Open Data Lake
An open data lake is a data lake built on open, vendor-neutral technologies so that multiple engines can read and write the same data without lock-in. It separates cheap, durable storage from interchangeable compute, which is especially useful for large time-series and market data.
What Is an Open Data Lake?
In a traditional data lake, raw files live in object storage but each analytics engine often expects its own layout or metadata. An open data lake adds a standardized table format and catalog layer on top of shared object storage.
Technologies like Apache Iceberg, Delta Lake, or Apache Hudi provide this tabular abstraction, while any compatible engine can query it: Spark, Trino, a data lake query engine, or specialized time-series databases.
The result is a lake that behaves more like a multi-engine warehouse, without giving a single vendor control of your storage format.
Key Building Blocks
An open data lake typically relies on:
- Cloud or on-prem object storage as the durable bit bucket.
- An open table format defining schemas, partitions, snapshots, and a metadata manifest.
- A catalog service (for example an Iceberg catalog) that tracks tables and snapshots.
- Multiple engines sharing that tabular layer, often via a tabular data layer.
Typical Architecture at a Glance
This architecture lets financial or industrial teams evolve engines, languages, and vendors while keeping time-series data in a single, open, long-lived lake.
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 Openness Matters for Time-Series and Markets
Capital markets, observability, and industrial telemetry all generate petabyte-scale time-series. With an open data lake, firms can land raw ticks or sensor feeds once, then:
- run intraday analytics using a real-time engine,
- perform overnight risk or compliance on the same tables,
- experiment with new engines without re-ingesting or converting data.
This model complements systems like a time-series database: the DB can serve latency-critical queries while the open lake becomes the long-term, interoperable archive.