Why Parquet Is the Default Choice for Big Data Storage

With data volumes exploding, efficient storage and fast analytics are critical. Since its debut in 2013, Apache Parquet has become the de‑facto standard for big‑data storage. The article first contrasts row‑oriented formats (e.g., CSV, Avro) that store complete rows sequentially—suitable for OLTP—with column‑oriented formats like Parquet that store each column’s values together, which benefits OLAP queries that touch only a subset of columns.

Fundamental Difference: Row vs. Column Storage

Row storage keeps all fields of a record together, while column storage isolates each column’s data, allowing queries to read only the needed columns (projection push‑down) and to skip entire row groups based on column statistics (predicate push‑down).

Parquet’s Core Design: More Than Simple Columnar Storage

Parquet introduces a three‑level hierarchy:

Row Group : a file is split into multiple row groups, each containing up to one million rows by default.

Column Chunk : within each row group, each column has its own chunk holding all values for that column.

Page : each column chunk is further divided into pages (max 1 MB), the smallest read/write unit, with data pages and dictionary pages.

This structure enables projection and predicate push‑down. For example, a query that selects only Product and Country columns can skip all other columns, and a WHERE clause on Product = 'socks' can bypass row groups where the product column never matches.

Practical Example of Push‑Down

Given a table of 100 million sales rows, a query for "customers from Beijing in 2023" can:

Read only the "customer_region" and "sale_date" columns (projection).

Skip row groups whose date ranges fall outside 2023 (predicate).

The engine may read as little as 10 % of the raw data, dramatically speeding up the query.

Efficient Compression and Encoding

Columnar storage naturally compresses better because values in a column share type and distribution. Parquet applies several encodings:

Dictionary encoding for low‑cardinality columns (e.g., country).

Run‑length encoding to collapse consecutive repeats.

Bit‑packing for small integer ranges.

It supports multiple compression algorithms, allowing trade‑offs between speed and ratio:

Snappy – balanced speed and ratio (default).

GZIP – higher ratio, slower.

ZSTD – modern, good balance.

LZ4 – fast, lower ratio.

Brotli – high ratio.

Combined, these techniques can shrink a Parquet file to 75‑90 % of the size of an equivalent CSV.

Hands‑On: Reading and Writing Parquet

Python (pandas + pyarrow) :

pip install pandas pyarrow

import pandas as pd
# Create example DataFrame
data = {
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'salary': [70000, 80000, 90000]
}
df = pd.DataFrame(data)
# Write to Parquet with Snappy compression
df.to_parquet('example.parquet', engine='pyarrow', compression='snappy')
# Read back
df_parquet = pd.read_parquet('example.parquet', engine='pyarrow')
print(df_parquet)

ClickHouse can query Parquet files directly without loading them:

DESCRIBE TABLE file('house_prices.parquet')
SELECT toYear(toDate(date)) AS year,
       round(avg(price)) AS price
FROM file('house_prices.parquet')
WHERE town = 'LONDON'
GROUP BY year
ORDER BY year ASC;

Performance‑Tuning Tips

Set an appropriate row‑group size (larger groups improve compression but need more memory).

Choose a compression algorithm that matches your workload (Snappy for speed, GZIP for size).

Avoid many tiny Parquet files; aim for files of several hundred MB to reduce metadata overhead.

Sort data by frequently filtered columns before writing to improve predicate push‑down.

Use dictionary encoding wisely—effective for low‑cardinality columns, detrimental for high‑cardinality ones.

Parquet’s Ecosystem and Future

Parquet is a cornerstone of the big‑data stack, integrating seamlessly with Apache Spark, Hadoop, Drill, and newer lake‑table formats such as Delta Lake, Iceberg, and Hudi, which add transaction and versioning layers on top of Parquet. Ongoing improvements include more efficient encodings (byte‑stream split), better support for nested types, and tighter Apache Arrow integration.

Conclusion

Parquet’s combination of columnar storage, multi‑level row‑group architecture, and powerful compression/encoding makes it the default choice for big‑data storage, delivering superior query performance and storage efficiency for data scientists, analysts, and engineers.