What’s The Deal With Apache Parquet?
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Apache Parquet stores data column by column rather than row by row, letting queries read only the columns they need (column pruning) and skip irrelevant row groups via min/max statistics and predicate pushdown. This columnar layout also enables strong compression through dictionary, run-length, and delta encoding, commonly yielding files 5-10x smaller than CSV. Parquet has become the default physical storage format underlying open lakehouse table formats like Apache Iceberg and Delta Lake, queried by engines such as Dremio, Spark, Trino, and DuckDB.
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How Columnar Storage Changes the Economics of a QueryWhy Parquet Compresses So WellParquet as the Foundation of the Open LakehouseTry Dremio Cloud free for 30 daysQuestions this post answers
Why is Apache Parquet faster than CSV for analytical queries?
Parquet stores data column by column instead of row by row, so a query needing only a few columns out of hundreds reads just those columns from disk instead of the entire row. This is called column pruning. Combined with predicate pushdown and row-group statistics (min, max, null count) that let engines skip entire row groups, Parquet drastically reduces the I/O required compared with row-based formats like CSV. daily.dev surfaces practical breakdowns like this for engineers comparing storage formats before a lakehouse migration.
How much smaller are Parquet files compared to CSV?
Parquet files are commonly 5-10x smaller than equivalent CSV files. This comes from columnar storage grouping same-datatype values together, which lets compression techniques work more efficiently: dictionary encoding for repeated low-cardinality strings, run-length encoding for repeated sequences, and delta encoding for monotonically increasing values like timestamps. Engineers weighing storage costs can track format comparisons like this one on daily.dev.
What file format do Apache Iceberg tables use for storage?
Apache Iceberg supports multiple file formats including Avro and ORC, but Parquet has become the default physical storage format for Iceberg tables as well as other table formats like Delta Lake. Engines such as Dremio, Spark, Trino, and DuckDB read the underlying Parquet files on cloud object storage, while Iceberg adds metadata, transactions, and schema evolution on top. daily.dev helps lakehouse builders keep up with how table formats and storage layers fit together.