Real-time Analytics

When batch processing isn’t enough¶

Batch processing (daily/hourly ETL) is the right choice for most analytical use cases. But there are scenarios where delays cost real money:

Fraud Detection¶

Fraudulent transactions must be detected in seconds, not hours. Real-time scoring: transaction → enrichment (customer history, geolocation, device fingerprint) → ML model → approve/decline. Latency >5s = approved fraud.

Dynamic Pricing¶

E-commerce, ride-sharing, hospitality — prices change based on demand, competition, inventory. Batch pricing with hourly updates means an hour of suboptimal prices. Real-time pricing reacts to events immediately.

IoT & Telemetry¶

Thousands of sensors generate millions of events per minute. Anomaly detection on streaming data — if machine temperature exceeds threshold, alert arrives in seconds, not hours. Predictive maintenance requires real-time feature engineering.

Operational Dashboards¶

Live overview of orders, shipments, SLA, throughput. Supply chain visibility — where every shipment is now, not where it was yesterday. Operators need current state, not historical snapshot.

Real-time platform architecture¶

Apache Kafka — event streaming backbone¶

Kafka isn’t just a message broker. It’s a distributed commit log, event streaming platform and integration backbone in one:

• Guaranteed delivery: At-least-once (default) or exactly-once (transactional)
• Ordering: Per-partition ordering guarantees sequential processing
• Replay: Consumer can read from any offset — reprocessing, debugging, new consumer
• Retention: Configurable (hours to unlimited) — Kafka as source of truth
• Schema Registry: Schema evolution (Avro, Protobuf) — producer and consumer agree on format

Stream Processing¶

Apache Flink — our primary stream processor: - Stateful processing with exactly-once semantics - Event time processing — correct results even with out-of-order events - Windowing: tumbling, sliding, session windows - Complex Event Processing (CEP) — pattern matching over event streams - Savepoints and checkpoints for fault tolerance

Kafka Streams — for simpler transformations: - Library, not cluster — runs as part of your application - Filtering, mapping, aggregation, joins - State stores for local state - Ideal for microservice-based architectures

ksqlDB — SQL over streaming data: - SELECT, WHERE, GROUP BY, JOIN — like SQL, but over infinite streams - Materialized views updated in real-time - Ideal for prototyping and simple use cases

Change Data Capture (CDC)¶

Debezium captures database changes and sends them to Kafka in real-time:

• PostgreSQL, MySQL, SQL Server, MongoDB — supported sources
• Log-based CDC — reads from WAL/binlog, no impact on source database
• Schema propagation — schema changes propagate automatically
• Initial snapshot — first load of entire table, then incremental changes

Real-time sinking¶

Data from Kafka to target systems: - Elasticsearch — full-text search, real-time indexing - ClickHouse — OLAP queries over streaming data - Redis — cache for real-time feature store - Snowflake/Databricks — streaming ingestion to warehouse/lakehouse - S3/ADLS — archival of raw events

Monitoring and operations¶

Kafka monitoring¶

• Consumer lag — how far consumer is behind producer. Growing lag = processing bottleneck
• Throughput — messages/s per topic, partition balance
• Broker health — ISR (in-sync replicas), under-replicated partitions
• Storage — disk usage, retention vs. capacity

Stream processing monitoring¶

• Backpressure — processing is slower than input rate
• Checkpoint duration — how long checkpoint takes (Flink)
• Watermark lag — delay between event time vs. processing time
• State size — growing state = potential memory issue

Alerting¶

• Consumer lag > threshold → scale up consumers
• Broker offline → immediate alert + automatic rebalance
• Processing latency > SLA → investigate bottleneck
• Error rate spike → circuit breaker + dead letter queue

Implementation approach¶

1. Use case assessment (1 week): Identify use cases where real-time brings measurable value. Not everything needs to be real-time.
2. Kafka cluster setup (1-2 weeks): Provisioning (Confluent Cloud or self-managed), topic design, security (mTLS, SASL), Schema Registry.
3. MVP streaming pipeline (2-4 weeks): CDC from primary source → Kafka → stream processing → target system. End-to-end monitoring.
4. Scaling and optimization (ongoing): Additional sources and consumers, performance tuning, partitioning strategies, cost optimization.

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