Time-Series ETL
Time-series ETL adapts classic extract-transform-load flows to high-volume timestamped data. This entry explains how it differs from batch ETL, key patterns for streaming and backfill, and design choices for capital markets, observability, and industrial telemetry.
What Is Time-Series ETL?
Time-series ETL is the set of extract, transform, load workflows tailored to timestamped, mostly append-only data. Sources include Kafka topics, MQTT brokers, REST APIs, log streams, and files such as CSV or Parquet. Unlike general ETL into a data warehouse, the target is usually a time-series database that is optimized for time-based partitioning, high ingest rates, and retention policies rather than slowly changing dimensions.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Why Time-Series ETL Is Different
Time-series workloads are high-volume, unbounded streams where order and timing matter. Pipelines must handle out-of-order ingestion, late arriving data, and varying sampling rates while preserving event time. Transformations frequently include resampling, time bucketing, enrichment with reference data, and unit or timezone normalization. Because analysts expect fresh data for dashboards and trading algorithms, time-series ETL is often continuous rather than nightly, blending real-time flows with periodic historical backfills.
Typical Pipelines and Tools
Broadly there are three patterns. Streaming ETL reads from brokers such as Kafka or MQTT, applies lightweight transforms in a stream processing layer, then writes directly into a time-series engine via protocols like line protocol, JSON, or Protobuf. This resembles streaming time-series ingestion with an explicit transform stage. Batch ETL periodically loads large CSV or Parquet files, often from a data lake, which is useful for backfills and regulatory archives. CDC-based ETL uses Change Data Capture (CDC) to replicate transactional changes into a time-series store for analytics.
Design Considerations and Best Practices
Effective time-series ETL starts with a clear ingestion schema or contract: well-defined timestamps, keys (such as instrument or device IDs), and tags. Pipelines should enforce ordering where possible, attach an ingestion timestamp, and choose time-based partitioning and data retention policy that match query horizons. In capital markets, this means reliably ingesting tick data and order books from multiple venues; in industrial IoT, it means harmonizing noisy sensor feeds for monitoring and predictive maintenance.
For a deeper treatment of ETL vs ELT and CDC for time-series workloads, see Data Integration for Time-Series: ETL, ELT, and CDC.