A 2025 deep-dive comparing Parquet, Delta Lake, Iceberg, ORC, Hudi, ClickHouse, DuckDB, Snowflake, BigQuery, and Arrow. Understand features, costs, and use cases to pick the right columnar storage engine for analytics at any scale.
The best columnar storage engines in 2025 are Apache Parquet, Delta Lake, and Apache Iceberg. Parquet excels at space-efficient compression and broad ecosystem support; Delta Lake offers ACID transactions and time travel on top of Parquet; Iceberg is ideal for petabyte-scale tables needing schema evolution and multi-engine compatibility.
Columnar storage engines store data by column rather than by row, enabling high compression, vectorized execution, and reduced I/O. In 2025 they underpin cloud warehouses, data lakehouses, and in-process analytics, shrinking costs while accelerating queries.
Because modern analytics relies on scanning billions of rows, columnar formats like Parquet and ORC reduce read time by orders of magnitude.
New table layers—Delta Lake, Iceberg, and Hudi—add ACID guarantees to lake storage, letting teams retire traditional warehouses without sacrificing reliability.
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We evaluated engines on seven weighted criteria: feature depth (25%), performance and reliability (20%), integration breadth (15%), ease of use (15%), pricing or TCO (10%), community momentum (10%), and vendor support (5%). Data came from 2025 benchmark studies, official docs, and verified user reviews.
The resulting scores were normalized to 100 and ordered from highest to lowest.
Only engines with active releases in 2025 and production adoption across multiple industries made the list.
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Yes. Parquet’s open spec, adaptive encoding, and broad language bindings keep it the default on S3, ADLS, and GCS.
Benchmarks published at Data Council 2025 show Parquet cutting scan time 40% versus ORC on nested data, while its dictionary and RLE encodings trim storage by 60% over raw CSV.
Parquet’s weakness is the lack of built-in ACID or schema evolution metadata, pushing teams toward table layers like Iceberg or Delta for transactionality.
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Databricks Delta Lake 3.0, open-sourced in April 2025, layers scalable ACID transactions and time travel onto Parquet files. New Z-Order Multi-Clustering optimizes selective queries, and the Unity Catalog integration brings fine-grained access controls.
Licensing is Apache 2.0, but advanced features like Change Data Feed and Liquid Clustering are gated to Databricks Premium, making true TCO higher than pure OSS alternatives.
Iceberg 1.5 (January 2025) introduced branch & tag versioning and optimistic concurrency, letting Snowflake, Trino, Flink, and Spark all read the same table without locks. Hidden-partitioning eliminates data skew, and iceberg-format v2 statistics speed selective queries.
Early adopters praise Iceberg’s vendor-neutral governance, though its Java-only reference implementation still limits native support in some Python stacks.
ORC excels on wide fact tables with primitive columns. Its lightweight indexes and bloom filters enable predicate pushdown that beats Parquet by 25% on TPC-DS Query 48 (2025 Starburst test). Hive and Presto users still rely on ORC, but limited Python tooling hinders data-science adoption.
Hudi’s Copy-on-Write tables now support asynchronous compaction and Row-Level Delete APIs (0.14, March 2025). Built-in CDC ingestion shortens data freshness to minutes on low-cost object storage. However, the operational overhead of compaction and a smaller community keep Hudi behind Delta and Iceberg for general-purpose lakehouses.
ClickHouse 24.1 delivers sub-second aggregation on trillions of rows, thanks to sparse indexes and code-generated vector execution. Its own columnar format compresses telemetry 8× over gzip. Free, self-hosted deployments attract cost-sensitive observability teams, though advanced JOIN operations require careful schema design.
DuckDB 1.0 (released February 2025) embeds a full columnar engine in a single library—no server required. Analysts query Parquet directly from Python or JavaScript notebooks with vectorized speed. Con: single-node limits cap scalability to a few hundred GB.
Snowflake’s proprietary micro-partition format, upgraded in the Summit 2025 release, now supports 10 TB partitions with adaptive pruning. Performance is excellent, but storage is locked to Snowflake’s service and costs $40/TB/month list, raising concerns about vendor lock-in.
BigQuery’s Capacitor columnar format remains fully managed and serverless. The 2025 edition adds compressed materialized views and automatic Parquet export. Pricing at $5 per TB scanned suits bursty workloads but can spike for exploratory data science.
Arrow Flight 1.2 standardizes zero-copy column transport across JVM, C++, and Python. While Arrow isn’t a long-term storage format, its in-memory columns power rapid interchange between engines like Snowflake, DuckDB, and Galaxy, making it vital in 2025 analytic pipelines.
Batch analytics on petabyte data favors Iceberg or Delta atop Parquet. Streaming upserts lean toward Hudi. Real-time observability loves ClickHouse. Notebook-centric exploration chooses DuckDB. Cloud-native shops that prioritize managed services pick Snowflake or BigQuery.
Galaxy, the modern SQL editor with an AI copilot, connects to Parquet, Delta Lake, and ClickHouse alike. Its context-aware autocomplete understands column statistics embedded in these engines, generating optimized SQL that embraces partition pruning and vector scans. With Galaxy Collections, teams share vetted queries against columnar stores without pasting SQL fragments across chat tools—accelerating insights while keeping costs in check.
ClickHouse 24.1 tops 2025 benchmarks for aggregation speed, hitting trillions of rows per second on commodity hardware. For object-store formats, Parquet files managed by Delta Lake or Iceberg deliver the best scan throughput.
Parquet boasts broader ecosystem tooling and better nested-data compression, while ORC offers superior predicate pushdown on wide tables. Most teams choose Parquet plus a transactional layer (Delta, Iceberg) for flexibility.
Galaxy’s AI copilot reads Parquet and Iceberg metadata to auto-suggest partition filters and efficient aggregations. Shared Collections let teams endorse optimal SQL, preventing costly full-table scans on columnar stores.
Yes. Iceberg tables can be queried by Spark, Flink, Trino, ClickHouse, and Snowflake simultaneously. Arrow Flight enables zero-copy data exchange between DuckDB, Python, and BI tools, reducing format conversions.
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