Streamkap vs Airbyte: Managed Real-Time CDC vs Open-Source ETL

The comparison between Streamkap and Airbyte represents a broader decision in the data engineering world: managed real-time streaming vs. flexible open-source batch ETL.

Airbyte has emerged as a popular open-source alternative to commercial ETL tools, offering a vast connector library and the flexibility to self-host. Streamkap provides a managed, real-time CDC platform that eliminates infrastructure complexity while delivering sub-second latency.

This guide examines both platforms in depth to help you choose the right tool for your data integration needs.

Quick Comparison: Streamkap vs Airbyte

Aspect	Streamkap	Airbyte
Deployment	Fully managed SaaS	Self-hosted or Airbyte Cloud
Primary Use Case	Real-time CDC streaming	Batch/incremental ETL
Data Latency	Sub-second to seconds	Minutes to hours
CDC Support	Native log-based CDC	Limited CDC connectors
Connector Count	60+ (database-focused)	300+ (broad coverage)
Stream Processing	Built-in (Flink)	Not included
Kafka	Included	Not included
Infrastructure	Zero management	Kubernetes required (self-hosted)
Pricing	Usage-based ($600+/mo)	Free (self-hosted) or usage-based
Best For	Real-time CDC, no-ops teams	Budget batch ETL, DIY teams

Understanding the Platforms

Airbyte: Open-Source ETL Flexibility

Airbyte launched in 2020 with a compelling value proposition: an open-source alternative to expensive commercial ETL tools. The platform has grown rapidly, now offering:

• 300+ connectors covering databases, SaaS apps, files, and APIs
• Open-source core with permissive licensing
• Flexible deployment: Self-hosted on Kubernetes or managed Airbyte Cloud
• Connector Development Kit (CDK): Build custom connectors easily
• Active community: Thousands of contributors and users

Airbyte’s architecture follows a traditional batch/incremental pattern:

1. Connectors extract data on a schedule (hourly, daily, etc.)
2. Data is staged in an intermediate location
3. Normalized data loads into your destination
4. Optional dbt transformations run post-load

This model works well for analytics use cases where near-real-time data isn’t critical.

Streamkap: Managed Real-Time Streaming

Streamkap takes a fundamentally different approach, built from the ground up for real-time Change Data Capture:

• Log-based CDC via Debezium captures every database change
• Apache Kafka provides durable, ordered event streaming
• Apache Flink enables real-time transformations
• Fully managed: No clusters, no Kubernetes, no infrastructure to maintain
• Sub-second latency from source to destination

Streamkap’s architecture is event-driven:

1. Debezium reads the database transaction log
2. Changes stream through Kafka in real-time
3. Optional Flink transformations process data in-flight
4. Data arrives at destinations within seconds

This architecture enables use cases that batch ETL simply cannot address.

Deployment and Operations

The operational burden is often the deciding factor between these platforms.

Airbyte Deployment Options

Self-Hosted (Open Source):

• Requires Kubernetes cluster (or Docker for small deployments)
• You manage: upgrades, scaling, monitoring, security
• Full control over infrastructure
• No licensing costs
• Typical setup: 2-4 weeks for production readiness

Airbyte Cloud:

• Managed infrastructure
• Pay-per-use pricing
• Reduced operational burden
• Some feature limitations vs. self-hosted

Infrastructure Requirements (Self-Hosted):

Component	Minimum	Recommended
Kubernetes Nodes	3	5+
CPU per Node	4 cores	8 cores
Memory per Node	16GB	32GB
Storage	100GB SSD	500GB+ SSD
DevOps Expertise	Medium	High

For teams without Kubernetes expertise, self-hosted Airbyte represents a significant investment in infrastructure skills.

Streamkap Deployment

Streamkap is SaaS-only, which means:

• Zero infrastructure: No clusters, no Kubernetes, no VMs
• Setup in minutes: Connect source, configure destination, start streaming
• Automatic scaling: Handles traffic spikes without intervention
• Managed upgrades: New features and security patches applied automatically
• Built-in monitoring: Observability dashboards included

The trade-off is less customization—you can’t modify the underlying infrastructure. For most teams, this is a feature, not a limitation.

Time to Production Comparison:

Milestone	Airbyte (Self-Hosted)	Airbyte Cloud	Streamkap
Initial setup	1-2 days	Hours	Minutes
First pipeline	1 day	Hours	Minutes
Production-ready	2-4 weeks	1 week	Days
Ongoing maintenance	4-8 hrs/week	Minimal	Zero

Data Latency: Batch vs Real-Time

This is the fundamental architectural difference between the platforms.

Airbyte Latency

Airbyte operates on a scheduled sync model:

Sync Mode	Typical Latency	Use Case
Full Refresh	Hours to days	Small tables, infrequent
Incremental (append)	15 min - 1 hour	Append-only logs
Incremental (deduped)	15 min - 1 hour	Mutable data
CDC (where available)	15 min - 1 hour	Database changes

Even when Airbyte uses CDC connectors, the sync is still scheduled, not continuous. You’re getting CDC data, but in batches.

Factors affecting Airbyte latency:

• Sync schedule configuration
• Data volume and query performance
• Destination loading time
• Normalization processing

For analytics and reporting, this latency is often acceptable. For operational use cases, it’s a blocker.

Streamkap Latency

Streamkap provides continuous streaming with consistent low latency:

Stage	Typical Latency
Source → Kafka	100-500ms
Kafka → Flink (if used)	50-200ms
Flink → Destination	500ms - 2s
End-to-End	1-3 seconds

This latency is consistent regardless of:

• Data volume (100 rows/sec or 100,000 rows/sec)
• Time of day
• Sync schedules (there are none)

Real-time latency enables fundamentally different applications:

• Fraud detection: Block fraudulent transactions before they complete
• Inventory sync: Update availability across channels instantly
• Real-time ML: Feed fresh data to feature stores
• Operational dashboards: See what’s happening now, not 15 minutes ago

CDC Capabilities

Change Data Capture is where the platforms diverge most significantly.

Airbyte CDC Support

Airbyte offers CDC for some database connectors:

CDC-Capable Sources:

• PostgreSQL (logical replication)
• MySQL (binlog)
• SQL Server (CT/CDC)
• MongoDB (change streams)

Limitations:

• Not all connectors support CDC
• CDC is still batch-processed on a schedule
• Complex setup for some sources
• Limited visibility into CDC lag

Airbyte’s CDC implementation works, but it doesn’t provide the continuous streaming that log-based CDC enables.

Streamkap CDC Support

Streamkap is built entirely around CDC:

Log-Based CDC for All Database Sources:

• PostgreSQL: WAL logical replication
• MySQL/MariaDB: Binary log
• SQL Server: Transaction log via CT/CDC
• Oracle: LogMiner/XStream
• MongoDB: Oplog/Change Streams
• DynamoDB: DynamoDB Streams

CDC Features:

• Continuous capture: No scheduling, no batching
• Complete capture: Inserts, updates, deletes (including hard deletes)
• Transactional consistency: Changes in commit order
• Zero source impact: Reading logs doesn’t load production databases
• Schema evolution: Automatic handling of column adds, renames, type changes

The difference isn’t just about latency—it’s about completeness and reliability. Streamkap’s log-based CDC captures every change, in order, without impacting source performance.

Connector Ecosystem

Airbyte Connectors

Airbyte’s connector breadth is impressive:

• 300+ connectors total
• Strong SaaS coverage: Salesforce, HubSpot, Stripe, etc.
• Database support: PostgreSQL, MySQL, SQL Server, MongoDB, etc.
• API sources: REST, GraphQL, custom APIs
• File sources: S3, GCS, SFTP, etc.

Connector Quality Tiers:

• Certified: Maintained by Airbyte, production-ready
• Community: Community-contributed, variable quality
• Custom: Build your own with CDK

The breadth is a double-edged sword—some community connectors are excellent, others are buggy or incomplete.

Streamkap Connectors

Streamkap focuses on depth over breadth for database CDC:

Sources (30+):

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

Destinations (35+):

• Data warehouses: Snowflake, Databricks, BigQuery, Redshift
• OLAP: ClickHouse, Druid, Firebolt, StarRocks
• Lakes: S3, Iceberg, Delta Lake, Azure Data Lake
• Streaming: Kafka, Kinesis, Event Hubs, Pub/Sub

Streamkap doesn’t try to be everything to everyone. If you need to sync Salesforce data, use Airbyte or Fivetran. If you need real-time CDC from databases, Streamkap excels.

Stream Processing and Transformations

Airbyte Transformations

Airbyte’s transformation story is evolving:

• Basic normalization: Flattens nested JSON, creates typed columns
• dbt integration: Run dbt models after data loads
• No in-flight processing: All transforms happen post-load

This works for analytics workflows but means:

• PII reaches your warehouse before masking
• No real-time aggregations
• Additional warehouse compute costs for transforms

Streamkap Transformations

Streamkap includes Apache Flink for stream processing:

SQL Transforms:

-- Mask PII before reaching destination
SELECT
  id,
  REGEXP_REPLACE(email, '(.).*@', '$1***@') as email,
  amount,
  created_at
FROM transactions

Python Transforms:

# Custom enrichment logic
def transform(record):
    record['risk_score'] = calculate_risk(record)
    return record

Use Cases:

• PII masking: Remove sensitive data before it leaves your VPC
• Aggregations: Pre-compute metrics to reduce warehouse costs
• Filtering: Only stream relevant records
• Enrichment: Join with reference data in real-time
• Format conversion: Transform between Avro, JSON, Parquet

Processing happens in-flight, so data arrives at destinations already transformed.

Pricing Comparison

Airbyte Pricing

Self-Hosted (Open Source):

• Software: Free
• Infrastructure: $500-2,000+/month (Kubernetes cluster)
• DevOps time: 4-8 hours/week

Airbyte Cloud:

• Credits-based pricing
• ~$1-3 per GB synced (varies by connector)
• Minimum: ~$300/month for basic usage
• Enterprise: Custom pricing

Total Cost of Ownership (Self-Hosted):

• Small deployment: $1,000-2,000/month
• Medium deployment: $3,000-5,000/month
• Large deployment: $10,000+/month

The “free” open-source version has real costs in infrastructure and operations.

Streamkap Pricing

Plan	Monthly Price	Capacity	Key Features
Starter	$600	10GB (~50M rows)	Full CDC, schema evolution
Scale	$1,800	150GB (~750M rows)	+ Transforms, SSO, SOC 2
Enterprise	Custom	Unlimited	+ BYOC, HIPAA, PCI DSS

Streamkap’s pricing is predictable and all-inclusive—no infrastructure costs, no DevOps time.

Cost Comparison Example

Scenario: 50GB of CDC data per month from PostgreSQL to Snowflake

Platform	Monthly Cost	Notes
Airbyte Self-Hosted	~$2,500	Infra + ops time
Airbyte Cloud	~$1,500	Credits-based
Streamkap Scale	$1,800	Fixed, all-inclusive

For teams without Kubernetes expertise, Streamkap is often more cost-effective when factoring in operational overhead.

When to Choose Airbyte

Airbyte is the better choice when:

1. Budget is the primary concern: Self-hosted Airbyte is free (minus infrastructure), making it attractive for cost-sensitive teams with DevOps capabilities.

2. You need broad connector coverage: If you’re syncing data from dozens of SaaS applications, Airbyte’s 300+ connectors provide coverage Streamkap doesn’t.

3. Batch latency is acceptable: For analytics and reporting where 15-60 minute data freshness is fine, Airbyte delivers.

4. You have Kubernetes expertise: Self-hosted Airbyte requires meaningful DevOps investment. If you already run Kubernetes, the incremental burden is lower.

5. You want maximum flexibility: Self-hosted gives you full control over deployment, networking, and configuration.

6. Custom connectors are critical: Airbyte’s CDK makes building custom connectors relatively straightforward.

When to Choose Streamkap

Streamkap is the better choice when:

1. You need true real-time data: Sub-second latency is a requirement, not a nice-to-have. Fraud detection, real-time personalization, operational dashboards—batch simply won’t work.

2. Databases are your primary sources: Your critical data lives in PostgreSQL, MySQL, MongoDB, or other operational databases, and you need reliable CDC.

3. You want zero infrastructure management: No Kubernetes, no clusters, no ongoing DevOps burden. Connect and stream.

4. In-flight transformations matter: Masking PII, pre-aggregating data, or filtering before destination requires stream processing that Airbyte doesn’t provide.

5. You need Kafka integration: Streamkap includes managed Kafka, making your CDC data available to any Kafka consumer.

6. Predictable costs are important: Streamkap’s fixed pricing is easier to budget than variable infrastructure and usage costs.

7. Time-to-production is critical: Start streaming in minutes, not weeks.

Architecture Comparison

Airbyte Architecture

[Sources] → [Airbyte Connectors] → [Staging] → [Normalization] → [Destinations]
                    ↓
              [Scheduler]
              (Kubernetes)

• Connectors run as Docker containers
• Scheduled extraction and loading
• Centralized orchestration
• Horizontal scaling via Kubernetes

Streamkap Architecture

[Sources] → [Debezium CDC] → [Kafka] → [Flink Transforms] → [Destinations]
                                ↓
                        [Kafka Topics]
                    (Available to consumers)

• Continuous log-based CDC
• Kafka provides durability and ordering
• Optional Flink processing
• Data available as Kafka topics for other consumers

Migration Paths

From Airbyte to Streamkap

If you’re on Airbyte and need real-time:

1. Identify CDC candidates: Which database sources need real-time?
2. Configure Streamkap sources: Point at the same databases
3. Initial snapshot: Streamkap captures existing data
4. Parallel validation: Run both platforms to compare
5. Cutover: Switch production to Streamkap
6. Keep Airbyte for SaaS: Use Airbyte for non-database sources

From Streamkap to Airbyte

If you’re on Streamkap and want broader connectors:

1. Evaluate latency needs: Can you accept batch delays?
2. Plan infrastructure: Set up Kubernetes or choose Cloud
3. Test CDC connectors: Validate Airbyte’s CDC meets requirements
4. Migrate gradually: Move sources one at a time

Hybrid Architectures

Many organizations use both platforms:

Airbyte for:

• SaaS application data (Salesforce, HubSpot, Stripe)
• File sources (S3, SFTP)
• APIs without real-time requirements
• Cost-sensitive batch workloads

Streamkap for:

• Database CDC requiring real-time
• Event-driven architectures
• Operational use cases
• Kafka-based data mesh

This hybrid approach provides comprehensive coverage without compromising on real-time requirements.

Conclusion

Airbyte and Streamkap solve different problems:

Airbyte is an excellent choice for teams that need broad connector coverage, are comfortable managing Kubernetes, and can accept batch latency. The open-source model and active community make it attractive for budget-conscious organizations with DevOps capabilities.

Streamkap is purpose-built for real-time CDC from databases. If you need sub-second latency, want zero infrastructure management, or require stream processing capabilities, Streamkap delivers what batch tools cannot.

The decision often comes down to: Do you need real-time, and do you want to manage infrastructure?

• Real-time + no ops = Streamkap
• Batch + DIY ops = Airbyte (self-hosted)
• Batch + minimal ops = Airbyte Cloud
• Both real-time and batch = Use both platforms

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