A Beginner’s Guide to Apache Parquet
Apache Parquet is a widely used file format in modern data analytics and data engineering. It is especially common in in data lakes and data lakehouse architectures, where performance, scalability, and efficient storage are critical. This guide explains what Parquet is, where it came from, how it is structured internally, and how to query it effectively.
Apache Parquet is a widely used file format in modern data analytics and data engineering. It is especially common in in data lakes and data lakehouse architectures, where performance, scalability, and efficient storage are critical. And if you are creating data lakes and lakehouse, you are very likely working with Parquet files already. But what does the internal structure of a Parquet file actually look like? And why is recommended for building data architectures? This guide explains what Parquet is, where it came from, how it is structured internally, and how to query it effectively.
A Brief History of Apache Parquet
Apache Parquet 1.0 debuted in July 2023 and the name Parquet comes from the small blocks that are usually used in flooring, and are often arranged in patterns. At the time, the Hadoop ecosystem primarily relied on row-based file formats such as CSV, JSON, and Avro. While these formats were flexible and easy to work with, they were inefficient for analytical workloads that typically scan large datasets but only access a subset of columns.
Parquet was designed as a column-oriented format to address this inefficiency. It quickly gained adoption and became an Apache Software Foundation project . Over time, Parquet evolved into a standard storage format for analytics and is now supported by nearly every major data processing engine and cloud platform.
What Is Apache Parquet?
Apache Parquet is a columnar, binary file format optimized for analytical queries. Unlike row-based formats that store complete records together, Parquet stores values from the same column together on disk. This layout allows query engines to read only the columns required for a query rather than scanning entire rows.
As a result, Parquet significantly reduces disk input and output, improves query performance, and enables more efficient compression. It is particularly well suited for read-heavy analytical workloads rather than transactional systems.
Internal Structure of a Parquet File
Parquet files are organized into a hierarchy that balances efficient storage with parallel processing.
At the top level, a Parquet file is divided into row groups. Each row group contains a subset of the total rows in the dataset and is designed to be processed independently, which makes parallel execution efficient in distributed systems.
Within each row group, data is stored in column chunks. Each column chunk contains all the values for a single column within that row group. This organization allows query engines to read only the relevant columns needed for a query.
Column chunks are further divided into pages. Pages are the smallest units of storage and compression in Parquet. They contain encoded and compressed data along with metadata such as statistics, including minimum and maximum values.
At the end of the file, Parquet stores a footer that includes the schema and metadata for all row groups and columns. Because this metadata is centralized, query engines can plan efficient reads without scanning the entire file.
You can view and inspect a Parquet file using a simple parquet-tools CLI in Python. You can use the CLI to view a local Parquet file, or one stored on S3 as shown below:
parquet-tools show s3://bucket-name/prefix/*Likewise, you can also inspect the file schema-- which gives you insight into the number of columns within the file, the size, the columns, compression, etc:
parquet-tools inspect s3://bucket-name/prefix/*Below is the file schema output from the inspect command, using a sample parquet file from the New York City Taxi data set ( yellow_tripdata_2025-01.parquet ).
############ file meta data ############
created_by: parquet-cpp-arrow version 16.1.0
num_columns: 20
num_rows: 3475226
num_row_groups: 4
format_version: 2.6
serialized_size: 11212
############ Columns ############
VendorID
tpep_pickup_datetime
tpep_dropoff_datetime
passenger_count
trip_distance
RatecodeID
store_and_fwd_flag
PULocationID
DOLocationID
payment_type
fare_amount
extra
mta_tax
tip_amount
tolls_amount
improvement_surcharge
total_amount
congestion_surcharge
Airport_fee
cbd_congestion_fee
############ Column(VendorID) ############
name: VendorID
path: VendorID
max_definition_level: 1
max_repetition_level: 0
physical_type: INT32
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 48%)
############ Column(tpep_pickup_datetime) ############
name: tpep_pickup_datetime
path: tpep_pickup_datetime
max_definition_level: 1
max_repetition_level: 0
physical_type: INT64
logical_type: Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false)
converted_type (legacy): NONE
compression: ZSTD (space_saved: 45%)
############ Column(tpep_dropoff_datetime) ############
name: tpep_dropoff_datetime
path: tpep_dropoff_datetime
max_definition_level: 1
max_repetition_level: 0
physical_type: INT64
logical_type: Timestamp(isAdjustedToUTC=false, timeUnit=microseconds, is_from_converted_type=false, force_set_converted_type=false)
converted_type (legacy): NONE
compression: ZSTD (space_saved: 44%)
############ Column(passenger_count) ############
name: passenger_count
path: passenger_count
max_definition_level: 1
max_repetition_level: 0
physical_type: INT64
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 58%)
############ Column(trip_distance) ############
name: trip_distance
path: trip_distance
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 11%)
############ Column(RatecodeID) ############
name: RatecodeID
path: RatecodeID
max_definition_level: 1
max_repetition_level: 0
physical_type: INT64
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 54%)
############ Column(store_and_fwd_flag) ############
name: store_and_fwd_flag
path: store_and_fwd_flag
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8
compression: ZSTD (space_saved: 49%)
############ Column(PULocationID) ############
name: PULocationID
path: PULocationID
max_definition_level: 1
max_repetition_level: 0
physical_type: INT32
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 29%)
############ Column(DOLocationID) ############
name: DOLocationID
path: DOLocationID
max_definition_level: 1
max_repetition_level: 0
physical_type: INT32
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 11%)
############ Column(payment_type) ############
name: payment_type
path: payment_type
max_definition_level: 1
max_repetition_level: 0
physical_type: INT64
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 53%)
############ Column(fare_amount) ############
name: fare_amount
path: fare_amount
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 28%)
############ Column(extra) ############
name: extra
path: extra
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 52%)
############ Column(mta_tax) ############
name: mta_tax
path: mta_tax
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 60%)
############ Column(tip_amount) ############
name: tip_amount
path: tip_amount
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 20%)
############ Column(tolls_amount) ############
name: tolls_amount
path: tolls_amount
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 77%)
############ Column(improvement_surcharge) ############
name: improvement_surcharge
path: improvement_surcharge
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 55%)
############ Column(total_amount) ############
name: total_amount
path: total_amount
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 7%)
############ Column(congestion_surcharge) ############
name: congestion_surcharge
path: congestion_surcharge
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 50%)
############ Column(Airport_fee) ############
name: Airport_fee
path: Airport_fee
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 59%)
############ Column(cbd_congestion_fee) ############
name: cbd_congestion_fee
path: cbd_congestion_fee
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE
compression: ZSTD (space_saved: 47%)Why Columnar Storage Improves Performance
Columnar storage is the defining feature of Parquet and the primary reason for its performance benefits. Since data for each column is stored together, Parquet can skip unused columns entirely during a query. This drastically reduces the amount of data read from disk.
Columnar storage also improves compression efficiency because values within a column are often similar or repetitive. This makes encoding and compression techniques much more effective than they would be in row-based formats.
Additionally, column-level statistics allow query engines to skip entire sections of data when filters are applied, further reducing the amount of data scanned.
Compression and Encoding
Parquet supports multiple encoding techniques such as dictionary encoding and run-length encoding, as well as several compression codecs including LZO, Snappy, GZIP, Zstandard, etc.. These mechanisms reduce file size while maintaining fast decompression speeds.
Snappy is commonly used because it offers a good balance between compression and performance. Zstandard is increasingly popular due to its higher compression ratios with minimal performance impact.
Best Practices for Querying Parquet Data
To fully benefit from Parquet, both data layout and query design matter.
Partitioning data by frequently filtered columns, such as date or region, allows query engines to skip entire directories of data. However, excessive partitioning can create too many small files and negatively impact performance.
Queries should apply filters as early as possible. Predicate pushdown allows query engines to use Parquet’s metadata to avoid reading unnecessary data. Filtering after data is loaded into memory defeats this optimization.
Avoiding small files is critical for performance. Large numbers of tiny Parquet files increase metadata overhead and slow down query planning. Periodic compaction jobs can help consolidate files into optimal sizes.
Using correct data types improves compression and query efficiency. Storing numeric values as numbers and timestamps as proper timestamp types enables better encoding and more effective predicate pushdown.
Common Use Cases for Parquet
Apache Parquet is a foundational format for data lakes and analytical platforms. It is widely used with object storage systems such as Amazon S3, and is foundational part of S3 Tables , which is an object store with built-in Apache Iceberg support and streamline storing tabular data at scale.
For analytics and querying, Parquet is supported in Amazon Athena , Amazon Redshift Spectrum , Amazon EMR . For ingesting data and creating data pipelines, Parquet support is available in AWS Glue , AWS Database Migration Service (DMS) and Amazon Kinesis Data Firehouse can be configured to convert incoming data streams to Parquet before delivering them to S3.
Amazon SageMaker for machine learning, also supports Parquet files for building and training AI and ML models and Parquet's open specification and broad ecosystem support make Parquet a reliable and future-proof choice for analytical data storage.
Conclusion
Apache Parquet has become a standard file format for analytics on AWS because it is efficient, scalable, and well-integrated with modern data tools. Its columnar design, rich metadata, and support for compression allow teams to store large datasets economically while still enabling fast queries.
For beginners, understanding how Parquet works internally and how to structure and query Parquet data effectively can lead to significant performance improvements. Thinking in terms of columns rather than rows is a key mindset shift that unlocks the full potential of Parquet.
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