When to Use Kafka vs Databricks: A Practical Decision Framework

The Confusion: Kafka vs Databricks

Most architects and data engineers struggle with fundamental questions when designing data pipelines:

• Should I use Kafka or Databricks to transform data?
• When is Databricks enough without Kafka?
• Should I just use a JDBC connector?
• Do I need Kafka Connect, or can Databricks directly ingest?

Let's clarify the fundamentals upfront:

• Kafka = Transport layer for real-time events
• Databricks = Compute engine for batch and stream processing
• Connectors = Ingestion plumbing

Understanding these distinctions is crucial for building scalable, efficient data architectures. In modern data platforms, the typical flow looks like: Source Systems → Kafka → Databricks → Warehouse → Applications.

What Kafka Is (and Isn't)

Kafka IS:

• A distributed log for real-time event streaming
• A messaging and storage system for high throughput
• A pub/sub backbone for microservices
• Supports ordering, durability, and horizontal scaling

Kafka IS NOT:

• A computation or transformation engine
• A long-term data warehouse
• A query engine for analytics
• A replacement for Databricks or Spark

Ideal Use Cases for Kafka:

• Milliseconds to seconds latency requirements
• Multiple consumers reading the same event stream
• Event-driven microservice architectures
• High throughput scenarios (millions of events/second)
• Broadcasting events to multiple downstream systems

Real-World Examples:

Example 1: Real-time User Activity Tracking

Capturing user clicks and pageviews → Kafka → Real-time analytics engine → Machine learning models for personalization.

Example 2: Microservice Communication

User Service → Kafka → Notification Service, Fraud Detection Service, Analytics Service. Each service independently consumes relevant events.

Example 3: Change Data Capture (CDC)

Debezium connector → Kafka → Databricks/Snowflake → Downstream analytical systems. This enables real-time replication with full event history.

What Databricks Is (and Isn't)

Databricks IS:

• A unified analytics platform built on Apache Spark
• Ideal for large-scale batch and streaming ETL
• A lakehouse platform combining data lake and warehouse capabilities
• Supports SQL, Python, Scala, and R
• Handles both structured and unstructured data efficiently

Databricks IS NOT:

• A low-latency event transport system
• A message broker
• A replacement for Kafka's real-time streaming capabilities

Ideal Use Cases for Databricks:

• Complex data transformations and aggregations
• Processing large volumes stored in data lakes
• Delta Lake ACID transactions and time travel
• Machine learning pipeline orchestration
• Processing minutes-to-hours worth of accumulated data

Real-World Examples:

Example 1: Large-Scale Log Processing

Transform 2TB of daily logs through medallion architecture: Raw logs → Bronze (ingestion) → Silver (cleansed) → Gold (business-level aggregates).

Example 2: ML Feature Engineering

Kafka event streams → Databricks → Feature engineering → Feature store → ML models. Databricks provides the computational power for complex feature calculations.

Example 3: Hybrid Stream and Batch Processing

Join real-time CDC streams with batch-loaded product catalogs to create enriched datasets for analytics.

JDBC and Source Connectors Explained

When to Use JDBC Connectors:

• Direct reads/writes to relational databases
• Real-time latency is not critical
• Event replay capability is not needed
• Moderate data volumes (typically under 200GB per batch)

JDBC Is NOT Good For:

• High-frequency real-time data ingestion
• Full table scans on large tables every batch run
• Change Data Capture scenarios
• Resilient, fault-tolerant pipelines with offset management

Important: JDBC connections put direct load on source databases. Use sparingly and consider CDC-based approaches for production systems.

Kafka Connect vs JDBC vs Databricks Ingestion

Kafka Connect (Source Connectors):

• Purpose-built for CDC, incremental loads, and streaming ingestion
• Works with Debezium, Oracle CDC, SQL Server CDC connectors
• Provides offset tracking, schema evolution, and fault tolerance
• Ideal for real-time database replication into Kafka

JDBC (via Databricks):

• Simpler setup for batch data pulls
• No built-in resilience or replay capabilities
• No offset management or schema evolution
• Creates database load with frequent polling

Decision Matrix: When to Use What

Real-World Scenarios and Best Solutions

Scenario A: Real-time Database Replication to Data Lake

Need: Sync database changes to data lake in real-time

Solution: Kafka Connect + Kafka → Databricks

Why: Provides resilience, CDC capabilities, and horizontal scalability. Kafka acts as a buffer, and Databricks handles transformation.

Scenario B: Large Daily Log Processing for BI

Need: Transform 500GB/day of logs for business intelligence

Solution: Databricks only

Why: Batch processing is sufficient. No need for Kafka unless real-time dashboards are required.

Scenario C: Periodic Bulk Database Transfer

Need: SQL Server → Snowflake every 4 hours

Solution: Snowflake JDBC or Databricks JDBC

Why: Simple periodic batch loads don't need the complexity of Kafka infrastructure.

Scenario D: Sub-second Fraud Detection

Need: Payment events → Fraud detection system in under 1 second

Solution: Kafka

Why: Databricks uses micro-batching and cannot provide sub-second latency. Kafka's stream processing is essential here.

Scenario E: ML on Clickstream Data

Need: Machine learning on user clickstream data

Solution: Kafka + Databricks

Why: Kafka captures and transports events in real-time, while Databricks provides the computational power for feature engineering and model training.

Scenario F: Schema Evolution and Event Replay

Need: Ability to replay events and handle schema changes

Solution: Kafka or Kafka Connect

Why: Kafka's log retention enables replay, and Schema Registry handles evolution. JDBC cannot provide these capabilities.

Common Pitfalls to Avoid

❌ Using Databricks as Real-time Event Transport

Databricks uses micro-batch processing (typically 5-10 second intervals). It's not designed for millisecond-level event streaming that Kafka provides.

❌ Using Kafka as a Transformation Tool

Kafka Streams can handle simple transformations, but complex ETL with joins, aggregations, and ML should be done in Databricks or Spark.

❌ JDBC for Large Table Full Scans

Pulling entire large tables via JDBC causes significant database load and network bottlenecks. Use CDC-based approaches instead.

❌ Daily Full-Table Loads Instead of CDC

Inefficient and resource-intensive. Implement CDC with Kafka/Debezium to capture only changed records.

❌ Multiple Consumers Directly Writing to Databases

Creates tight coupling and potential data inconsistencies. Better pattern: Kafka → Databricks/Snowflake for centralized ingestion and processing.

Quick Decision Rules

Use these rules of thumb for rapid architectural decisions:

• Latency < 5 seconds: Use Kafka
• Heavy transformations needed: Use Databricks
• Data comes from databases: Use Kafka Connect
• One-time/full batch loads: Use JDBC
• Consumer count > 1: Use Kafka
• Consumer count = 1: Consider JDBC
• ML pipelines required: Use Databricks
• CDC needed: Use Kafka Connect

The Modern Data Architecture Pattern

Most enterprise-scale data platforms follow this hybrid architecture:

Source Systems → Kafka Connect → Kafka → Databricks → Data Warehouse → BI/Applications
                                           ↓
                                    Stream Processing
                                    Real-time Analytics

This architecture provides:

• Real-time capabilities via Kafka for event streaming
• Heavy compute power via Databricks for transformations and ML
• Decoupling between producers and consumers
• Scalability at each layer independently
• Resilience with replay capabilities and fault tolerance

Conclusion

Understanding the strengths and limitations of each component is crucial for building efficient data architectures:

• Kafka: Event streaming and real-time transport layer
• Databricks: Heavy compute for ETL, transformations, and ML
• Kafka Connect: Resilient CDC and database ingestion
• JDBC: Simple batch reads for moderate data volumes

The key is not choosing one over the other, but understanding how they complement each other in modern data architectures. Most production systems benefit from a hybrid approach where Kafka handles real-time transport, Databricks performs transformations, and specialized connectors manage ingestion.

Remember: The best architecture is one that matches your specific latency, scale, and complexity requirements—not the one with the most cutting-edge tools.

Building scalable data platforms requires understanding these trade-offs and selecting the right tool for each job. As you design your next data pipeline, refer back to the decision matrix and scenarios outlined here to make informed choices.