Modern businesses operate in real time. Batch processing slows you down:
Analytics & decision-making
Anomaly detection & AI inference
Live product updates & notifications
Event-driven automation & workflows
Real-time pipelines deliver:
Instant insights & dashboards
AI/ML-ready data streams
High-volume, fault-tolerant ingestion
Operational reliability & data freshness
Kafka, Kinesis, Pulsar, DynamoDB Streams, EventBridge
Outcomes: reliable, high-throughput ingestion
Stream processors, enrichment, aggregation, retries
Tools: Flink, Spark Streaming, Lambda
Outcomes: clean, transformed, actionable data
Sync databases in real time for microservices, search, ML updates
Tools: Debezium, AWS DMS, Kafka Connect
Parsing, enrichment, validation, anomaly detection
Tools: Spark Structured Streaming, Flink, AWS Glue
QuickSight, Grafana, Kibana, custom dashboards
Instant visibility & better decision-making
Elasticsearch, OpenSearch, Pinecone, Weaviate
Low-latency indexing for improved user experience
Streaming inference, embeddings, RAG context injection
Tools: SageMaker, MLflow, Triton Server
Lag, throughput, error tracking, alerts, schema evolution
Tools: Datadog, Prometheus, CloudWatch
Architecture Design: Define SLAs, tools, throughput, cost
Implementation: Build ingestion, processing, storage, serving
Validation: Load testing, monitoring setup, failure simulation
Optimization: Tune partitions, throughput, cost, resilience
Scaling & Evolution: Expand pipelines, ML, analytics layers
