Streamkap vs Estuary: Real-Time CDC Platform Comparison

Streamkap and Estuary are both modern data integration platforms focused on real-time data movement. Unlike batch-first tools like Fivetran or Airbyte, both were built with streaming as a core principle.

This makes the comparison more nuanced—you’re choosing between two platforms that share a philosophy but differ in implementation, focus areas, and architectural choices.

Quick Comparison: Streamkap vs Estuary

Aspect	Streamkap	Estuary
Focus	Database CDC	Broad real-time ETL
Architecture	Debezium + Kafka + Flink	Gazette (custom streaming)
CDC Engine	Debezium	Custom + Debezium
Database Sources	30+ (deep CDC)	20+ databases
SaaS Sources	Limited	Growing library
Stream Processing	Flink SQL/Python	TypeScript transforms
Kafka Integration	Native (included)	Via connector
Latency	Sub-second	Sub-second
Pricing	Per GB	Usage-based
Best For	Deep database CDC	Broader source variety

Understanding the Platforms

Estuary Flow

Estuary Flow is a real-time data operations platform built on a custom streaming infrastructure called Gazette. Key characteristics:

Architecture:

• Gazette: Distributed streaming storage (similar to Kafka)
• Captures: Sources that ingest data
• Derivations: Transformations using TypeScript
• Materializations: Destinations

Strengths:

• Broad connector library (databases + SaaS)
• TypeScript transformations
• Built-in data reduction/compaction
• CDC + batch sources in one platform

Focus Areas:

• Versatile real-time ETL
• Operational data stores
• Growing SaaS connector coverage

Streamkap

Streamkap is a real-time CDC platform built on proven open-source foundations:

Architecture:

• Debezium: Industry-standard log-based CDC
• Apache Kafka: Durable event streaming
• Apache Flink: Stream processing engine

Strengths:

• Deep database CDC expertise
• Native Kafka integration
• Flink-powered SQL/Python transforms
• Optimized warehouse connectors

Focus Areas:

• Database CDC to data warehouses
• Event-driven architectures
• Kafka-based data infrastructure

Architectural Differences

Estuary’s Gazette Architecture

Estuary built a custom streaming foundation called Gazette:

[Sources/Captures]
       ↓
[Gazette Journals] (streaming storage)
       ↓
[Derivations] (TypeScript transforms)
       ↓
[Materializations] (destinations)

Advantages:

• Purpose-built for Estuary’s use case
• Integrated streaming storage
• Optimized for their workflows

Considerations:

• Proprietary technology
• Less ecosystem compatibility
• Learning curve for Gazette concepts

Streamkap’s Kafka/Flink Architecture

Streamkap uses industry-standard open-source components:

[Sources]
       ↓
[Debezium CDC]
       ↓
[Apache Kafka]
       ↓
[Apache Flink] (SQL/Python transforms)
       ↓
[Destinations]

Advantages:

• Battle-tested at massive scale
• Kafka topics accessible to other consumers
• Flink ecosystem and community
• No proprietary lock-in to streaming layer

Considerations:

• Multiple components (managed by Streamkap)
• Kafka concepts may be new to some teams

CDC Capabilities

Estuary CDC

Estuary supports CDC for major databases:

Supported Sources:

• PostgreSQL (via Debezium)
• MySQL (via Debezium)
• SQL Server
• MongoDB
• Others

Approach:

• Mix of Debezium and custom connectors
• Focus on broad coverage

Streamkap CDC

Streamkap specializes in database CDC:

Supported Sources (30+):

• PostgreSQL ecosystem: RDS, Aurora, GCP, Azure, Supabase, Neon, TimescaleDB, AlloyDB, CockroachDB, YugabyteDB
• MySQL ecosystem: RDS, Aurora, GCP, Azure, MariaDB, PlanetScale, Vitess
• SQL Server: On-prem, Azure SQL, RDS
• Oracle: On-prem, RDS
• MongoDB: On-prem, Atlas, DocumentDB
• DynamoDB
• DB2

Approach:

• Debezium for all database CDC
• Deep optimization per database type
• Focus on reliability and latency

CDC Depth Comparison

Database	Streamkap	Estuary
PostgreSQL (core)	✓ Deep	✓
PostgreSQL (variants)	10+ variants	Limited
MySQL (core)	✓ Deep	✓
MySQL (variants)	8+ variants	Limited
SQL Server	✓	✓
Oracle	✓	Limited
MongoDB	✓	✓
DynamoDB	✓	✓

Streamkap offers deeper coverage for database variants and cloud-specific implementations.

SaaS and API Sources

Estuary Sources

Estuary is building broader SaaS connector coverage:

• Growing library of SaaS connectors
• REST API sources
• File sources
• Streaming sources

If you need to combine database CDC with SaaS data in real-time, Estuary offers more options.

Streamkap Sources

Streamkap focuses on databases and streaming sources:

• Databases (primary focus)
• Kafka as a source
• S3 as a source
• Webhook source
• Redis

Streamkap doesn’t compete on SaaS connector breadth—it focuses on database CDC excellence.

Stream Processing

Estuary Derivations

Estuary uses TypeScript for transformations:

// Estuary derivation example
import { IDerivation, Document, Register } from 'flow/yourCollection';

export class Derivation extends IDerivation {
  transform(source: Document): Register[] {
    return [{
      ...source,
      processed_at: new Date().toISOString(),
      risk_score: calculateRisk(source),
    }];
  }
}

Characteristics:

• TypeScript-based
• Stateful derivations
• Integrated with Estuary platform
• Good for developers familiar with TypeScript

Streamkap Transformations

Streamkap uses Apache Flink for stream processing:

SQL Transforms:

SELECT
  id,
  REGEXP_REPLACE(email, '(.).*@', '$1***@') as masked_email,
  amount,
  event_time
FROM orders
WHERE amount > 100

Python Transforms:

def transform(record):
    record['risk_score'] = calculate_risk(record)
    record['region'] = lookup_region(record['ip'])
    return record

Characteristics:

• SQL for common transformations
• Python for complex logic
• Full Flink capabilities available
• Familiar to data engineers

Destination Support

Estuary Destinations

Estuary materializes data to various destinations:

• Data warehouses: Snowflake, BigQuery, Databricks, Redshift
• Databases: PostgreSQL, MySQL, Elasticsearch
• Data lakes: S3 (various formats)
• Streaming: Kafka

Streamkap Destinations

Streamkap offers optimized warehouse connectors:

Data Warehouses (native connectors):

• Snowflake (Snowpipe Streaming)
• Databricks
• BigQuery (Storage Write API)
• Redshift
• ClickHouse
• Firebolt, StarRocks, Druid

Data Lakes:

• S3 (Parquet, Avro, JSON)
• Apache Iceberg
• Delta Lake
• Azure Data Lake

Streaming:

• Kafka (included)
• Kinesis
• Event Hubs
• Pub/Sub

Warehouse Optimization

Both platforms support major warehouses, but implementation matters:

Warehouse	Streamkap	Estuary
Snowflake	Snowpipe Streaming API	Standard connector
BigQuery	Storage Write API	Standard connector
Databricks	Optimized connector	Standard connector
ClickHouse	Native connector	Via generic SQL

Streamkap invests in warehouse-specific optimizations for latency and efficiency.

Kafka Integration

This is a significant differentiator.

Estuary and Kafka

Estuary uses its own streaming layer (Gazette). Kafka is a destination, not the core:

• Can write to Kafka as a materialization
• Kafka is not the primary transport

Streamkap and Kafka

Streamkap is built on Kafka:

• All CDC data flows through Kafka
• Kafka topics available for direct consumption
• Multiple consumers can read the same data
• Standard Kafka ecosystem compatibility

If you want your CDC data available as Kafka topics for other applications, Streamkap provides this natively.

Pricing Comparison

Estuary Pricing

Estuary uses a usage-based model:

• Free tier for small workloads
• Growth tier: Usage-based pricing
• Enterprise: Custom pricing

Specific pricing details vary; contact Estuary for quotes.

Streamkap Pricing

Streamkap uses straightforward per-GB pricing:

Plan	Price	Capacity	Features
Starter	$600/mo	10GB/mo	Full CDC
Scale	$1,800/mo	150GB/mo	+ Transforms, SOC 2
Enterprise	Custom	Unlimited	+ HIPAA, PCI DSS

All-inclusive pricing with no hidden fees.

When to Choose Estuary

Estuary is a strong choice when:

1. You need SaaS + database sources: Combining real-time CDC with SaaS connectors in one platform is valuable.

2. TypeScript transformations fit your team: If your team is JavaScript/TypeScript-native, Estuary’s derivations may feel more natural.

3. You want a single platform for all real-time ETL: Estuary aims to be a comprehensive real-time data platform, not just CDC.

4. You don’t need Kafka access: If you just need data flowing to destinations without Kafka consumption, Estuary’s architecture works.

5. You’re evaluating newer platforms: Estuary brings fresh thinking to real-time data integration.

When to Choose Streamkap

Streamkap is the better choice when:

1. Database CDC is your primary focus: Streamkap’s depth in database variants and CDC reliability is unmatched.

2. You need Kafka integration: CDC data as Kafka topics for microservices or other consumers.

3. SQL/Python transforms are preferred: Data engineers often prefer SQL over TypeScript for transformations.

4. Open-source foundations matter: Debezium, Kafka, and Flink are proven at massive scale with large communities.

5. Warehouse optimization is critical: Native Snowpipe Streaming, BigQuery Storage Write API matter for latency and cost.

6. You want predictable pricing: Clear per-GB pricing without complexity.

7. You prefer battle-tested technology: Kafka and Flink power real-time systems at Netflix, Uber, LinkedIn, and thousands of others.

Technical Deep Dive: Architecture Comparison

Reliability and Durability

Estuary:

• Gazette provides durability
• Custom replication and recovery
• Proprietary infrastructure

Streamkap:

• Kafka provides durability (proven at petabyte scale)
• Standard replication factor configuration
• Well-understood failure modes

Scalability

Estuary:

• Gazette scales horizontally
• Proprietary scaling model

Streamkap:

• Kafka partition-based scaling
• Flink parallel processing
• Proven patterns from massive deployments

Ecosystem

Estuary:

• Custom ecosystem
• Estuary-specific tooling

Streamkap:

• Kafka ecosystem: Kafka Connect, Kafka Streams, ksqlDB compatibility
• Flink ecosystem: Extensive connectors and integrations
• Broad tooling compatibility

Migration Considerations

From Estuary to Streamkap

If you’re evaluating a switch:

1. Map Estuary captures to Streamkap sources
2. Convert TypeScript derivations to SQL/Python transforms
3. Configure destination connectors
4. Parallel validation
5. Cutover

From Streamkap to Estuary

If Estuary better fits your needs:

1. Evaluate Estuary’s connector coverage
2. Convert Flink transforms to TypeScript derivations
3. Configure materializations
4. Test latency and reliability
5. Transition

Hybrid Approaches

Some organizations use both:

Streamkap for:

• Database CDC requiring deep reliability
• Kafka-based architectures
• Data warehouse streaming

Estuary for:

• SaaS source connectors
• Specific use cases better suited to Estuary

This isn’t always necessary but can make sense for complex environments.

Conclusion

Streamkap and Estuary share a real-time philosophy but differ in implementation:

Estuary offers broader source coverage and a unified platform for real-time ETL. Its TypeScript transformations and growing SaaS connector library make it versatile. The custom Gazette architecture is purpose-built for their use case.

Streamkap offers deep database CDC expertise built on proven open-source foundations. Native Kafka integration, Flink-powered transformations, and optimized warehouse connectors make it excellent for database-to-warehouse streaming. The battle-tested architecture inspires confidence at scale.

The choice depends on your specific needs:

• Broader source variety? → Consider Estuary
• Deep database CDC + Kafka? → Consider Streamkap
• TypeScript transforms? → Consider Estuary
• SQL/Python transforms? → Consider Streamkap

Both are capable platforms for real-time data integration. Your decision should be based on which platform’s strengths align with your priorities.

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