Big Data Architectures for Streaming and Batch Analytics

Big data systems today mix streaming and batch analytics to support both fast actions and thorough analysis. A solid architecture uses a shared data backbone—object storage with a clean schema—so different teams can work on the same facts.

Two core modes Streaming analytics processes events as they arrive, delivering near real-time insights. Batch analytics runs on a schedule, handling large volumes for deeper understanding. Both rely on a data lake for raw data and a warehouse or lakehouse for curated views. Plan for schema evolution, data quality checks, and traceability.

Architectural patterns Lambda architecture combines a speed layer with a batch layer. It provides resilience, but can be complex. Kappa architecture keeps a single stream-first pipeline and replays data to support historical analysis, reducing maintenance.

Key components in modern stacks

  • Ingest via Kafka or cloud streams
  • Processing with Spark Structured Streaming or Flink
  • Storage in Parquet/ORC on object stores; Delta Lake helps for upserts
  • Serving layer for BI and dashboards
  • Governance: metadata, schema registry, access control

An example pipeline A retail site ingests clicks and transactions into Kafka. A near real-time processor updates dashboards and a Delta Lake table. A nightly job runs a batch ETL to produce a warehouse table for long-term reporting. The result is a single source of truth that supports both ops and strategy.

Operational tips

  • Keep a clear data owner and lineage
  • Use backward-compatible schema changes
  • Monitor latency, throughput, and errors
  • Lock down access and encrypt sensitive data

Choosing an approach Start with business needs and required freshness. Use a common data model and choose tools you can maintain. Cloud-native patterns help scale on demand and keep costs predictable. For regulated data, include audit trails and retention rules in your metadata.

Key Takeaways

  • Align streaming and batch on a shared data backbone
  • Use lakehouse patterns to reduce duplication
  • Start simple, then evolve as needs grow