Streamkap vs Fivetran: The Complete 2025 Comparison for Real-Time Data Teams

Choosing between Streamkap and Fivetran comes down to one fundamental question: do you need batch data integration or real-time streaming?

Both platforms move data from sources to destinations. But they were built for fundamentally different use cases. Fivetran pioneered the modern ELT movement, making it dead simple to sync data from hundreds of SaaS applications into your warehouse on a schedule. Streamkap was built from the ground up for real-time Change Data Capture (CDC), streaming database changes with sub-second latency.

This guide breaks down every aspect of both platforms so you can make an informed decision for your specific needs.

Quick Comparison: Streamkap vs Fivetran at a Glance

Before diving deep, here’s the high-level comparison:

Understanding the Fundamental Difference

Fivetran: The Batch ELT Pioneer

Fivetran helped define the modern ELT (Extract, Load, Transform) paradigm. The core idea is simple: extract data from sources, load it into your warehouse as-is, then transform it using tools like dbt. This approach works beautifully for analytics use cases where data freshness of 5-60 minutes is acceptable.

Fivetran’s strength is its massive connector library. With 500+ pre-built connectors covering everything from Salesforce to Stripe to Google Analytics, Fivetran excels at consolidating SaaS data into your warehouse. If your primary need is syncing marketing, sales, and product data from various cloud applications, Fivetran is a strong choice.

However, Fivetran’s architecture has inherent limitations when it comes to real-time data:

• Polling-based CDC: For database sources, Fivetran typically uses timestamp-based incremental loading or periodic polling rather than reading the database’s transaction log
• Minimum sync intervals: Even on the fastest plans, syncs run every 5 minutes at best
• No native streaming: Data moves in batches, not as a continuous stream

Streamkap: Purpose-Built for Real-Time

Streamkap takes a fundamentally different approach. Built on Apache Kafka, Apache Flink, and Debezium, Streamkap is a true real-time streaming platform. Every insert, update, and delete in your source database is captured from the transaction log and streamed to destinations within milliseconds.

This architecture enables use cases that are simply impossible with batch tools:

• Fraud detection that catches suspicious transactions before they complete
• Real-time personalization that responds to user behavior instantly
• Operational dashboards showing what’s happening right now, not 15 minutes ago
• Event-driven architectures where downstream systems react to changes immediately

Deep Dive: Data Latency

Latency is often the deciding factor between these platforms. Let’s examine what “real-time” actually means for each.

Fivetran Latency

Fivetran offers several sync frequency tiers:

Even at the highest tier, you’re looking at a minimum of 5 minutes between when data changes in your source and when it appears in your destination. In practice, accounting for sync duration and scheduling, end-to-end latency is often 10-15 minutes.

For many analytics use cases, this is perfectly fine. A marketing dashboard updated every 15 minutes serves most reporting needs. But for operational use cases—fraud detection, inventory management, real-time pricing—this delay is a non-starter.

Streamkap Latency

Streamkap achieves sub-second to single-digit second latency by fundamentally changing how data moves:

1. Log-based CDC: Streamkap reads directly from your database’s transaction log (WAL for PostgreSQL, binlog for MySQL, etc.) using Debezium
2. Streaming architecture: Changes flow through Kafka as a continuous stream, not batched requests
3. No polling: There’s no waiting for the next sync window—changes propagate immediately

Real-world Streamkap deployments typically see:

• 100-500ms from database commit to Kafka
• 1-3 seconds end-to-end to destinations like Snowflake or ClickHouse
• Consistent latency regardless of data volume

This isn’t just “faster batch”—it’s a fundamentally different paradigm that enables new categories of applications.

CDC Technology: Log-Based vs Polling

The method used to capture changes dramatically impacts reliability, performance, and completeness.

How Fivetran Captures Changes

For database sources, Fivetran primarily uses:

1. Timestamp-based incremental sync: Queries rows where updated_at > last_sync_time
2. Periodic full table scans: For tables without reliable timestamps
3. Limited log-based CDC: Available for some sources but not the primary method

This approach has several challenges:

• Missing deletes: Timestamp-based sync can’t detect deleted rows unless you use soft deletes
• Database load: Each sync queries your production database, adding read load
• Clock skew issues: Timestamp-based sync depends on reliable system clocks
• Schema requirements: Tables need proper updated_at columns

How Streamkap Captures Changes

Streamkap uses Debezium, the industry-standard open-source CDC engine, to read transaction logs directly:

1. PostgreSQL: Reads the Write-Ahead Log (WAL) via logical replication
2. MySQL/MariaDB: Reads the binary log (binlog)
3. SQL Server: Uses SQL Server’s native CDC or CT features
4. MongoDB: Reads the oplog or change streams
5. Oracle: Uses LogMiner or XStream

Benefits of log-based CDC:

• Complete capture: Every insert, update, and delete is captured, including hard deletes
• Zero database load: Reading logs doesn’t impact production query performance
• Transactional consistency: Changes are captured in the exact order they were committed
• No schema requirements: Works with any table structure

Connector Ecosystem

Both platforms offer extensive connector libraries, but with different focuses.

Fivetran Connectors

Fivetran’s strength is SaaS application connectors:

• 500+ total connectors
• Strong coverage for: Salesforce, HubSpot, Marketo, Google Analytics, Stripe, Zendesk, and virtually every popular SaaS tool
• Good database support including PostgreSQL, MySQL, SQL Server, Oracle, MongoDB
• Pre-built schemas optimized for analytics

If your primary need is consolidating data from dozens of SaaS applications, Fivetran’s connector breadth is hard to beat.

Streamkap Connectors

Streamkap focuses on database and streaming sources with deep CDC support:

Sources (30+):

• PostgreSQL (including RDS, GCP Cloud SQL, Aurora, Supabase, Neon, TimescaleDB, YugabyteDB, AlloyDB, CockroachDB)
• MySQL (including RDS, GCP, Azure, Aurora, MariaDB, PlanetScale, Vitess)
• SQL Server (including Azure SQL, RDS)
• Oracle
• MongoDB (including Atlas, DocumentDB)
• DynamoDB
• DB2
• Kafka, Redis, Elasticsearch

Destinations (35+):

• Data Warehouses: Snowflake, Databricks, BigQuery, Redshift
• OLAP: ClickHouse, Apache Druid, Firebolt, StarRocks
• Data Lakes: S3, Apache Iceberg, Delta Lake, Azure Data Lake
• Streaming: Kafka, Kinesis, Event Hubs, Pub/Sub
• Databases: PostgreSQL, MySQL, MongoDB, Redis

Streamkap doesn’t try to compete with Fivetran on SaaS connectors. Instead, it offers deep, production-grade CDC for the databases that power your applications.

Pricing Comparison

Pricing models differ significantly and can result in dramatically different costs depending on your use case.

Fivetran Pricing

Fivetran uses a consumption-based model with several components:

• Monthly Active Rows (MAR): Charged based on unique rows synced per month
• Connector fees: Some connectors have additional costs
• Platform tiers: Higher tiers for faster sync frequencies and additional features

Estimated monthly costs:

• Starter: ~$1,200/month for basic usage
• Standard: $2,000-5,000/month for typical mid-market usage
• Enterprise: $10,000+/month for high-volume or fast sync requirements

The MAR model can become expensive as data volumes grow, especially for high-change-rate tables.

Streamkap Pricing

Streamkap uses a simpler per-GB pricing model:

For high-change-rate workloads, Streamkap is often 3x or more cheaper than Fivetran. Streamkap’s published claim of “3x lower TCO” is based on these comparisons.

Cost Example: E-commerce Database

Consider an e-commerce company with:

• 5 PostgreSQL databases
• 500 tables total
• 100 million row changes per month

Fivetran estimate: $5,000-15,000/month (depending on MAR calculation and sync frequency)

Streamkap estimate: $1,800/month (Scale plan with ~50GB of CDC data)

The delta grows larger as data volumes increase.

Stream Processing and Transformations

Modern data pipelines often require transforming data in-flight. The platforms differ significantly here.

Fivetran Transformations

Fivetran’s philosophy is “load first, transform later”:

• No in-flight transformations: Data lands in your warehouse as-is
• dbt integration: Recommended to use dbt for post-load transformations
• Fivetran Transformations: A separate product for running dbt in Fivetran’s infrastructure

This approach works well for batch analytics workflows where you’re building dimensional models in your warehouse.

Streamkap Transformations

Streamkap includes built-in stream processing via Apache Flink:

• SQL transforms: Write SQL to filter, aggregate, and reshape data in real-time
• Python transforms: Custom Python logic for complex transformations
• Apache Flink: Full Flink support for Java/Scala transformations
• Pre-destination processing: Data arrives at destinations already transformed

Use cases for streaming transformations:

• PII masking: Remove sensitive data before it reaches the warehouse
• Aggregations: Pre-compute metrics to reduce warehouse compute costs
• Filtering: Only stream relevant records to specific destinations
• Enrichment: Join with reference data in real-time

Schema Evolution and Data Quality

Both platforms handle schema changes, but with different approaches.

Fivetran Schema Handling

• Automatic schema evolution: New columns are added automatically
• Schema drift handling: Changes propagate to destination
• Data type mapping: Fivetran-managed type conversions
• Reliable but opaque: Limited visibility into how changes are handled

Streamkap Schema Handling

• Automatic schema evolution: New columns, renamed columns, and type changes handled automatically
• Schema Registry: Avro schemas stored in Confluent Schema Registry
• Full visibility: See exactly how schemas map between source and destination
• Configurable behavior: Choose how to handle breaking changes

Security and Compliance

Enterprise deployments require robust security features.

Fivetran Security

• SOC 2 Type II certified
• HIPAA available on enterprise plans
• Private networking options (PrivateLink)
• Role-based access control
• SSO support

Streamkap Security

• SOC 2 Type II certified (Scale plan and above)
• HIPAA and PCI DSS 4.0.1 (Enterprise plan)
• AWS PrivateLink (Scale plan)
• VPN connectivity (Enterprise plan)
• SSO and Enterprise SSO options
• BYOC (Bring Your Own Cloud) for maximum control
• IP safelist and SSH tunneling

When to Choose Fivetran

Fivetran is the right choice when:

1. You need SaaS data consolidation: If your primary goal is syncing Salesforce, HubSpot, Stripe, and dozens of other SaaS apps into your warehouse, Fivetran’s connector library is unmatched.

2. Batch analytics is sufficient: For use cases like weekly business reviews, monthly reporting, or historical trend analysis, 15-60 minute latency is perfectly acceptable.

3. You want maximum simplicity: Fivetran’s fully managed approach requires minimal configuration. Point it at a source, pick a destination, and data flows.

4. Your team prefers dbt workflows: If your data team is built around dbt for transformations, Fivetran’s “load then transform” philosophy aligns well.

5. You’re not database-heavy: If most of your data comes from SaaS applications rather than operational databases, Fivetran’s strengths align with your needs.

When to Choose Streamkap

Streamkap is the right choice when:

1. You need true real-time data: Sub-second latency isn’t a nice-to-have—it’s a requirement. Fraud detection, real-time personalization, live operational dashboards, and event-driven architectures all demand immediate data.

2. Databases are your primary sources: Your critical data lives in PostgreSQL, MySQL, MongoDB, or other operational databases, and you need to stream every change reliably.

3. You’re building event-driven systems: Your architecture is moving toward event sourcing, CQRS, or microservices that react to data changes in real-time.

4. Cost matters at scale: For high-volume CDC workloads, Streamkap’s per-GB pricing is often 3x cheaper than Fivetran’s MAR-based model.

5. You need in-flight transformations: Masking PII, pre-aggregating data, or filtering records before they reach destinations is easier with Streamkap’s built-in Flink processing.

6. You want Kafka in your stack: Streamkap includes managed Kafka, so your CDC data is also available as a Kafka topic for other consumers—no additional infrastructure needed.

7. You value open-source foundations: Streamkap is built on Debezium, Kafka, and Flink—proven open-source technologies. There’s no proprietary lock-in to the CDC engine itself.

Migration Considerations

Moving from Fivetran to Streamkap

If you’re currently on Fivetran and considering Streamkap for database sources:

1. Identify real-time candidates: Which pipelines would benefit from sub-second latency?
2. Plan the backfill: Streamkap can snapshot existing data before starting CDC
3. Parallel running: Run both platforms during transition to validate data consistency
4. Keep Fivetran for SaaS: Consider using Fivetran for SaaS connectors and Streamkap for databases

Moving from Streamkap to Fivetran

If you’re on Streamkap and considering Fivetran:

1. Evaluate latency requirements: Can your use cases tolerate 5+ minute delays?
2. Review cost projections: Calculate MAR costs at your data volumes
3. Plan for transformation changes: Move from streaming transforms to dbt

Real-World Use Cases

Use Case 1: E-commerce Inventory Sync

Scenario: An online retailer needs inventory levels synced from their PostgreSQL database to their analytics warehouse and also to their Elasticsearch-powered search platform.

With Fivetran:

• Inventory syncs every 5-15 minutes
• Search results may show out-of-stock items as available
• Overselling risk during high-traffic periods

With Streamkap:

• Inventory changes stream in sub-second latency
• Search index updates within 2-3 seconds of a sale
• Accurate availability even during flash sales

Use Case 2: Financial Fraud Detection

Scenario: A fintech company needs to analyze transactions in real-time to flag potential fraud.

With Fivetran:

• Transactions batch every 5 minutes at best
• Fraudulent transactions may complete before detection
• Reactive rather than preventive

With Streamkap:

• Transactions stream in milliseconds
• Flink-based rules engine evaluates each transaction
• Suspicious transactions flagged before completion

Use Case 3: SaaS Analytics Dashboard

Scenario: A marketing team wants to consolidate data from Salesforce, HubSpot, Google Analytics, and Facebook Ads into a unified dashboard.

With Fivetran:

• 500+ connectors cover all sources
• Pre-built schemas simplify modeling
• Daily/hourly refreshes meet reporting needs

With Streamkap:

• Not the right tool—Streamkap doesn’t have SaaS connectors
• Would require alternative ingestion methods

Use Case 4: Multi-Tenant SaaS CDC

Scenario: A B2B SaaS company with a multi-tenant PostgreSQL database needs to stream changes to their data lake for ML training.

With Fivetran:

• Works, but expensive with high MAR
• 15-minute latency means stale training data

With Streamkap:

• Native multi-tenant CDC support
• Per-tenant data isolation
• Fresh data for ML pipelines

Conclusion: Different Tools for Different Jobs

Fivetran and Streamkap aren’t really competitors—they’re complementary tools for different parts of your data stack.

Choose Fivetran for batch analytics and SaaS data consolidation. Its breadth of connectors and simplicity make it ideal for building a unified view of your business from dozens of cloud applications.

Choose Streamkap for real-time database CDC. When you need sub-second latency from your operational databases—for event-driven architectures, real-time analytics, or operational use cases—Streamkap’s streaming-first architecture delivers what batch tools fundamentally cannot.

Many data teams use both: Fivetran for their SaaS applications, Streamkap for their databases. This combination provides comprehensive coverage without compromising on real-time requirements.

Ready to see how Streamkap handles real-time CDC? Start a free 30-day trial or compare features in detail.