Streamkap vs Confluent: Purpose-Built CDC vs Kafka Platform

Confluent and Streamkap both involve Kafka and real-time data, but they serve different purposes. Understanding this distinction is key to choosing the right platform.

Confluent is the company behind Apache Kafka, offering a comprehensive data streaming platform. It’s designed for organizations building event-driven architectures where Kafka is a central nervous system.

Streamkap uses Kafka internally but abstracts it away, providing a focused solution for streaming CDC data from databases to modern data warehouses and lakes.

This guide explores when you need a full Kafka platform versus a purpose-built CDC solution.

Quick Comparison: Streamkap vs Confluent

Aspect	Streamkap	Confluent Cloud
Primary Purpose	CDC to data warehouses	Data streaming platform
Kafka Exposure	Abstracted (internal)	Full access
Use Case Focus	Database CDC pipelines	Event-driven architectures
Setup Complexity	Minutes	Hours to days
Kafka Knowledge Required	None	Significant
CDC Method	Debezium (managed)	Debezium (self-configured)
Connectors	Pre-configured	120+ (you configure)
Stream Processing	Built-in Flink	ksqlDB, Flink (separate)
Pricing	Per GB ($600+/mo)	Complex (multi-component)
Best For	CDC to warehouses	Full Kafka ecosystem

Understanding Confluent

What Confluent Offers

Confluent provides a complete data streaming platform built around Apache Kafka:

Confluent Cloud (Fully Managed):

• Managed Kafka clusters
• Schema Registry
• ksqlDB for stream processing
• Kafka Connect (managed connectors)
• Cluster linking and replication
• Governance and security tools

Confluent Platform (Self-Managed):

• Enterprise Kafka distribution
• Additional proprietary features
• On-premises deployment option

Confluent Architecture

A typical Confluent deployment for CDC looks like:

[Source Databases]
       ↓
[Debezium Connectors] (via Kafka Connect)
       ↓
[Confluent Kafka]
       ↓
[ksqlDB / Flink] (optional processing)
       ↓
[Sink Connectors]
       ↓
[Destinations]

Each component requires separate configuration and management.

Confluent Strengths

Confluent excels when you need:

1. Full Kafka Capabilities: Producers, consumers, streams, exactly-once semantics, transactions

2. Event-Driven Architecture: Kafka as the backbone for microservices communication

3. Multi-Consumer Patterns: Many applications consuming from the same topics

4. Stream Processing: Complex event processing with ksqlDB or Flink

5. Data Governance: Schema management, lineage, audit logging

6. Hybrid Deployments: Cluster linking between cloud and on-premises

Understanding Streamkap

What Streamkap Offers

Streamkap provides a focused CDC platform:

Core Features:

• Managed Debezium CDC
• Managed Kafka (internal)
• Managed Flink for transformations
• Native warehouse/lake connectors
• Schema evolution handling
• Observability and alerting

Streamkap Architecture

Streamkap abstracts the complexity:

[Source Databases]
       ↓
[Streamkap] ← (Debezium + Kafka + Flink inside)
       ↓
[Destinations]

You configure sources and destinations; Streamkap handles everything in between.

Streamkap Strengths

Streamkap excels when you need:

1. Simple CDC Pipelines: Database changes to data warehouses without Kafka expertise

2. Fast Time-to-Production: Minutes instead of days or weeks

3. Zero Kafka Management: Don’t want to learn or operate Kafka

4. Native Warehouse Support: Direct streaming to Snowflake, Databricks, etc.

5. In-Flight Transformations: SQL/Python processing without additional infrastructure

When You Need Confluent (Full Kafka)

Building Event-Driven Architectures

If Kafka is the central nervous system of your application architecture:

Example: E-commerce platform where:

• Orders are published to Kafka
• Inventory service consumes order events
• Shipping service consumes order events
• Notification service consumes order events
• Analytics consumes order events

This multi-consumer, event-sourcing pattern is Confluent’s sweet spot.

Multiple Producers and Consumers

When many applications both produce and consume from Kafka:

[App A] ←→ [Kafka] ←→ [App B]
   ↓           ↓          ↓
[App C] ←→ [Kafka] ←→ [App D]

Confluent provides the platform for this complex topology.

Advanced Stream Processing

When you need sophisticated real-time processing:

ksqlDB Use Cases:

• Real-time aggregations across streams
• Joining streams with streams
• Windowed computations
• Materialized views

Confluent Flink:

• Complex event processing
• ML model inference
• Multi-stream joins

Data Governance at Scale

When you need enterprise governance:

• Schema Registry with compatibility policies
• Data lineage tracking
• Role-based access control
• Audit logging
• Multi-region compliance

You Already Have Kafka Expertise

If your team already knows Kafka well:

• Existing Kafka clusters
• Operational runbooks
• Monitoring and alerting
• Team expertise

The incremental effort to add CDC via Confluent is lower.

When You Need Streamkap (Focused CDC)

CDC to Data Warehouses

Your primary goal is streaming database changes to analytics platforms:

Example: Stream PostgreSQL changes to Snowflake for real-time dashboards

You don’t need:

• Direct Kafka topic access
• Multiple consumers
• Complex stream topologies

You just need data flowing quickly and reliably.

No Kafka Expertise

Your team hasn’t worked with Kafka:

• No Kafka operational experience
• No topic design knowledge
• No Connect configuration experience
• No ksqlDB/Flink experience

Learning Kafka to set up CDC pipelines is significant overhead if CDC is your only goal.

Fast Time-to-Production

You need to start streaming today, not next month:

Task	Confluent	Streamkap
Cluster setup	Hours	Included
Connect configuration	Hours	Minutes
Debezium setup	Hours	Minutes
Sink connector setup	Hours	Minutes
Testing and validation	Days	Hours
Total	1-4 weeks	Hours

Predictable, Simple Pricing

Confluent Cloud pricing has multiple components:

• Cluster costs (by CKU or usage)
• Connect costs (by task/hour)
• ksqlDB costs (by CSU)
• Data transfer costs
• Schema Registry costs

Streamkap pricing is straightforward:

• Starter: $600/month for 10GB
• Scale: $1,800/month for 150GB
• Enterprise: Custom

You Want CDC Data in Kafka Too

Streamkap includes Kafka internally. Your CDC data can be consumed directly from Kafka topics if needed—you get the best of both worlds without managing Kafka yourself.

Feature Comparison

CDC Capabilities

Feature	Streamkap	Confluent
CDC Engine	Debezium (managed)	Debezium (self-configured)
PostgreSQL	✓	✓ (configure yourself)
MySQL	✓	✓ (configure yourself)
SQL Server	✓	✓ (configure yourself)
MongoDB	✓	✓ (configure yourself)
Oracle	✓	✓ (configure yourself)
Setup Time	Minutes	Hours-Days
Schema Evolution	Automatic	Configure yourself

Both use Debezium, but Streamkap manages all configuration.

Destination Support

Destination	Streamkap	Confluent
Snowflake	Native connector	Sink connector (configure)
Databricks	Native connector	Sink connector (configure)
BigQuery	Native connector	Sink connector (configure)
ClickHouse	Native connector	Sink connector (configure)
S3/Iceberg	Native connector	Sink connector (configure)
Kafka Topics	Available	Native

Streamkap optimizes for data warehouse delivery; Confluent provides raw Kafka access.

Stream Processing

Feature	Streamkap	Confluent
SQL Processing	Flink SQL (built-in)	ksqlDB (separate product)
Python Processing	Built-in	Custom application
Setup	Included	Additional configuration
Pricing	Included	Additional cost

Operational Experience

Aspect	Streamkap	Confluent Cloud
Kafka Management	None	Managed (but you configure)
Connect Management	None	You manage connectors
Schema Registry	Automatic	You configure
Monitoring	Built-in	Confluent Control Center
Alerting	Built-in	Configure yourself

Pricing Deep Dive

Confluent Cloud Pricing

Confluent pricing is complex. Example for CDC pipeline:

Basic Kafka Cluster:

• Standard cluster: ~$0.10-0.50/GB
• Dedicated: $1,500+/month base

Kafka Connect:

• $0.08/task/hour (min 1 task per connector)
• Debezium source: ~$60/month per task
• Snowflake sink: ~$60/month per task

ksqlDB (if used):

• Starting at $0.12/CSU/hour (~$90/month min)

Schema Registry:

• $0.10/million API calls

Example Total (PostgreSQL → Kafka → Snowflake):

• Kafka: $200-500/month
• Source connector: $60/month
• Sink connector: $60/month
• Schema Registry: $50/month
• Minimum: ~$370-670/month

Add ksqlDB, higher throughput, or multiple sources, and costs escalate quickly.

Streamkap Pricing

Plan	Price	Capacity	Includes
Starter	$600/mo	10GB	Everything
Scale	$1,800/mo	150GB	+ Transforms
Enterprise	Custom	Unlimited	+ Compliance

No component pricing, no task fees, no API call charges.

Cost Comparison

Scenario: 3 PostgreSQL databases → Snowflake, 50GB CDC/month

Confluent Cloud:

• Kafka (dedicated for reliability): $1,500
• 3 source connectors: $180
• 1 sink connector: $60
• Schema Registry: $50
• Total: ~$1,790/month
• Plus: Configuration and management time

Streamkap:

• Scale plan: $1,800/month
• Total: $1,800/month
• Plus: Nothing to configure

Similar cost, but Streamkap requires far less expertise and effort.

Integration Patterns

Pattern 1: Streamkap for CDC, Confluent for Events

Use both platforms for their strengths:

[Databases] → [Streamkap] → [Snowflake]
                    ↓
            [Kafka Topics] ← (available)

[Applications] → [Confluent] → [Microservices]

Streamkap handles CDC; Confluent handles application events.

Pattern 2: Streamkap as Confluent Alternative

For teams that only need CDC:

[Databases] → [Streamkap] → [Data Warehouse]
                    ↓
            [Kafka Topics] ← (if needed)

Full CDC capability without Confluent complexity.

Pattern 3: Confluent Everything

For teams fully invested in Kafka:

[Databases] → [Confluent Connect + Debezium] → [Confluent Kafka]
                                                      ↓
                                              [ksqlDB / Flink]
                                                      ↓
                                              [Sink Connectors]
                                                      ↓
                                              [Destinations]

Maximum flexibility, maximum configuration.

Migration Considerations

From Confluent to Streamkap

If you’re using Confluent primarily for CDC:

1. Assess: Is CDC your main use case?
2. Evaluate: Do you need direct Kafka access?
3. Migrate: Point Streamkap at the same sources
4. Validate: Compare data in destinations
5. Transition: Move production traffic

From Streamkap to Confluent

If you outgrow focused CDC:

1. Assess: Do you need full Kafka capabilities?
2. Plan: Design topic architecture
3. Deploy: Set up Confluent cluster
4. Configure: Set up Connect, connectors
5. Transition: Migrate sources

Decision Framework

Choose Confluent When:

Requirement	Why Confluent
Event-driven microservices	Kafka is the backbone
Many producers/consumers	Multi-application pub/sub
Complex stream processing	ksqlDB, Flink capabilities
Direct Kafka API access	Applications use Kafka directly
Existing Kafka expertise	Leverage team knowledge
Data governance needs	Schema Registry, lineage

Choose Streamkap When:

Requirement	Why Streamkap
CDC to data warehouses	Purpose-built solution
No Kafka expertise	No learning curve
Fast time-to-production	Hours, not weeks
Simple pricing	Predictable costs
Zero ops burden	Fully managed
Focus on data, not infrastructure	Abstracted complexity

Conclusion

Confluent and Streamkap serve different needs:

Confluent is a comprehensive data streaming platform. It’s the right choice when Kafka is central to your architecture—when multiple applications produce and consume events, when you need sophisticated stream processing, or when you have the expertise to leverage the full Kafka ecosystem.

Streamkap is a focused CDC solution. It’s the right choice when your goal is streaming database changes to data warehouses and lakes—when you want sub-second latency without learning Kafka, when time-to-production matters, or when you prefer simplicity over flexibility.

Many organizations use both: Confluent for event-driven application architecture, Streamkap for analytics CDC pipelines. They’re complementary tools, not competitors.

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