Real-Time vs Batch Analytics

When should you analyze data? As it happens (real-time) or periodically in chunks (batch)? The answer affects your architecture, costs, and what questions you can answer.

Batch Analytics

Batch processing runs on a schedule - hourly, daily, weekly. You collect data, then process it all at once.

How it works: At midnight, a job kicks off. It pulls all of yesterday's transactions, runs calculations, updates reports. By morning, dashboards show yesterday's numbers.

Examples: - Daily sales reports - Weekly marketing performance - Monthly financial close - Quarterly business reviews

Advantages: - Simpler to build and maintain - More cost-effective for large volumes - Easier to handle complex transformations - Retries are straightforward when things fail

Limitations: - Data is always somewhat stale - Can't react to events as they happen - "How are we doing today?" has no answer until tomorrow

Real-Time Analytics

Real-time (or streaming) processing handles data as it arrives. Events flow continuously through the system.

How it works: When a transaction happens, it immediately flows into the analytics system. Dashboards update within seconds or minutes.

Examples: - Live website traffic monitoring - Fraud detection (must decide instantly) - Operational dashboards (current order backlog) - Real-time personalization

Advantages: - Immediate insights - Can trigger actions based on events - Supports time-sensitive decisions

Limitations: - Much more complex to build - Higher infrastructure costs - Harder to do complex calculations - More things can break

How to Choose

Ask yourself: What decision will I make differently if I have data in 5 seconds vs 5 hours?

If the answer is "none" - batch is probably fine. Most business decisions don't change based on the last 5 minutes of data.

Real-time makes sense when: - Delays cost money (fraud, outages) - Operations need live visibility - Users expect immediate feedback - Competitive advantage requires speed

Batch makes sense when: - You're analyzing trends and patterns - Data arrives in chunks anyway - Transformations are complex - Costs need to stay low

The Hybrid Approach

Most mature data architectures use both:

Batch for deep analytics - Historical analysis, complex aggregations, ML model training. Run overnight, available in the morning.

Real-time for operational needs - Live dashboards for operations, immediate alerting, time-sensitive decisions.

This is sometimes called the "Lambda Architecture" or "Kappa Architecture" depending on implementation details. The key insight: different use cases have different latency requirements.

Cost Implications

Real-time is expensive: - Streaming infrastructure costs more than batch - Data must be processed continuously (not just at 2 AM when compute is cheap) - More engineering complexity means more engineering time - More moving parts means more things to monitor and fix

A rough rule: real-time analytics costs 3-10x more than equivalent batch analytics. Make sure the business value justifies it.

Implementation Considerations

Batch tools: Airflow, dbt, traditional ETL, SQL-based transforms. Mature, well-documented, widely known.

Streaming tools: Kafka, Spark Streaming, Flink, AWS Kinesis. More specialized skills required.

Hybrid tools: Many modern data platforms (Databricks, Snowflake) support both paradigms.

If you're starting out, begin with batch. Get your fundamentals right. Add real-time capabilities for specific use cases that justify the complexity.

Common Mistakes

Real-time for everything - Building streaming pipelines for data that's analyzed monthly. Wasted complexity.

Batch when you need real-time - Daily fraud reports don't stop fraud. Some use cases genuinely need speed.

Underestimating complexity - "We'll just make it real-time" is never as easy as it sounds.

Ignoring operational requirements - Real-time systems need monitoring, alerting, and on-call support. Budget for it.

The Right Question

Don't ask "should we do real-time?" Ask "what specific decisions require real-time data, and what's the cost of not having it?"

Start with business requirements, not technology preferences.

Processing timing is one architectural decision. Learn about building data pipelines and data warehouses for batch analytics.

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Sources: - Confluent: Batch vs Real-Time Processing - Databricks: Batch Processing - AWS: What Is Batch Processing?