Avro vs. Parquet: A Complete Comparison for Big Data Storage

Differences Between Avro and Parquet

Avro and Parquet are widely used data storage formats in big data ecosystems, but they serve different purposes and excel in different scenarios. Below is a detailed comparison of their differences.

Data structure

• Avro: Uses a row-based storage format, meaning entire records are stored sequentially. This makes Avro efficient for write-heavy workloads where data needs to be quickly appended.
• Parquet: Uses a columnar storage format, where data is stored by columns rather than rows. This structure is beneficial for analytical queries that require reading only specific columns rather than entire rows.

Visualizing row-based vs. columnar storage

To better understand the difference between row-based (Avro) and columnar-based (Parquet) storage, consider this dataset:

How Avro stores data (row-based):

• [101, Alice, 25, New York]
• [102, Bob, 30, Chicago]
• [103, Carol, 28, Seattle]

Each record is stored sequentially, making it efficient for write operations but slower for querying specific fields.

How Parquet stores data (columnar-based):

• ID: [101, 102, 103]
• Name: [Alice, Bob, Carol]
• Age: [25, 30, 28]
• City: [New York, Chicago, Seattle]

Each column is stored separately, making it faster to retrieve only the required columns (e.g., querying just the "Age" column).

Schema evolution

• Avro: Designed for schema evolution, allowing new fields to be added or modified without breaking compatibility with older data. The schema is stored with the data, making it self-descriptive.
• Parquet: Supports schema evolution but is less flexible than Avro. Schema changes can be more complex, especially when adding or modifying column structures.

Compression and storage efficiency

• Avro: Uses compact binary encoding, but it does not leverage columnar compression techniques, leading to larger file sizes when compared to Parquet.
• Parquet: Uses columnar compression techniques like dictionary encoding, run-length encoding, and bit-packing, making it more space-efficient, especially for large datasets.

Query performance

• Avro: Not optimized for analytical queries since it stores data row-wise. Scanning large datasets requires reading entire records, leading to slower query performance in analytics workloads.
• Parquet: Optimized for fast queries, especially when only a subset of columns is needed. Its columnar structure allows for selective scanning, improving performance in big data analytics.

Write and read efficiency

• Avro: Provides fast write speeds since it stores data row-by-row. However, read operations can be slower for analytics because entire rows must be read.
• Parquet: Optimized for read performance but can have slower write speeds due to the overhead of columnar storage and compression techniques.

Avro vs. Parquet Comparison Table

When to Use Avro vs Parquet

Choosing between Avro and Parquet depends on your specific use case, workload characteristics, and data processing requirements. Below are practical guidelines to help you determine when to use each format.

When to use Avro

Avro is best suited for scenarios that require efficient serialization, schema evolution, and row-based storage. Consider using Avro in the following situations:

• Streaming and event-driven data pipelines: Avro is widely used in real-time data streaming platforms like Apache Kafka due to its compact binary format and efficient serialization.
• Schema evolution is frequent: Avro is the preferred choice when dealing with evolving data schemas since it embeds the schema within the file, allowing smooth modifications without breaking compatibility.
• Inter-system data exchange and integration: Avro is commonly used to transmit structured data between different applications due to its self-descriptive schema and support for multiple programming languages.
• Write-heavy workloads: If your workflow involves frequent writes or appends, Avro performs better than Parquet. It stores data in sequential rows without the overhead of columnar indexing.
• Log storage and raw data collection: Avro is often used in logging systems or storing raw unprocessed data because of its efficient row-wise storage and ability to capture detailed records.

When to use Parquet

Parquet is ideal for analytical workloads, big data processing, and storage efficiency. Use Parquet when:

• Analytical queries and data warehousing: If your primary goal is fast query performance in data lakes, data warehouses, or OLAP (Online Analytical Processing) systems, Parquet’s columnar structure is highly efficient.
• Read-heavy workloads: Parquet is optimized for scenarios where data is read frequently but written less often, such as BI dashboards, reporting, and batch analytics.
• Big data and distributed processing: Parquet is the preferred format for big data frameworks like Apache Spark, Hive, Presto, and Snowflake, where columnar storage reduces I/O costs and improves performance.
• Compression and storage optimization: If storage efficiency is a priority, Parquet’s advanced compression techniques (e.g., dictionary encoding, run-length encoding) significantly reduce file sizes compared to row-based formats.
• Selective column reads and schema projection: When working with large datasets but only querying a few columns, Parquet enables selective column scanning, making queries significantly faster and more cost-efficient.

Which one should you choose?

How Avro and Parquet Work with Big Data Tools

Avro and Parquet are widely used in big data processing frameworks, cloud platforms, and distributed computing environments. Below is an overview of how Avro and Parquet integrate with popular big data tools.

Apache Spark

Apache Spark is a distributed computing framework widely used for processing large datasets. It supports both Avro and Parquet, but each format has distinct advantages depending on the use case.

Using Avro in Apache Spark

• Avro is ideal for data ingestion and ETL pipelines due to its row-based structure.
• It allows schema evolution, making it a preferred choice for Kafka streaming pipelines.
• Typically used as an intermediary format before converting data into analytics-friendly formats like Parquet.

Example:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("AvroExample").getOrCreate()

# Read Avro file
df = spark.read.format("avro").load("data.avro")
df.show()

# Write DataFrame to Avro
df.write.format("avro").save("output.avro")

Using Parquet in Apache Spark

• Parquet is optimized for analytics and batch processing in Spark.
• Since Spark queries often involve scanning specific columns, Parquet's columnar storage reduces I/O and speeds up queries.
• It is the default storage format in many data lakes and analytical environments.

Example:

# Read Parquet file
df = spark.read.parquet("data.parquet")
df.show()

# Write DataFrame to Parquet
df.write.mode("overwrite").parquet("output.parquet")

Apache Hive and Presto

Apache Hive and Presto are SQL-based query engines designed for big data. Both engines support Avro and Parquet but behave differently:

Avro in Hive and Presto

• Used in data ingestion workflows before transformation into an optimized format.
• Provides flexibility for schema evolution, but queries can be slower due to row-based scanning.

Example:

CREATE EXTERNAL TABLE avro_table
STORED AS AVRO
LOCATION 's3://my-data-bucket/avro/';

Parquet in Hive and Presto

• Preferred format for analytics due to column pruning and efficient compression.
• Queries are significantly faster because only necessary columns are read.

Example:

CREATE EXTERNAL TABLE parquet_table
STORED AS PARQUET
LOCATION 's3://my-data-bucket/parquet/';

Apache Kafka

Kafka is a real-time data streaming platform, and Avro is the de facto standard for message serialization.

Why Avro is preferred in Kafka

• Compact binary format makes messages smaller, reducing bandwidth and storage costs.
• Schema evolution support ensures compatibility between producers and consumers.
• Works seamlessly with Confluent Schema Registry, allowing schema version control.

Example:

from confluent_kafka import avro
from confluent_kafka.avro import AvroProducer

schema_registry_url = "http://localhost:8081"
avro_producer = AvroProducer({'bootstrap.servers': 'localhost:9092',
                              'schema.registry.url': schema_registry_url},
                             default_value_schema=avro.loads(value_schema_str))

avro_producer.produce(topic='avro_topic', value={"id": 1, "name": "Alice"})
avro_producer.flush()

Cloud data platforms (AWS, GCP, Azure)

Both Avro and Parquet are supported by major cloud platforms but are used differently.

AWS (Amazon S3, Glue, Redshift, Athena)

• Avro: Used for storing streaming data and schema evolution in AWS Glue ETL jobs.
• Parquet: Preferred in AWS Athena, Redshift Spectrum, and data lakes for faster analytics.

SELECT * FROM my_parquet_table WHERE year = 2023;

Google Cloud Platform (BigQuery, Dataflow, GCS)

• Avro: Used to ingest raw data in Google Dataflow.
• Parquet: Optimized for Google BigQuery, allowing column-based retrieval.

LOAD DATA INTO my_dataset.my_table
FROM 'gs://my-bucket/data.parquet'
FORMAT PARQUET;

Azure (Azure Data Lake, Synapse Analytics, Databricks)

• Avro: Used for data exchange and ingestion in Azure Data Factory.
• Parquet: Preferred in Azure Synapse Analytics and Azure Databricks for optimized storage and analytics.

ETL pipelines and data warehousing

Both formats play different roles in ETL pipelines:

Conclusion

Avro and Parquet are essential data storage formats in big data ecosystems, each serving different purposes. This post explored their technical architecture, use cases, integration with big data tools, and role in ETL pipelines.

Key takeaways include:

• Avro’s row-based format is efficient for streaming, schema evolution, and data serialization.
• Parquet’s columnar format optimizes analytical queries, compression, and storage efficiency.
• Big data tools such as Apache Spark, Hive, and Kafka integrate with these formats in different ways.
• ETL pipelines often use both, with Avro handling raw data ingestion and Parquet enabling efficient analytics.

To build a stronger foundation in data warehousing and big data processing, explore Data Warehousing Concepts. For insights into real-time vs. batch data processing, read Batch vs. Stream Processing. To get hands-on experience with big data tools, the Big Data Fundamentals with PySpark course is a great place to start.

Understanding these storage formats and their applications is key to designing scalable and efficient data architectures.