To design an ETL process that handles both batch and streaming data, start by adopting a hybrid architecture that separates processing layers while unifying storage and orchestration. A common approach is the lambda architecture, which uses a batch layer (for processing historical data) and a speed layer (for real-time streams), with a serving layer to merge results. Alternatively, a kappa architecture simplifies this by treating all data as streams, using tools like Apache Kafka to retain raw data for reprocessing. Tools like Apache Spark (for batch) and Apache Flink (for streaming) can coexist, sharing a distributed storage system like a data lake (e.g., AWS S3 or Delta Lake) to store raw and processed data. This ensures scalability and avoids duplication.
Next, unify transformation logic where possible to maintain consistency. For example, use a framework like Apache Beam, which allows writing pipeline logic once and deploying it in batch or streaming mode via runners like Spark or Flink. This ensures transformations (e.g., filtering, aggregation) behave identically across both data types. For streaming, implement windowing (e.g., sliding or tumbling windows) and handle late-arriving data with watermarking. For batch, schedule jobs to process daily logs or large datasets. Use idempotent operations (e.g., UPSERTs in databases) to avoid duplicates when reprocessing data, and track metadata (e.g., timestamps or offsets) to identify what has been processed.
Finally, ensure fault tolerance and monitoring. Use checkpoints in streaming pipelines (e.g., Kafka consumer offsets) and retries in batch jobs. Deploy monitoring tools like Prometheus and Grafana to track latency, throughput, and errors. For example, a retail system might ingest real-time sales data via Kafka, process it with Flink for instant inventory updates, while a nightly Spark batch job reconciles daily totals. A unified storage layer (e.g., Delta Lake) allows both layers to query the same dataset. This hybrid approach balances low-latency insights with accurate historical analysis, while shared tooling reduces maintenance overhead.