A presentation at ACEs @ Home by Robin Moffatt
Integrating Oracle and Kafka Robin Moffatt | #ACEsAtHome | @rmoff
$ 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 | #ACEsAtHome | @confluentinc
Kafka is an Event Streaming Platform App App App App request-response changelogs App messaging App KAFKA App OR stream App DWH Hadoop @rmoff | processing streaming data pipelines #ACEsAtHome | @confluentinc
What is an Event Streaming Platform? Producer Connectors Consumer The Log Connectors Streaming Engine @rmoff | #ACEsAtHome | @confluentinc
Immutable Event Log Old New Messages are added at the end of the log @rmoff | #ACEsAtHome | @confluentinc
Topics Clicks Orders Customers Topics are similar in concept to tables in a database @rmoff | #ACEsAtHome | @confluentinc
Partitions Clicks p0 P1 P2 Messages are guaranteed to be strictly ordered within a partition @rmoff | #ACEsAtHome | @confluentinc
Messages are just K/V bytes plus headers + timestamp Clicks Header Timestamp Key Value @rmoff | #ACEsAtHome | @confluentinc
Serialisation & Schemas JSON Avro Protobuf Schema JSON CSV 👍 👍 👍 😬 https://qconnewyork.com/system/files/presentation-slides/qcon_17_-_schemas_and_apis.pdf @rmoff | #ACEsAtHome | @confluentinc
Consumers have a position all of their own Old New Sally Scan is here @rmoff | #ACEsAtHome | @confluentinc
Consumers have a position all of their own Old New Fred Sally Scan is here Scan is here @rmoff | #ACEsAtHome | @confluentinc
Consumers have a position all of their own George Scan is here Old New Fred Sally Scan is here Scan is here @rmoff | #ACEsAtHome | @confluentinc
Free Books! https://rmoff.dev/aces @rmoff | #ACEsAtHome | @confluentinc
Database Offload Amazon S3 RDBMS <x> RDBMS <y> @rmoff | #ACEsAtHome | @confluentinc
Evolve processing from old systems to new Existing New App <x> App C D C RDBMS Stream Processing @rmoff | #ACEsAtHome | @confluentinc
Evolve processing from old systems to new Existing New App <x> App New App <y> C D C RDBMS Stream Processing @rmoff | #ACEsAtHome | @confluentinc
Evolve processing from old systems to new Existing New App <x> App New App <y> C D C RDBMS Stream Processing @rmoff | #ACEsAtHome | @confluentinc
Streaming Integration with Kafka Connect syslog Sources Kafka Connect @rmoff | Kafka Brokers #ACEsAtHome | @confluentinc
Streaming Integration with Kafka Connect Amazon Sinks Google Kafka Connect @rmoff | Kafka Brokers #ACEsAtHome | @confluentinc
Streaming Integration with Kafka Connect Amazon syslog Google Kafka Connect @rmoff | Kafka Brokers #ACEsAtHome | @confluentinc
Look Ma, No Code! { “connector.class”: “io.confluent.connect.jdbc.JdbcSourceConnector”, “connection.url”: https://docs.confluent.io/current/ “jdbc:mysql://asgard:3306/demo”, “table.whitelist”: “sales,orders,customers” } @rmoff | #ACEsAtHome | @confluentinc
Extensible Connector Transform(s) @rmoff | Converter #ACEsAtHome | @confluentinc
hub.confluent.io @rmoff | #ACEsAtHome | @confluentinc
Single Kafka Connect node S3 Task #1 JDBC Task #1 JDBC Task #2 Kafka Connect cluster Worker Worker Offsets Config Status @rmoff | #ACEsAtHome | @confluentinc
Kafka Connect - scalable and fault-tolerant S3 Task #1 JDBC Task #1 Kafka Connect cluster JDBC Task #2 Worker Worker Offsets Config Status @rmoff | #ACEsAtHome | @confluentinc
Automatic fault tolerance S3 Task #1 JDBC Task #1 JDBC Task #2 Kafka Connect cluster Worker Worker Offsets Config Status @rmoff | #ACEsAtHome | @confluentinc
Change-Data-Capture (CDC) Query-based Log-based @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC SELECT * FROM my_table WHERE ts_col > previous ts @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC SELECT * @rmoff | FROM my_table WHERE ts_col > previous ts #ACEsAtHome | @confluentinc
Query-based CDC SELECT * FROM my_table WHERE ts_col > previous ts @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC SELECT * @rmoff | FROM my_table WHERE ts_col > previous ts #ACEsAtHome | @confluentinc
Query-based CDC SELECT * FROM my_table WHERE ts_col > previous ts @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC #051024 17:24:13 server id 1 # Position Timestamp
end_log_pos 98 Type Master ID Size Master Pos Flags 0f 01 00 00 00 5e 00 00 00 62 00 00 00 00 00
2d 64 65 62 75 67 2d 6c |..5.0.15.debug.l|
00 00 00 00 00 00 00 00 |og…………..|
00 00 00 00 00 00 00 00 |…………….|
13 38 0d 00 08 00 12 00 |…….C.8……|
00 04 1a @rmoff |…….K…| | #ACEsAtHome | @confluentinc
Log-based CDC Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC Transaction log @rmoff | #ACEsAtHome | @confluentinc
DEMO Photo by Tom Moffatt @rmoff | #ACEsAtHome | @confluentinc
@rmoff | #ACEsAtHome | @confluentinc
Read more: http://cnfl.io/kafka-c Query-based CDC ✅ Usually easier to setup ✅ Requires fewer permissions 🛑 Needs specific columns in source schema to track changes Photo by Matese Fields on Unsplash 🛑 Impact of polling the DB (or higher latencies tradeoff) 🛑 Can’t track deletes 🛑 Can’t track multiple events between polling interval @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC SELECT * @rmoff | FROM my_table WHERE ts_col > previous ts #ACEsAtHome | @confluentinc
Query-based CDC SELECT * CREATE TABLE my_table ( FROM my_table WHERE ts_col > previous ts ID INT, FOO VARCHAR, BAR VARCHAR, WIBBLE VARCHAR, TS_COL TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP ) @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC SELECT * INSERT @rmoff | FROM my_table WHERE ts_col > previous ts #ACEsAtHome | @confluentinc
Query-based CDC INSERT SELECT * FROM my_table WHERE ts_col > previous ts @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC UPDATE @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC UPDATE SELECT * FROM my_table WHERE ts_col > previous ts @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC DELETE @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC DELETE e p o SELECT * FROM my_table WHERE ts_col > previous ts @rmoff | #ACEsAtHome | N @confluentinc
Query-based CDC orderID status address updateTS SELECT * FROM orders WHERE updateTS > previous ts @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC orderID status address updateTS 42 29 Acacia Road 10:54:29 SHIPPED SELECT * FROM orders WHERE updateTS > { previous ts @rmoff | } “orderID”: 42, “status”: “SHIPPED”, “address”: “29 Acacia Road”, “updateTS”: “10:54:29” #ACEsAtHome | @confluentinc
10:54:00 PENDING INSERT INTO orders (orderID, status) VALUES (42, ‘PENDING’); @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC orderID status address 42 1640 Riverside Drive 10:54:20 PENDING updateTS UPDATE orders SET address = ‘1640 Riverside Drive’ WHERE orderID = 42; @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC orderID status address updateTS 42 29 Acacia Road 10:54:25 PENDING UPDATE orders SET address = ‘29 Acacia Road’ WHERE orderID = 42; @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC orderID status address updateTS 42 29 Acacia Road 10:54:29 SHIPPED UPDATE orders SET status = ‘SHIPPED’ WHERE orderID = 42; @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC orderID status orderID address status 42 SHIPPED 42 address 29 PENDING Acacia Road — updateTS updateTS 10:54:29 10:54:00 42 PENDING 1640 Riverside Drive 10:54:20 42 PENDING 29 Acacia Road 10:54:25 42 SHIPPED 29 Acacia Road 10:54:29 @rmoff | #ACEsAtHome | @confluentinc
Query-based CDC orderID status address updateTS 42 29 Acacia Road 10:54:29 SHIPPED SELECT * FROM orders WHERE updateTS > { previous ts @rmoff | } “orderID”: 42, “status”: “SHIPPED”, “address”: “29 Acacia Road”, “updateTS”: “10:54:29” #ACEsAtHome | @confluentinc
@rmoff Event-driven Query-based Log-based
Photo by Sebastian Pociecha on Unsp Log-based CDC ✅ Greater data fidelity ✅ Lower latency ✅ Lower impact on source 🛑 More setup steps 🛑 Higher system privileges required 🛑 For propriatory databases, usually $ $$ Read more: http://cnfl.io/kafka-cdc @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC UPDATE Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC UPDATE Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC DELETE Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC DELETE Transaction log @rmoff | #ACEsAtHome | @confluentinc
Log-based CDC Immutable event log @rmoff | #ACEsAtHome | @confluentinc
tl;dr : which tool do I use? • If you’re wanting to do query-based CDC, the choice is simple: confluent.io/hub @rmoff | #ACEsAtHome | @confluentinc
Which Log-Based CDC Tool? • Open Source RDBMS, e.g. MySQL, PostgreSQL • Debezium • (+ paid options) • Proprietory RDBMS, e.g. Oracle, MS SQL, DB2 • Oracle GoldenGate • Debezium + XStream • Attunity • Mainframe e.g. VSAM, IMS • IBM InfoSphere Data Replication • Attunity • SQData • SQData • HVR • tcVISION • tcVISION @rmoff | #ACEsAtHome | @confluentinc
} “reading_ts”: “2020-02-14T12:19:27Z”, “sensor_id”: “aa-101”, “production_line”: “w01”, “widget_type”: “acme94”, “temp_celcius”: 23, “widget_weight_g”: 100 Photo by Franck V. on Unsplash { @rmoff | #ACEsAtHome | @confluentinc
Streams of events Time @rmoff | #ACEsAtHome | @confluentinc
Stream Processing Stream: widgets Stream: widgets_red @rmoff | #ACEsAtHome | @confluentinc
Stream Processing with ksqlDB Stream: widgets ksqlDB CREATE STREAM widgets_red AS SELECT * FROM widgets WHERE colour=’RED’; Stream: widgets_red @rmoff | #ACEsAtHome | @confluentinc
} “reading_ts”: “2020-02-14T12:19:27Z”, “sensor_id”: “aa-101”, “production_line”: “w01”, “widget_type”: “acme94”, “temp_celcius”: 23, “widget_weight_g”: 100 Photo by Franck V. on Unsplash { @rmoff | #ACEsAtHome | @confluentinc
SELECT * FROM WIDGETS WHERE WEIGHT_G > 120 { SELECT COUNT(*) FROM WIDGETS GROUP BY PRODUCTION_LINE } “reading_ts”: “2020-02-14T12:19:27Z”, “sensor_id”: “aa-101”, “production_line”: “w01”, “widget_type”: “acme94”, “temp_celcius”: 23, “widget_weight_g”: 100 Photo by Franck V. on Unsplash SELECT AVG(TEMP_CELCIUS) AS TEMP FROM WIDGETS GROUP BY SENSOR_ID HAVING TEMP>20 CREATE SINK CONNECTOR dw WITH ( Object store, ‘connector.class’ = ‘S3Connector’, data warehouse, ‘topics’ = ‘widgets’ RDBMS …); @rmoff | #ACEsAtHome | @confluentinc
Stream Processing with ksqlDB Source stream @rmoff | #ACEsAtHome | @confluentinc
Stream Processing with ksqlDB Source stream @rmoff | #ACEsAtHome | @confluentinc
Stream Processing with ksqlDB Source stream @rmoff | #ACEsAtHome | @confluentinc
Stream Processing with ksqlDB Source stream Analytics @rmoff | #ACEsAtHome | @confluentinc
Stream Processing with ksqlDB Source stream Applications / Microservices @rmoff | #ACEsAtHome | @confluentinc
Stream Processing with ksqlDB …SUM(TXN_AMT) GROUP BY AC_ID AC _I D= 42 BA LA NC AC E= _I 94 D= .0 42 0 Source stream Applications / Microservices @rmoff | #ACEsAtHome | @confluentinc
Lookups and Joins with ksqlDB ORDERS @rmoff {“ordertime”: 1560070133853, “orderid”: 67, “itemid”: “Item_9”, “orderunits”: 5} | #ACEsAtHome | @confluentinc
Lookups and Joins with ksqlDB { “id”: “Item_9”, “make”: “Boyle-McDermott”, “model”: “Apiaceae”, “unit_cost”: 19.9 ITEMS } {“ordertime”: 1560070133853, “orderid”: 67, “itemid”: “Item_9”, “orderunits”: 5} ORDERS @rmoff | #ACEsAtHome | @confluentinc
Lookups and Joins with ksqlDB { “id”: “Item_9”, “make”: “Boyle-McDermott”, “model”: “Apiaceae”, “unit_cost”: 19.9 ITEMS ORDERS ksqlDB CREATE STREAM ORDERS_ENRICHED AS SELECT O., I., O.ORDERUNITS * I.UNIT_COST AS TOTAL_ORDER_VALUE, FROM ORDERS O INNER JOIN ITEMS I ON O.ITEMID = I.ID ; @rmoff | } {“ordertime”: 1560070133853, “orderid”: 67, “itemid”: “Item_9”, “orderunits”: 5} #ACEsAtHome | @confluentinc
Lookups and Joins with ksqlDB { “id”: “Item_9”, “make”: “Boyle-McDermott”, “model”: “Apiaceae”, “unit_cost”: 19.9 ITEMS ORDERS ksqlDB CREATE STREAM ORDERS_ENRICHED AS SELECT O., I., O.ORDERUNITS * I.UNIT_COST AS TOTAL_ORDER_VALUE, FROM ORDERS O INNER JOIN ITEMS I ON O.ITEMID = I.ID ; } {“ordertime”: 1560070133853, “orderid”: 67, “itemid”: “Item_9”, “orderunits”: 5} { “ordertime”: 1560070133853, “orderid”: 67, “itemid”: “Item_9”, “orderunits”: 5, “make”: “Boyle-McDermott”, “model”: “Apiaceae”, “unit_cost”: 19.9, “total_order_value”: 99.5 ORDERS_ENRICHED } @rmoff | #ACEsAtHome | @confluentinc
Streams & Tables @rmoff | #ACEsAtHome | @confluentinc
Streams and Tables Kafka topic (k/v bytes) { “event_ts”: “2020-02-17T15:22:00Z”, “person” : “robin”, “location”: “Leeds” } { “event_ts”: “2020-02-17T17:23:00Z”, “person” : “robin”, “location”: “London” } ksqlDB Stream +——————————+———-+————-+ |EVENT_TS |PERSON |LOCATION | +——————————+———-+————-+ |2020-02-17 15:22:00 |robin |Leeds | |2020-02-17 17:23:00 |robin |London | |2020-02-17 22:23:00 |robin |Wakefield| |2020-02-18 09:00:00 |robin |Leeds | Stream: Topic + Schema ksqlDB Table +———-+————-+ |PERSON |LOCATION | +———-+————-+ |robin |Leeds |London |Wakefield| | Table: state for given key Topic + Schema { “event_ts”: “2020-02-17T22:23:00Z”, “person” : “robin”, “location”: “Wakefield” } { “event_ts”: “2020-02-18T09:00:00Z”, “person” : “robin”, “location”: “Leeds” } @rmoff | #ACEsAtHome | @confluentinc
Stateful aggregations in ksqlDB Kafka topic { “event_ts”: “2020-02-17T15:22:00Z”, “person” : “robin”, “location”: “Leeds” } { “event_ts”: “2020-02-17T17:23:00Z”, “person” : “robin”, “location”: “London” } SELECT PERSON, COUNT(*) FROM MOVEMENTS GROUP BY PERSON; SELECT PERSON, COUNT_DISTINCT(LOCATION) FROM MOVEMENTS GROUP BY PERSON; +———-+—————————+ |PERSON | LOCATION_CHANGES | +———-+—————————+ |robin | 4 1 2 3 | +———-+—————————+ |PERSON | UNIQUE_LOCATIONS | +———-+—————————+ |robin | 3 1 2 | { “event_ts”: “2020-02-17T22:23:00Z”, “person” : “robin”, “location”: “Wakefield” } { “event_ts”: “2020-02-18T09:00:00Z”, “person” : “robin”, “location”: “Leeds” } Aggregations can be across the entire input, or windowed (TUMBLING, HOPPING, SESSION) @rmoff | #ACEsAtHome | @confluentinc
Kafka topic { “event_ts”: “2020-02-17T15:22:00Z”, “person” : “robin”, “location”: “Leeds” } { “event_ts”: “2020-02-17T17:23:00Z”, “person” : “robin”, “location”: “London” } CREATE TABLE PERSON_MOVEMENTS AS SELECT PERSON, COUNT_DISTINCT(LOCATION) AS UNIQUE_LOCATIONS, COUNT(*) AS LOCATION_CHANGES FROM MOVEMENTS GROUP BY PERSON; PERSON_ MOVEMENTS Internal ksqlDB state store Stateful aggregations in ksqlDB { “event_ts”: “2020-02-17T22:23:00Z”, “person” : “robin”, “location”: “Wakefield” } { “event_ts”: “2020-02-18T09:00:00Z”, “person” : “robin”, “location”: “Leeds” } @rmoff | #ACEsAtHome | @confluentinc
{ “event_ts”: “2020-02-17T15:22:00Z”, “person” : “robin”, “location”: “Leeds” } { “event_ts”: “2020-02-17T17:23:00Z”, “person” : “robin”, “location”: “London” } { “event_ts”: “2020-02-17T22:23:00Z”, “person” : “robin”, “location”: “Wakefield” } { “event_ts”: “2020-02-18T09:00:00Z”, “person” : “robin”, “location”: “Leeds” } CREATE TABLE PERSON_MOVEMENTS AS SELECT PERSON, COUNT_DISTINCT(LOCATION) AS UNIQUE_LOCATIONS, COUNT(*) AS LOCATION_CHANGES FROM MOVEMENTS GROUP BY PERSON; ksql> SELECT LOCATION_CHANGES, UNIQUE_LOCATIONS FROM PERSON_MOVEMENTS WHERE ROWKEY=’robin’; +————————-+————————-+ |LOCATION_CHANGES |UNIQUE_LOCATIONS | +————————-+————————-+ |3 |3 | Query terminated ksql> PERSON_ MOVEMENTS Internal ksqlDB state store Kafka topic Pull and Push queries in ksqlDB ksql> SELECT LOCATION_CHANGES, UNIQUE_LOCATIONS FROM PERSON_MOVEMENTS WHERE ROWKEY=’robin’; EMIT CHANGES; +————————-+————————-+ |LOCATION_CHANGES |UNIQUE_LOCATIONS | +————————-+————————-+ |1 |1 | |2 |2 | |3 |3 | |4 |3 | Press CTRL-C to interrupt Pull query Push query @rmoff | #ACEsAtHome | @confluentinc
{ “event_ts”: “2020-02-17T15:22:00Z”, “person” : “robin”, “location”: “Leeds” } { “event_ts”: “2020-02-17T17:23:00Z”, “person” : “robin”, “location”: “London” } { “event_ts”: “2020-02-17T22:23:00Z”, “person” : “robin”, “location”: “Wakefield” } { “event_ts”: “2020-02-18T09:00:00Z”, “person” : “robin”, “location”: “Leeds” } CREATE TABLE PERSON_MOVEMENTS AS SELECT PERSON, COUNT_DISTINCT(LOCATION) AS UNIQUE_LOCATIONS, COUNT(*) AS LOCATION_CHANGES FROM MOVEMENTS GROUP BY PERSON; PERSON_ MOVEMENTS Internal ksqlDB state store Kafka topic ksqlDB REST API curl -s -X “POST” “http://localhost:8088/query” \ -H “Content-Type: application/vnd.ksql.v1+json; charset=utf-8” \ -d ‘{“ksql”:”SELECT UNIQUE_LOCATIONS FROM PERSON_MOVEMENTS WHERE ROWKEY=”’robin”’;”}’ {“value”:”3”} @rmoff | #ACEsAtHome | @confluentinc
Pull and Push queries in ksqlDB Pull query Tells you: Point in time value Push query All value changes Exits: Immediately Never @rmoff | #ACEsAtHome | @confluentinc
on Photo by Want to learn more? CTAs, not CATs (sorry, not sorry) @rmoff | #ACEsAtHome | @confluentinc
Free Books! https://rmoff.dev/aces @rmoff | #ACEsAtHome | @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 | #ACEsAtHome | @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 • 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 | #ACEsAtHome | @confluentinc
#EOF @rmoff #ACEsAtHome rmoff.dev/talks
For all its quirks and licence fees, we all love Oracle for what it does. But sometimes we want to get the data out to use elsewhere. Maybe we want to build analytics on it; perhaps we want to drive applications with it; sometimes we might even want to move it to another non-Oracle database—can you imagine that! 😱
With Apache Kafka as our scalable, distributed event streaming platform, we can ingest data from Oracle as a stream of events. We can use Kafka to transform and enrich the events if we want to, even joining them to data from other sources. We can stream the resulting events to target systems, as well as use them to create event-driven microservices.
This talk will show some basics of Kafka and then dive into ingesting data from Oracle into Kafka, applying stream processing with ksqlDB, and then pushing that data to systems including PostgreSQL as well as back into Oracle itself.
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
Try out the demo for yourself - all you need is Docker and Docker Compose.
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.
