Streaming Analytics with Spark and Flink

Streaming analytics helps teams react to data as it arrives. Spark and Flink are two popular engines for this work. Spark shines with a unified approach to batch and streaming and a large ecosystem. Flink focuses on continuous streaming with low latency and strong state handling. Both can power dashboards, alerts, and real-time decisions.

Differences in approach

  • Spark is versatile for mixed workloads, pairing batch jobs with streaming via Structured Streaming. It’s easy to reuse code from ETL jobs.
  • Flink is built for true stream processing, with fast event handling, fine-grained state, and low latency guarantees.
  • Spark often relies on micro-batching, while Flink aims for record-by-record processing in most cases.

Choosing the right tool

  • If your team already works with Spark for nightly jobs, Structured Streaming can be a smooth upgrade for near real-time needs.
  • If you need very low latency, complex event processing, or long-running state, Flink can be a better fit.
  • For simple dashboards and alerting, both can work, but consider operational simplicity and the maturity of your data sources.

Typical pipeline patterns

  • Ingest from Kafka or a message bus, apply filters, enrich with lookups, and write results to a data lake or database.
  • Use windowed aggregations to summarize events over time, such as counts per minute or average values per 5 seconds.
  • Combine event time and processing time, then handle late events with allowed lateness and watermarks.
  • Implement fault tolerance with checkpointing and exactly-once sinks when possible.

A simple scenario to picture

  • A retailer streams click events from Kafka. The app groups them into 1-minute tumbling windows, counts clicks per user, and writes results to a dashboard database. Alerts fire if counts spike unexpectedly.

Operational tips

  • Separate stateful processing from sinks to simplify upgrades and scaling.
  • Use idempotent sinks and consistent checkpointing to avoid duplicates.
  • Monitor latency, throughput, and state size; scale resources before bottlenecks appear.
  • Plan for deployment on containers or Kubernetes, with clear autoscale rules.

Key decisions for your team

  • Align the choice with latency goals, team skills, and existing data flows.
  • Start small with a single end-to-end pipeline, then iterate on window sizes and fault tolerance.
  • Build observability into the pipeline from day one.

Key Takeaways

  • Spark and Flink both enable real-time analytics, but they suit different workloads and latency needs.
  • Windowed patterns and robust fault tolerance are central to reliable streaming pipelines.
  • Start with clear requirements, then choose the engine that best fits your latency, state, and operations.