One table, three readers: QuestDB, Polars and DuckDB on the same Parquet

QuestDB is an open-source time-series database designed for low-latency, high-ingestion workloads that can store table partitions in native format or standard Parquet while querying both seamlessly through SQL. Its example workflow uses a partitioned FX trades table, materialized OHLC views, deduplication keys, and synthetic data to show that SQL, Polars, and DuckDB produce consistent analytical results from the same Parquet-backed history. Older partitions can be converted manually or through Enterprise storage policies, reducing storage in the demonstrated case and remaining writable for late-arriving data, although such writes may require Parquet row-group rewrites and later compaction. QuestDB supports direct Parquet access by external tools, exports tables or query results to Parquet, and in Enterprise can move sealed historical partitions to object storage using Hive-style layouts readable by platforms such as Databricks, Trino, BigQuery, Snowflake, Polars, and DuckDB. The approach aims to avoid separate export pipelines and inconsistent data copies, while imposing operational constraints such as keeping QuestDB as the sole writer to managed remote files, planning cold-storage sealing beyond correction windows, and accounting for slower object-storage queries.

No tracked trend matches for this post yet.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.