Real-time analytics processes data as it arrives, enabling instant decision-making. Unlike batch processing that runs hourly or daily, streaming analytics provides insights within seconds or milliseconds. This is critical for fraud detection, dynamic pricing, live dashboards, IoT monitoring, and personalization engines. We build streaming pipelines using Kafka for event ingestion, Flink for stateful stream processing, and real-time data warehouses like ClickHouse or Druid for sub-second queries. Our architectures handle millions of events per second with exactly-once processing guarantees.
Critical for fintech companies detecting fraud in real-time, e-commerce platforms personalizing experiences, IoT companies monitoring devices, gaming platforms tracking player behavior, logistics companies tracking shipments, and any business where stale data means lost opportunities.
Set up Apache Kafka or AWS Kinesis for event ingestion. Configure topics, partitions, replication, and retention policies. Handle backpressure and producer throttling.
Build stream processing jobs with Apache Flink or Spark Structured Streaming. Implement windowing, joins, aggregations, and stateful processing with checkpointing.
Real-time data transformations, filtering, enrichment, and aggregations. Join streams with reference data, deduplicate events, and handle out-of-order data.
Compute rolling averages, session windows, tumbling windows, and sliding windows. Maintain state across millions of keys with RocksDB or Redis.
End-to-end latency < 100ms from event production to consumption. Optimize serialization, network, and processing overhead. Monitor latency with percentile metrics.
Store and query time-series data with ClickHouse, TimescaleDB, or Apache Druid. Efficient compression, downsampling, and retention policies for historical data.
Threshold-based and ML-powered anomaly detection. Trigger alerts to Slack, PagerDuty, or custom webhooks. Configurable alert rules without code deployment.
Decouple services with event-driven patterns. Publish-subscribe, event sourcing, CQRS, and saga patterns. Event schemas with Avro or Protobuf.
Enrich streaming events with dimension tables from data warehouses. Implement CDC (Change Data Capture) to keep reference data fresh in stream processors.
Gracefully handle traffic spikes and slow consumers. Implement rate limiting, buffering strategies, and auto-scaling based on lag metrics.
Ensure no duplicates or data loss with exactly-once processing. Implement idempotent consumers, transactional writes, and two-phase commits.
Live updating dashboards with WebSocket connections. Grafana, Superset, or custom React dashboards visualizing streaming metrics with < 1 second refresh.
Define real-time use cases, establish latency requirements (100ms? 1s? 10s?), estimate event volumes, and identify data sources producing events.
Design streaming architecture with Kafka/Kinesis for ingestion, Flink/Spark for processing, and target sinks (warehouse, database, API). Plan for scaling and fault tolerance.
Deploy Kafka clusters or provision Kinesis streams, set up Flink/Spark clusters with auto-scaling, configure monitoring and alerting infrastructure.
Develop streaming jobs with windowing logic, stateful aggregations, stream enrichment, and error handling. Implement checkpointing for fault tolerance.
Load testing with realistic event volumes, chaos engineering for failure scenarios, validate exactly-once semantics, and benchmark latency percentiles (p50, p95, p99).
Deploy with blue-green strategy, set up real-time monitoring dashboards, configure alerts for lag and errors, and establish on-call runbooks.
Optimize for throughput and latency, tune parallelism and partition counts, implement auto-scaling policies, and continuously monitor cost and performance.
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