When to Use Kafka vs Databricks: A Practical Decision Framework

The High Cost of Wrong Technology Choices

Over the past 7+ years building enterprise data platforms, I've observed teams invest millions in infrastructure that doesn't match their requirements. Some build complex Kafka clusters for workloads that need simple batch processing. Others rely on JDBC polling and watch their systems struggle under production load. The impact extends beyond infrastructure costs—it includes engineering time, missed SLAs, and delayed business initiatives.

Most architects and data engineers face these critical 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 = Distributed event streaming platform and transport layer (typical latency: 5-200ms)
• Databricks = Unified analytics platform built on Apache Spark for batch and streaming compute (typical latency: 5+ seconds)
• Connectors = Ingestion mechanisms (CDC-based vs polling-based)

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, fault-tolerant event streaming platform based on commit log architecture
• A high-throughput, low-latency messaging system with persistent storage
• A publish-subscribe system with topic partitioning for parallel consumption
• Supports strict ordering within partitions, durability via replication, and horizontal scalability
• Typical end-to-end latency: 5-200ms (producer to consumer, varies with configuration)

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:

• Sub-second to low-seconds latency requirements (typically <5 seconds end-to-end)
• Multiple independent consumers reading the same event stream (fan-out pattern with consumer groups)
• Event-driven microservice architectures requiring decoupling
• High throughput scenarios (thousands to millions of events/second)
• Broadcasting events to multiple downstream systems with guaranteed ordering per partition
• Event replay and reprocessing capabilities (time-travel debugging, recomputing aggregates)

Performance Benchmarks:

• Throughput: 100K-2M messages/sec per broker (depends on message size, replication factor, hardware)
• Latency: P99 typically 5-200ms end-to-end in well-tuned clusters (producer to consumer)
• Storage: Configurable retention from hours to weeks (limited by disk capacity)
• Scalability: Near-linear horizontal scaling by adding brokers and partitions

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
• Typical micro-batch processing: 5-10 second intervals minimum

Databricks IS NOT:

• A low-latency event transport system (Structured Streaming has trigger latency, not suitable for sub-second requirements)
• A message broker or message queue system
• A direct replacement for Kafka's pub/sub and event replay capabilities

Ideal Use Cases for Databricks:

• Complex data transformations and aggregations (joins, windowing, ML)
• Processing large volumes stored in data lakes (TB to PB scale)
• Delta Lake ACID transactions and time travel
• Machine learning pipeline orchestration
• Processing minutes-to-hours worth of accumulated data
• SQL-based analytics on large datasets

Performance Benchmarks:

• Throughput: Process 1-100+ TB/hour (cluster-dependent, scales linearly)
• Latency: Structured Streaming trigger intervals configurable from 500ms+, but practical production is typically 5-30 seconds for stability
• Data Processing: 10-100x faster than traditional MapReduce for iterative algorithms (Spark paper, UC Berkeley)
• Scalability: Horizontal scaling via cluster auto-scaling, supports 1000+ node clusters

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 Source Connectors and Databricks JDBC Explained

When ingesting data from relational databases, you have multiple options. Two common approaches are using Kafka JDBC Source Connectors (which push data into Kafka topics) versus Databricks reading directly via JDBC. Understanding the trade-offs is critical for choosing the right ingestion pattern.

Kafka JDBC Source Connector:

• Runs as part of Kafka Connect framework, continuously polls source databases
• Ingests data into Kafka topics for downstream consumption
• Supports incremental ingestion using incrementing column or timestamp column modes
• Provides offset tracking and basic fault tolerance
• Enables multiple consumers to read the same dataset from Kafka

When to Use Kafka JDBC Source Connector:

• Multiple downstream consumers need the same database data
• You want to decouple database load from consumer processing
• Need to buffer data in Kafka for replay or late-arriving consumers
• Moderate data volumes with periodic polling (typically minutes to hours)
• Simple point-to-point data movement into event streaming pipeline

Kafka JDBC Source Connector Performance:

• Throughput: 5K-50K rows/sec (depends on network, query complexity, poll interval)
• Latency: Defined by poll interval (typically 1-15 minutes between polls)
• Database Impact: Polling queries create periodic load on source database
• Fault Tolerance: Kafka Connect handles failures and retries; offset tracking per table

Databricks JDBC Reads:

• Direct JDBC connection from Databricks cluster to source database
• Reads data on-demand during notebook/job execution
• Can leverage parallel reads via partitioning (partition column, number of partitions)
• No intermediate storage layer—data flows directly into Spark DataFrames
• Simpler architecture for batch ETL workloads

When to Use Databricks JDBC:

• Single consumer (only Databricks needs the data)
• Batch processing is acceptable (hourly, daily schedules)
• Real-time latency is not critical (minutes to hours acceptable)
• Don't need event replay or historical data buffering
• Want simpler architecture without Kafka infrastructure

Databricks JDBC Performance:

• Throughput: 10K-100K+ rows/sec with parallel reads (partition-dependent)
• Latency: Job-scheduled (minutes to hours between runs)
• Database Impact: Direct query load during job execution; can use predicates to reduce load
• Scalability: Horizontal scaling via Databricks cluster size and JDBC partition parallelism

Kafka JDBC Source Connector Is NOT Good For:

• High-frequency real-time data ingestion (<1 min polling creates excessive database load)
• Large table full scans every poll cycle (>100GB tables)
• True Change Data Capture (polling can't detect deletes reliably)
• Sub-minute latency requirements (polling-based, not event-driven)

Databricks JDBC Is NOT Good For:

• Multiple independent consumers needing the same data (creates redundant database queries)
• Real-time or near-real-time requirements (<5 minutes)
• Event replay scenarios (no persistent buffer)
• When you need to decouple producers from consumers

Important: Both JDBC approaches put query load on source databases. For production systems with high-frequency updates or large tables, prefer log-based CDC (Debezium/Kafka Connect) which reads transaction logs instead of polling tables.

Ingestion Pattern Comparison: CDC vs JDBC Polling vs Direct JDBC

Kafka Connect with CDC (Debezium - Recommended for Production):

• Purpose-built for CDC, incremental loads, and streaming data ingestion
• Integrates with Debezium (MySQL, PostgreSQL, SQL Server, Oracle, MongoDB CDC)
• Provides automatic offset tracking, at-least-once/exactly-once semantics, and schema evolution via Schema Registry
• Enables real-time database replication into Kafka topics
• Throughput: 10K-500K+ events/sec per connector task (depends on source database, connector type, configuration)
• Latency: Sub-second to few seconds from database commit to Kafka topic
• Reliability: Transaction log-based CDC ensures no data loss; automatic failover and recovery
• Database Impact: Minimal—reads transaction logs, not query load on tables

Kafka JDBC Source Connector:

• Polling-based ingestion into Kafka topics using SQL queries
• Supports incrementing column or timestamp modes for incremental loads
• Good for tables without transaction log access or simpler use cases
• Best for: Moderate-frequency updates (5+ minute intervals), multiple downstream consumers
• Limitation: Cannot reliably capture deletes; polling creates database load

Databricks JDBC (Direct Reads):

• Direct connection from Databricks to source database without intermediate layer
• Batch-oriented approach with scheduled job execution
• Parallel reads via JDBC partitioning (split large queries across Spark executors)
• Best for: Single consumer (Databricks only), batch processing (hourly/daily), simpler architecture
• Limitation: No buffering, no replay, creates direct load on source during each execution

Databricks Auto Loader (Cloud Storage Files):

• Cloud-native file ingestion (S3, ADLS, GCS) with automatic schema inference
• Scalable ingestion of millions of files with incremental processing
• Throughput: Process 100GB-10TB+ per hour depending on cluster and file formats
• Best for: File-based data exports, data lake ingestion, Parquet/CSV/JSON files

Decision Matrix: When to Use What

Cost Analysis: TCO Comparison

Understanding the total cost of ownership is critical for architectural decisions. Here's a breakdown based on data volume and latency requirements:

Note: Cost estimates below are approximate and vary significantly based on region, cloud provider, instance types, and specific requirements. Use these as directional guidance, not exact quotes. Always validate with your cloud provider's pricing calculator.

Kafka Infrastructure Costs (Self-Managed on AWS/GCP):

• Small Scale (100GB/day): $1.5K-4K/month (3-5 brokers on m5.large/n1-standard-4, storage, networking)
• Medium Scale (1TB/day): $8K-18K/month (8-12 brokers on m5.xlarge, increased storage, monitoring)
• Large Scale (10TB/day): $40K-90K/month (40+ brokers, high-performance instances, enterprise tooling)
• Additional: Engineering overhead (1.5-3 FTEs for operations ~$150K-450K/year), Schema Registry, monitoring tools

Confluent Cloud (Managed Kafka):

• Small Scale: $400-1.5K/month (Basic tier, pay-as-you-go based on throughput)
• Medium Scale: $4K-12K/month (Standard/Dedicated clusters with HA)
• Large Scale: $25K-70K/month (Enterprise tier with advanced features)
• Advantage: Reduced operational overhead (0.5 FTE vs 2-3 FTE), managed upgrades, built-in monitoring, SLA guarantees

Databricks Costs:

• Compute: $0.07-0.65/DBU (Databricks Unit) + underlying cloud compute costs
• Small Workloads (100GB/day): $800-2.5K/month (batch processing, 4-8 hours daily jobs)
• Medium Workloads (1TB/day): $4K-12K/month (continuous/streaming jobs, auto-scaling)
• Large Workloads (10TB/day): $25K-80K/month (large clusters, 24/7 streaming, ML workloads)
• Storage: Cloud storage (S3/ADLS/GCS) typically $23/TB/month (standard tier)

Kafka JDBC Source Connector Costs:

• Kafka Connect workers: $300-2K/month (2-5 workers for fault tolerance)
• Kafka cluster storage: Depends on retention and throughput (included in Kafka costs above)
• Database load impact: Polling queries create periodic CPU/IO load on source
• Best for: When you need the data in Kafka for multiple consumers

Databricks JDBC Ingestion Costs:

• Databricks compute for JDBC reads: Minimal if batch (hourly/daily) - $200-1K/month
• Database load impact: Potential performance degradation requiring larger DB instances (cost varies)
• Network egress: $0.09/GB from AWS RDS, varies by cloud provider
• Total: $400-4K/month depending on volume and frequency
• Best for: Single consumer (Databricks only), simpler architecture without Kafka

Cost Decision Framework:

• Real-time requirement + multiple consumers: Kafka justified despite higher infrastructure cost
• Batch processing only: Databricks alone saves 30-50% vs Kafka + Databricks combined
• CDC required: Kafka Connect + Kafka more reliable and often more cost-effective long-term than polling alternatives
• Low volume (<50GB/day) + infrequent updates: JDBC/direct ingestion most cost-effective
• Managed services: Often 20-40% more expensive than self-managed but save significant operational costs

Rule of thumb: If you don't have a sub-5-second latency requirement or multiple consumers, question whether you need Kafka. The operational complexity and cost may not justify the benefits.

Real-World Scenarios and Best Solutions

The following scenarios represent common enterprise data engineering challenges. Solutions are based on production implementations, considering factors like latency, reliability, cost, and operational complexity.

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, single consumer

Solution: Databricks JDBC direct read → Snowflake

Why: Simple periodic batch loads with single consumer don't need Kafka infrastructure. Databricks can read via JDBC, transform, and write to Snowflake efficiently.

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 Structured Streaming uses micro-batch processing (configurable trigger intervals, typically 5-30 seconds in production for stability). While technically capable of lower latencies, it's not designed for the sub-second event delivery that message brokers like Kafka provide.

Why teams make this mistake: Attempting to simplify architecture by consolidating on a single platform; misunderstanding Structured Streaming capabilities.

Impact: SLA violations on latency-sensitive workloads, poor user experience for real-time features, inability to meet sub-5-second requirements.

Fix: Use Kafka as the transport and buffering layer; consume from Kafka in Databricks for transformation and analytics.

❌ Using Kafka as a Transformation Tool

Kafka Streams and ksqlDB can handle stateful stream processing, but they're optimized for lightweight transformations (filtering, mapping, simple aggregations). Complex ETL involving multi-way joins, window functions, machine learning, or large state stores are better suited for Spark/Databricks.

Why teams make this mistake: Kafka Streams is convenient and avoids additional infrastructure; desire to keep processing close to the data.

Impact: Complex, difficult-to-debug stream processing topology; limited ML/AI capabilities; operational challenges with state store management at scale.

Fix: Use Kafka for event transport and simple enrichment; move complex transformations, aggregations, and ML to Databricks/Spark.

❌ JDBC for Large Table Full Scans

Full table scans via JDBC create significant load on source databases (CPU, I/O, memory for result sets) and network bottlenecks. For large tables (>100GB), this approach doesn't scale and impacts OLTP workloads.

Why teams make this mistake: Simplest initial implementation; no CDC infrastructure setup required; familiar SQL-based approach.

Impact: Source database performance degradation affecting production transactions; extended batch windows; network saturation; job failures during peak hours.

Fix: Implement log-based CDC using Debezium/Kafka Connect for incremental-only replication; use JDBC only for initial snapshot loads with throttling.

❌ Daily Full-Table Loads Instead of CDC

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

Why teams make this mistake: Simpler logic; no need to track changes or handle deletes.

Impact: Wasted compute ($4K-40K/month depending on scale), longer processing windows, data freshness issues.

Fix: Migrate to log-based CDC capturing only inserts, updates, deletes. ROI typically 4-8 months.

❌ Multiple Consumers Directly Writing to Databases

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

Why teams make this mistake: Each team builds their own pipeline independently.

Impact: Inconsistent data across systems, database connection exhaustion, difficult troubleshooting.

Fix: Single source of truth pattern with Kafka as the distribution layer.

❌ Over-Engineering with Kafka for Simple Batch Workloads

Not every pipeline needs real-time capabilities. Assess whether hourly/daily batch processing suffices.

Why teams make this mistake: "We might need real-time in the future" or following trendy architectures.

Impact: 2-3x higher costs, operational complexity, longer time-to-market.

Fix: Start simple with batch processing. Migrate to streaming when real-time requirements emerge.

Quick Decision Rules

Use these rules of thumb for rapid architectural decisions:

• Latency requirement < 5 seconds: Use Kafka for transport layer
• Latency requirement 5-60 seconds: Kafka or Databricks Structured Streaming (depending on complexity)
• Latency requirement > 1 hour: Batch processing with Databricks/Spark sufficient
• Complex transformations (multi-way joins, ML, windowing): Use Databricks/Spark
• Data source is RDBMS + need true CDC: Use Kafka Connect with Debezium (log-based)
• Data source is RDBMS + polling acceptable: Kafka JDBC Source Connector (if multiple consumers) or Databricks JDBC (if single consumer)
• One-time or infrequent bulk loads (>1 hour intervals): Databricks JDBC acceptable for <500GB tables
• Multiple downstream consumers (3+): Use Kafka (with CDC or JDBC connector) for fan-out
• Single consumer, batch processing: Databricks JDBC direct read may suffice
• Event replay required: Use Kafka (provides time-travel via log retention)
• Schema evolution critical: Use Kafka with Schema Registry

The Modern Data Architecture Pattern

Most enterprise-scale data platforms implement a layered architecture that combines these technologies strategically:

Source Systems → Kafka Connect (CDC) → Kafka Topics → Databricks/Spark → Data Warehouse → BI/ML Applications
                                                    ↓
                                             Stream Processing (Kafka Streams/Flink)
                                             Real-time Applications

This architecture delivers:

• Real-time event streaming via Kafka (5-200ms latency for operational use cases)
• Scalable compute via Databricks for complex transformations, aggregations, and ML pipelines
• Decoupling between data producers and consumers via publish-subscribe pattern
• Independent scalability at each layer (ingest, process, store, serve)
• Fault tolerance and replay via Kafka's durable log storage and Spark's checkpointing

Migration Paths: Evolving Your Architecture

From JDBC Polling to Kafka Connect CDC

When to migrate: When batch windows become too long, data freshness requirements tighten, or database load becomes problematic.

Migration approach:

1. Set up Kafka cluster and Debezium/CDC connectors in parallel
2. Run dual-write mode: JDBC batch + CDC streaming simultaneously
3. Validate data consistency for 2-4 weeks
4. Switch downstream consumers to Kafka topics
5. Decommission JDBC batch jobs

Timeline: 6-12 weeks for full migration

ROI: Typically positive within 8-12 months due to reduced batch compute and improved data freshness

From Kafka to Kafka + Databricks

When to add Databricks: When transformation logic becomes too complex for Kafka Streams, ML pipelines are needed, or SQL-based analytics are required.

Migration approach:

1. Keep Kafka as event transport layer (no changes)
2. Add Databricks as consumer of Kafka topics
3. Implement medallion architecture (Bronze → Silver → Gold)
4. Gradually migrate complex transformations from Kafka Streams to Databricks
5. Maintain simple enrichments in Kafka Streams if needed

Timeline: 8-16 weeks depending on complexity

From Batch-Only Databricks to Hybrid Streaming

When to add streaming: When real-time dashboards, alerts, or sub-5-minute latency requirements emerge.

Migration approach:

1. Identify high-priority real-time use cases
2. Introduce Kafka for those specific data flows
3. Keep batch processing for non-time-sensitive workloads
4. Use Databricks Structured Streaming to consume Kafka
5. Hybrid mode: Real-time + batch coexist

Timeline: 4-8 weeks for initial streaming use cases

Hybrid Architecture Pattern (Recommended)

Most mature data platforms don't choose "Kafka OR Databricks" but rather use both strategically:

• Hot path (real-time): Kafka → Stream Processing → Operational systems
• Warm path (near real-time): Kafka → Databricks Streaming → Data Warehouse
• Cold path (batch): Cloud Storage → Databricks Batch → Data Warehouse

This lambda/kappa hybrid provides flexibility to optimize each workload independently.

Conclusion

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

• Kafka: Event streaming and real-time transport layer (5-200ms latency, 1M+ events/sec)
• Databricks: Heavy compute for ETL, transformations, and ML (5s+ latency, TB-scale processing)
• Kafka CDC (Debezium): Transaction log-based real-time replication (sub-second latency, exactly-once semantics)
• Kafka JDBC Source Connector: Polling-based ingestion into Kafka for multiple consumers (minutes latency)
• Databricks JDBC: Direct batch reads for single-consumer ETL (cost-effective for infrequent loads)

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.

Key Principle: The optimal architecture aligns with your specific latency, scale, and complexity requirements—not necessarily the most advanced technology stack. Start with clear requirements, validate assumptions with prototypes, and evolve your architecture based on measured needs rather than theoretical possibilities.

Action Items: Evaluate Your Architecture

Ask yourself these questions about your current or planned data platform:

1. What's your actual P95 latency requirement? (Not what you might need someday)
2. How many downstream consumers need the same data?
3. What's your data volume today and projected in 12 months?
4. Do you need event replay or just point-in-time data?
5. What's the cost delta between options for your specific workload?

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, cost analysis, and migration paths outlined here to make informed choices that balance functionality, cost, and operational complexity.

Remember: Over-engineering costs money and time. Under-engineering costs reliability and scalability. The sweet spot is understanding exactly what you need—and building precisely that.