAdd ItemAdd ItemRemove Bread Baked Beans Baked Beans @rmoff | #ConfluentVUG | @confluentinc
Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc
Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc
Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc
Events Basket Bread ItemAdd ItemAdd ItemRemove ItemAdd Bread Baked Beans Baked Beans Tinned Spaghetti @rmoff | #ConfluentVUG Tinned Spaghetti | @confluentinc
What is an Event Streaming Platform? Producer Connectors Consumer The Log Connectors Streaming Engine @rmoff | #ConfluentVUG | @confluentinc
Immutable Event Log New Old Messages are added at the end of the log @rmoff | #ConfluentVUG | @confluentinc
Topics Clicks Orders Customers Topics are similar in concept to tables in a database @rmoff | #ConfluentVUG | @confluentinc
Partitions Clicks p0 P1 P2 Messages are guaranteed to be strictly ordered within a partition @rmoff | #ConfluentVUG | @confluentinc
Messages are just K/V bytes plus headers + timestamp Clicks Header Timestamp Key Value @rmoff | #ConfluentVUG | @confluentinc
Serialisation & Schemas JSON Avro Protobuf Schema JSON CSV 👍 👍 👍 😬 https://rmoff.dev/qcon-schemas @rmoff | #DataXDays | @confluentinc
Consumers have a position all of their own New Old Sally is here @rmoff | Scan #ConfluentVUG | @confluentinc
Consumers have a position all of their own New Old Fred is here Sally is here Scan @rmoff | Scan #ConfluentVUG | @confluentinc
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
The Connect API Producer Connectors Consumer The Log Connectors Streaming Engine @rmoff | #ConfluentVUG | @confluentinc
Streaming Integration with Kafka Connect syslog Sources Tasks Workers @rmoff | Kafka Connect Kafka Brokers #ConfluentVUG | @confluentinc
Streaming Integration with Kafka Connect Amazon S3 Google BigQuery Sinks Tasks Workers @rmoff | Kafka Connect Kafka Brokers #ConfluentVUG | @confluentinc
Streaming Integration with Kafka Connect Amazon S3 syslog Google BigQuery Tasks Workers @rmoff | Kafka Connect Kafka Brokers #ConfluentVUG | @confluentinc
Stream Processing in Kafka Producer Connectors Consumer The Log Connectors Streaming Engine @rmoff | #ConfluentVUG | @confluentinc
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
Stream Processing with ksqlDB CREATE STREAM completedOrders AS SELECT * FROM orders WHERE status=’COMPLETE’; @rmoff | #ConfluentVUG | @confluentinc
Photo by Ash from Modern Afflatus on Unsplash This is Something New @rmoff | #ConfluentVUG | @confluentinc
Events in Action Review events reviews @rmoff | #ConfluentVUG | @confluentinc
Events in Action Review events reviews Operational dashboard @rmoff | #ConfluentVUG | @confluentinc
Events in Action Review events reviews Operational dashboard Data lake @rmoff | #ConfluentVUG | @confluentinc
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
Events in Action Existing apps User data users Kafka Connect RDBMS txn log Kafka @rmoff | #ConfluentVUG | @confluentinc
Events in Action Review events reviews users reviews_clean Operational dashboard User data Data lake @rmoff | #ConfluentVUG | @confluentinc
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
Events in Action Notification service Review events Operational dashboard User data Data lake @rmoff | #ConfluentVUG | @confluentinc
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
Photo by rmoff The Power of an Event-Driven Architecture
Not Everything is a Nail Events RDBMS @rmoff | #ConfluentVUG | @confluentinc
Not Everything is a Nail Events RDBMS @rmoff | #ConfluentVUG | @confluentinc
Not Everything is a Nail Events Elasticsearch RDBMS @rmoff | #ConfluentVUG | @confluentinc
Not Everything is a Nail Graph Events Elasticsearch RDBMS @rmoff | #ConfluentVUG | @confluentinc
Side-by-Side Tech Evaluation Events HDFS @rmoff | #ConfluentVUG | @confluentinc
Side-by-Side Tech Evaluation Events BiqQuery HDFS @rmoff | #ConfluentVUG | @confluentinc
Side-by-Side Tech Evaluation Snowflake Events BiqQuery HDFS @rmoff | #ConfluentVUG | @confluentinc
Evolve Data Sources Producer Consuming App A Onpremises Consuming App B @rmoff | #ConfluentVUG | @confluentinc
Evolve Data Sources Producer Consuming App A Onpremises Consuming App B Producer Cloud @rmoff | #ConfluentVUG | @confluentinc
Evolve Data Sources Consuming App A Consuming App B Producer Cloud @rmoff | #ConfluentVUG | @confluentinc
Tight Coupling != Flexible Orders RDBMS @rmoff | #ConfluentVUG | @confluentinc
Tight Coupling != Flexible Orders RDBMS @rmoff HDFS | #ConfluentVUG | @confluentinc
Tight Coupling != Flexible Orders RDBMS HDFS App @rmoff | #ConfluentVUG | @confluentinc
Loose Coupling == Freedom to Evolve RDBMS Orders @rmoff | #ConfluentVUG | @confluentinc
Loose Coupling == Freedom to Evolve RDBMS Orders HDFS @rmoff | #ConfluentVUG | @confluentinc
Loose Coupling == Freedom to Evolve RDBMS Orders App HDFS @rmoff | #ConfluentVUG | @confluentinc
Transform Once, Use Many: Data Cleansing temp_raw App IoT App RDBMS @rmoff | #ConfluentVUG | @confluentinc
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
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
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
Transform Once, Use Many: Data Enrichment RDBMS App 01 Events Join @rmoff | #ConfluentVUG | @confluentinc
Transform Once, Use Many: Data Enrichment RDBMS App 01 Events Join Elasticsearch App 02 Join @rmoff | #ConfluentVUG | @confluentinc
Transform Once, Use Many: Data Enrichment App 01 Events Elasticsearch RDBMS Join @rmoff | #ConfluentVUG | @confluentinc
Message Payload Compatibility Producer Consuming App @rmoff | #ConfluentVUG | @confluentinc
Message Payload Compatibility Producer Consuming App Producer @rmoff | #ConfluentVUG | @confluentinc
Message Payload Compatibility Producer Consuming App Producer Triangles to Squares @rmoff | #ConfluentVUG | @confluentinc
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
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
Photo by rmoff Say NO to brittle pipelines
App App App App cache monitoring cache MQ MQ security DWH search Hadoop @rmoff | #ConfluentVUG | @confluentinc
App App App App request-response changelogs App App KAFKA App App DWH Hadoop @rmoff | messaging OR stream processing streaming data pipelines #ConfluentVUG | @confluentinc
Photo by rmoff Events model the real world
Event streaming platform Photo by rmoff Native stream processing Data when you need it Data persistence Flexibility & scalability
on Photo by Want to learn more? CTAs, not CATs (sorry, not sorry)
Free Books! https://rmoff.dev/q2m @rmoff | #ConfluentVUG | @confluentinc
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.
Learn Kafka. Start building with Apache Kafka at Confluent Developer. developer.confluent.io
Confluent Community Slack group cnfl.io/slack @rmoff | #ConfluentVUG | @confluentinc
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
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 / partners@confluent.io) can help with introductions on a given sales op @rmoff | #ConfluentVUG | @confluentinc
Data integration in architectures built on static, update-in-place datastores inevitably end up with pathologically high degrees of coupling and poor scalability. This has been the standard practice for decades, as we attempt to build data pipelines on top of databases that do a poor job modelling the fundamental objects that drive our businesses and systems: events.
Events carry both notification and state, and form a powerful primitive on which to build systems for developers and data engineers alike. Developers benefit from the asynchronous communication that events enable between services, and data engineers benefit from the integration capabilities. Everyone gains from using the standards-based, scalable and resilient streaming platform.
In this talk, we’ll discuss the concepts of events, their relevance to both software engineers and data engineers and their ability to unify architectures in a powerful way. We’ll see how stream processing makes sense in both a microservices and ETL environment, and why analytics, data integration and ETL fit naturally into a streaming world. The talk will conclude with a hands-on demonstration of these concepts in practice using Apache Kafka and commentary on the design choices made.
Join this talk to learn:
The following resources were mentioned during the presentation or are useful additional information.
Fully Managed Apache Kafka, Schema Registry, ksqlDB, and Connectors.
Kafka 101 - introducing the concepts, the APIs, and the ecosystem
Free eBooks to download, including Kafka: The Definitive Guide.
Tutorials, videos, blogs, podcasts, and more - all for developers working with Apache Kafka and Confluent Platform
Huge list of connectors for Kafka Connect
A fun blog showing what you can do with ksqlDB and Kafka
Learn all about ksqlDB in this 45 minute talk & live demo
Learn all about Kafka Connect (including the connectors available with ksqlDB)
Apache Kafka and Confluent Platform in Action! Using live streams of rail movement data in all sorts of useful ways for analysis and applications.
Here’s what was said about this presentation on social media.
