The document discusses stream governance and technical features of the Confluent platform, including schema management, data contracts, and the importance of schema adherence to prevent data quality issues. It outlines a roadmap for future developments such as data cataloging and encryption, emphasizing the need for reliable data processing and security measures. The content emphasizes the significance of enforcing data contracts to enhance data trustworthiness and reduce operational complexities.

1. Strumenti e Strategie di Stream
Governance con Confluent Platform
Federico Alberton
Customer Success Technical Architects,
Confluent Italy
Paolo Venturini
Customer Success Technical Architects,
Confluent Italy

2. 11:00 - 11:20
11:20 - 11:40
11:20 - 11:40
11:40 - 11:50
12:00
Stream Governance & Technical features:
- Schema, Schema Registry, Data Contracts
Sessione di Demo
Roadmap & next steps:
- Stream Catalog, Data Portal, Stream Lineage, Client Side Field Level
Encryption
Q&A Session
Chiusura Lavori
Agenda

3. Confluent and Confluent Platform

4. Confluent Data Streaming
Platform
All your data continuously streamed, processed,
governed and shared as a product,
making it instantly valuable, usable, and
trustworthy everywhere.
CONNECT
PROCESS
GOVERN
STREAM
Accounts
Customer
Profile
Purchases
Shipments
Claims Orders
Clickstreams
From Data Mess To Data Products
To Instant Value
Everywhere
CONNECT
Data
Systems
Inventory
Replenishment
Forecasting
…
Custom Apps &
Microservices
Personalization
Recommendation
Fraud
…

5. Confluent Platform
5
https://www.confluent.io/whitepaper/confluent-enterprise-reference-architecture/
Application
Sticky Load Balancer
REST Proxy
Proxy
Kafka Brokers
Broker +
Rebalancer
Quorum Nodes (ZooKeeper or KRaft)
Q Q Q
Proxy
Broker +
Rebalancer
Broker +
Rebalancer
Broker +
Rebalancer
Schema Registry
Leader Follower
Q Q
Confluent
Control Center
Application
Clients
KStreams
pp
Streams
Kafka Connect
Worker +
Connectors
or
Replicator
Microservice
Worker +
Connectors
or
Replicator
ksqlDB
ksqlDB
Server
ksqlDB
Server

6. Data contracts

7. Data producer/owner
- Produce high quality data
- Evolve data safely
- Make data contextualized
and discoverable
- Share data
Data platform team
- Design and offer a self-serve data
streaming platform
- Facilitate the onboarding of
developers and other data users
Data consumer
- Search and discover data
- Understand data
- Trust data
- Consume and build on high
quality data
Remove friction at scale without centralization

8. produces
Kafka without Schema
No structure can cause
many problems
Producer
Consumer
consume
Consumer
Consumers

9. produces
Kafka without Schema
Who is encoding and
decoding?
Producer
Consumer
consume
Consumer
Consumers
Serializer Deserializer

10. Major problems without the schema
Breaking changes Low quality data Security challenges
Data producers and
consumers evolve
independently. Changes to
the data schema (e.g.,
adding a new field,
changing data types) can
break consumers if not
managed properly.
Without a central
mechanism to enforce
schema adherence,
producers may send
malformed or incompatible
data, leading to data quality
issues downstream (Missing
data, Incorrect data, etc )
Sensitive data cannot be
protected properly and can
be accessed by wrong users
/ applications

11. {
"type": "record",
"name": "Membership",
"fields": [
{"name": "start_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "end_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "email", "type": "string"},
{"name": "ssn", "type": "string"}
]
}
Example Avro Schema

12. {
"type": "record",
"name": "Membership",
"fields": [
{"name": "start_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "end_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "email", "type": "string"},
{"name": "ssn", "type": "string"},
{"name": "full_name", "type": ["null", "string"]}
]
}
Change in Structure
Can be disruptive

13. Structure Ignored
Unpredictable outcomes SCHEMA
"start_date", "int"
"end_date", "int"
"email", "string"
"ssn", "string"
{
"start_date": 19358,
"end_date": 19722,
"email": 100
}
// 2023-01-01
// 2023-12-31
INCORRECT DATA TYPE
MISSING SSN FIELD

14. OUT OF ORDER
{
"start_date": 19358,
"end_date": 0,
"email": "john.doe",
"ssn": "fizzbuzz"
}
// 2023-01-01
Domain integrity ignored
Garbage in, garbage out SCHEMA
"start_date", "int"
"end_date", "int"
"email", "string"
"ssn", "string"
INVALID EMAIL
INVALID SSN

15. {
"start_date": 19358,
"end_date": 19722,
"email": "john.doe@example.com",
"ssn": "856-45-6789"
}
Example of a high quality
data
Proper structure and
semantics
// 2023-01-01
// 2023-12-31
SCHEMA
"start_date", "int"
"end_date", "int"
"email", "string"
"ssn", "string"

16. CLEAR TEXT
{
"start_date": 19358,
"end_date": 19722,
"email": "john.doe@example.com",
"ssn": "856-45-6789"
}
Data security gaps
No Data Privacy
// 2023-01-01
// 2023-12-31
SCHEMA
"start_date", "int"
"end_date", "int"
"email", "string"
"ssn", "string"

17. promises trust
produces
Data contract
structure
Producer
Consumer
consume
Consumer
Consumers
Business
Logic
Business
Logic
semantics
rules
Data Contracts is the
answer
Adding structure,
meaning, and policies

18. promises trust
produces
Data Contracts
Schema Registry as the foundation
Producer
Schema Registry
Confluent Kafka
Consumer
consume
Consumer
Consumers

19. Confluent Schema Registry
Schema Repository and more!
1
2
3 1
2
2
1

20. promises trust
produces
Data contract
First step: Define the
structure
Structure is the
foundation of the
contract
Producer
Consumer
consume
Consumer
Consumers
structure

21. {
"type": "record",
"name": "Membership",
"fields": [
{"name": "start_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "end_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "email", "type": "string"},
{"name": "ssn", "type": "string"}
]
}
Example Avro Schema

22. {
"type": "record",
"name": "Membership",
"fields": [
{"name": "start_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "end_date", "type": {"type": "int", "logicalType": "date"}},
{"name": "email", "type": "string"},
{"name": "ssn", "type": "string"},
{"name": "full_name", "type": ["null", "string"]}
]
}
Constant Change
How do we know if this is allowed?

23. Set Compatibility for Schema Evolution
Compatibility enables predictable changes
COMPATIBILITY TYPE ALLOWED CHANGES COMPARED TO UPGRADE FIRST
BACKWARD
- Delete fields
- Add optional fields
Latest version Consumers
BACKWARD_TRANSITIVE
- Delete fields
- Add optional fields
All previous versions Consumers
FORWARD
- Add fields
- Delete optional fields
Latest version Producers
FORWARD_TRANSITIVE
- Add fields
- Delete optional fields
All previous versions Producers
FULL
- Add optional fields
- Delete optional fields
Latest version Either
FULL_TRANSITIVE
- Add optional fields
- Delete optional fields
All previous versions Either
NONE - Any change allowed Not compared Depends

24. {
"schema": "...",
"metadata": {
"properties": {
"owner": "Carol Smith",
"email": "csmith@acme.com",
"gdpr_sensitive": "True",
"retention_period": "24"
}
}
}
Adding Metadata

25. promises trust
produces
Data contract
structure
Proper semantics for data
Enables proper flow of
data
Producer
Consumer
consume
Consumer
Consumers
Business
Logic
Business
Logic
semantics
rules

26. Use Data Contract Rules
Data Quality
Constrains the values of
fields and customize
follow-up actions on
incompatible messages.
Data Encryption
Identifies and Encrypts
the value of a field
based on a tag added
to the field.
Data Transformation
Change the value of a
specific field or an entire
message based on a
condition.
Schema Migration
A Transform rule and
allows otherwise
breaking changes to be
performed on a schema
by adding upgrade and
downgrade rules.

27. // 2023-01-01
// 2023-12-31
{
"start_date": 19358,
"end_date": 19722,
"email": "john.doe@example.com",
"ssn": "856-45-6789"
}
Domain Integrity
Ensuring proper values SCHEMA
"start_date", "int"
"end_date", "int"
"email", "string"
"ssn", "string"

28. Data Quality Rule
{
"schema": "...",
"schemaType": "AVRO",
"ruleSet": {
"domainRules": [{
"name": "validateEmail",
"kind": "CONDITION",
"mode": "WRITE",
"type": "CEL",
"doc": "Rule checks email is well formatted and sends record to a DLQ if not.",
"expr": "Membership.email.matches(r".+@.+..+")",
"onFailure": "DLQ",
"params": {
"dlq.topic": "bad_members"
}
}]
}
}

29. // 2023-01-01
// 2023-12-31
{
"start_date": 19358,
"end_date": 19722,
"email": "for.mat@preserved.enc",
"ssn": "XXX-XX-6789"
}
Data Privacy
Protecting Sensitive Data SCHEMA
"start_date", "int"
"end_date", "int"
"email", "string"
"ssn", "string"

30. Data Encryption Rule
{
"schema": "...",
"schemaType": "AVRO",
"metadata": {
"tags": {
"Membership.email": ["PII"]
}
},
"ruleSet": {
"domainRules": [{
"name": "encryptPII",
"type": "ENCRYPT",
"doc": "Rule encrypts every field tagged as PII. ",
"tags": ["PII"],
"params": {
"encrypt.kek.name": "ce581594-3115-486e-b391-5ea874371e73",
"encrypt.kms.type": "aws-kms",
"encrypt.kms.key.id": "arn:aws:kms:us-east-1:586051073099:key/ce58..."
}
}]
}
}
On Roadmap

31. Data Transformation Rule
{
"schema": "...",
"schemaType": "AVRO",
"ruleSet": {
"domainRules": [{
"name": "populateDefaultSSN",
"kind": "TRANSFORM",
"type": "CEL_FIELD",
"doc": "Rule checks if ssn is empty and replaces it with 'unspecified' if it is.",
"mode": "WRITE",
"expr": "name == 'ssn' ; value == '' ? 'unspecified' : value"
}]
}
}

32. Schema Migration Rule
{
"schema": "...",
"schemaType": "AVRO",
"ruleSet": {
"migrationRules": [{
"name": "changeSsnToSocialSecurityNumber",
"kind": "TRANSFORM",
"type": "JSONATA",
"doc": "Consumer is on new major version and gets socialSecurityNumber while producer sends ssn.",
"mode": "UPGRADE",
"expr": "$merge([$sift($, function($v, $k) {$k != 'ssn'}), {'socialSecurityNumber': $.'ssn'}])"
}, {
"name": "changeSocialSecurityNumberToSsn",
"kind": "TRANSFORM",
"type": "JSONATA",
"doc": "Consumer is on old major version and gets ssn while producer sends socialSecurityNumber.",
"mode": "DOWNGRADE",
"expr": "$merge([$sift($, function($v, $k) {$k != 'socialSecurityNumber'}), {'ssn':
$.'socialSecurityNumber'}])"
}]
}

33. contract
validation
trust
Data Contracts
Shift-left and ensure high quality data
Producer
Schema Registry
Confluent Kafka
Consumer
Consumer
Consumers
Serializer
content
validation
consume
prevented

34. promises
provides
Data Contracts
High quality data for processing
Producer
Schema Registry
Confluent Kafka
Consumer
Consumer
Consumers
Deserializer
content
validation
prevented
contract
validation

35. Solution overview
Breaking changes Low quality data Security challenges
Utilize validation and
transformation rules
Take advantage of metadata
capabilities like tagging,
business metadata, etc
Protect sensitive data using
Confluent Security and
Governance capabilities
Use schema registry and
define schemas
Evolve them as needed
Use schema validation

36. Demo

37. Roadmap

38. ● Provide hybrid monitoring, governance
and management in a unified, secure
architecture:
✓ Data Portal Sync
✓ Data Policies
✓ Observability - Metrics and Alerting
✓ Cluster Linking & Mirrored Topics
Hybrid Connected Cloud
Extend Data Streaming Platform capabilities to
self-managed clusters
On Roadmap for 2025

39. Data Discovery

40. An Example from Amazon
Discoverable
Owner
Accessible
Contextualized
Standardized
Secure
Trustworthy
Self Describing
Metadata

41. Search and discover data
using Data Portal
On Roadmap for 2025

42. Understand the data lineage
On Roadmap for 2025

43. Stream Catalog
On Roadmap for 2025

44. Q&A Session

45. falberton@confluent.io
pventurini@confluent.io
Grazie!