One of the most important decisions in your Apache Spark pipeline is how you store your data. The data format you choose can dramatically affect performance, storage costs, and query speed. Let’s explore the most common file formats supported by Apache Spark, and in which cases they can fit the most. how you store your data Different file formats There are different types of data formats commonly used in data processing, especially with tools like Apache Spark, broken into categories based on their structure and use case: Apache Spark categories Row-Based File Formats The data is stored row by row, and it is easy to write and process linearly, but less efficient for analytical queries where only a few columns are needed. row by row less efficient CSV (Comma-Separated Values) CSV CSV is a plain text, row-based format where columns are separated by commas. It is easy to work with but not efficient for big data.


Pros: CSV is human-readable, simple to write and read, and is used globally.


Cons: CSV lacks data types, requiring Spark to infer column types from a sample of the CSV file, which adds extra work and may not be accurate. Additionally, CSV has poor compression and struggles with encoding complex data.


Use cases: Legacy systems, small data exports, debugging, and working with spreadsheets.


Reading CSV file in Apache Spark example:
# Pyspark example
df = spark.read.options(delimiter=",", header=True).csv(path)

# Scala example
val df = spark.read.option("delimiter", ",").option("header", "true").csv(path)

JSON (JavaScript Object Notation)


JSON is a lightweight, text-based format for exchanging data. It uses human-readable text to store and send information, but it can be slow and doesn't enforce a schema.


Pros: JSON is readable and widely supported by many systems, and can store semi-structural data.


Cons: JSON is slow to parse, and each row must be a valid JSON for Spark to parse. Additionally, from a storage perspective, JSON produces large files because many boilerplate tokens and key names are repeated in each row, and it lacks schema enforcement.


Use case: Mainly use JSON for debugging or exploring data. It can also be used to integrate with external systems that provide JSON, which you can't control, but don’t depend on it as the final storage data format.


Reading JSON file in Apache Spark example:
# Pyspark example
df = spark.read.json(path)

# Scala example
val df = spark.read.json(path)

Apache Avro


Apache Avro is a row-based format often used with Kafka pipelines and data exchange scenarios. It supports descriptive extendable schema and is compact for serialization.


Pros: Avro is efficient in storage, since it is in binary format, and has a great schema evolution feature.


Cons: While Avro is efficient in storage, it is not optimized for columnar queries, since you need to scan the whole file to read specific columns.


Use case: Avro is mainly used with real-time streaming systems like Kafka because it is easy to serialize and transmit. It also allows for easy schema evolution through a schema registry.


The spark-avro module is external and not included in the spark-submit or spark-shell by default, but spark-avro_VERSION and its dependencies can be directly added to spark-submit using --packages
./bin/spark-submit --packages org.apache.spark:spark-avro_VERSION

./bin/spark-shell --packages org.apache.spark:spark-avro_VERSION



Reading Avro file in Apache Spark example:
# Pyspark example
df = spark.read.format("avro").load(path)

# Scala example
val df = spark.read.format("avro").load(path)

Columnar File Formats
The data is stored column by column, making them ideal for analytics and interactive dashboards where only a subset of columns is queried. CSV is a plain text, row-based format where columns are separated by commas. It is easy to work with but not efficient for big data. CSV is a plain text, row-based format where columns are separated by commas. It is easy to work with but not efficient for big data. CSV Pros: CSV is human-readable, simple to write and read, and is used globally. Pros: CSV is human-readable, simple to write and read, and is used globally. Pros Cons: CSV lacks data types, requiring Spark to infer column types from a sample of the CSV file, which adds extra work and may not be accurate. Additionally, CSV has poor compression and struggles with encoding complex data. Cons: CSV lacks data types, requiring Spark to infer column types from a sample of the CSV file, which adds extra work and may not be accurate. Additionally, CSV has poor compression and struggles with encoding complex data. Cons Use cases: Legacy systems, small data exports, debugging, and working with spreadsheets. Use cases: Legacy systems, small data exports, debugging, and working with spreadsheets. Use cases Reading CSV file in Apache Spark example:
# Pyspark example
df = spark.read.options(delimiter=",", header=True).csv(path)

# Scala example
val df = spark.read.option("delimiter", ",").option("header", "true").csv(path)

JSON (JavaScript Object Notation) Reading CSV file in Apache Spark example: Reading CSV file CSV file in Apache Spark example: # Pyspark example
df = spark.read.options(delimiter=",", header=True).csv(path)

# Scala example
val df = spark.read.option("delimiter", ",").option("header", "true").csv(path) # Pyspark example
df = spark.read.options(delimiter=",", header=True).csv(path)

# Scala example
val df = spark.read.option("delimiter", ",").option("header", "true").csv(path) JSON (JavaScript Object Notation) JSON is a lightweight, text-based format for exchanging data. It uses human-readable text to store and send information, but it can be slow and doesn't enforce a schema. JSON is a lightweight, text-based format for exchanging data. It uses human-readable text to store and send information, but it can be slow and doesn't enforce a schema. JSON human-readable Pros: JSON is readable and widely supported by many systems, and can store semi-structural data. Pros: JSON is readable and widely supported by many systems, and can store semi-structural data. Pros JSON semi-structural Cons: JSON is slow to parse, and each row must be a valid JSON for Spark to parse. Additionally, from a storage perspective, JSON produces large files because many boilerplate tokens and key names are repeated in each row, and it lacks schema enforcement. Cons: JSON is slow to parse, and each row must be a valid JSON for Spark to parse. Additionally, from a storage perspective, JSON produces large files because many boilerplate tokens and key names are repeated in each row, and it lacks schema enforcement. Cons JSON Use case: Mainly use JSON for debugging or exploring data. It can also be used to integrate with external systems that provide JSON, which you can't control, but don’t depend on it as the final storage data format. Use case: Mainly use JSON for debugging or exploring data. It can also be used to integrate with external systems that provide JSON, which you can't control, but don’t depend on it as the final storage data format. Use case JSON JSON Reading JSON file in Apache Spark example:
# Pyspark example
df = spark.read.json(path)

# Scala example
val df = spark.read.json(path)

Apache Avro Reading JSON file in Apache Spark example: Reading JSON file JSON file in Apache Spark example: # Pyspark example
df = spark.read.json(path)

# Scala example
val df = spark.read.json(path) # Pyspark example
df = spark.read.json(path)

# Scala example
val df = spark.read.json(path) Apache Avro Apache Avro is a row-based format often used with Kafka pipelines and data exchange scenarios. It supports descriptive extendable schema and is compact for serialization. Apache Avro is a row-based format often used with Kafka pipelines and data exchange scenarios. It supports descriptive extendable schema and is compact for serialization. Apache Avro Kafka data exchange descriptive extendable schema Pros: Avro is efficient in storage, since it is in binary format, and has a great schema evolution feature. Pros: Avro is efficient in storage, since it is in binary format, and has a great schema evolution feature. Pros Avro Cons: While Avro is efficient in storage, it is not optimized for columnar queries, since you need to scan the whole file to read specific columns. Cons: While Avro is efficient in storage, it is not optimized for columnar queries, since you need to scan the whole file to read specific columns. Cons Avro Use case: Avro is mainly used with real-time streaming systems like Kafka because it is easy to serialize and transmit. It also allows for easy schema evolution through a schema registry. Use case: Avro is mainly used with real-time streaming systems like Kafka because it is easy to serialize and transmit. It also allows for easy schema evolution through a schema registry. Use case Avro Kafka The spark-avro module is external and not included in the spark-submit or spark-shell by default, but spark-avro_VERSION and its dependencies can be directly added to spark-submit using --packages
./bin/spark-submit --packages org.apache.spark:spark-avro_VERSION

./bin/spark-shell --packages org.apache.spark:spark-avro_VERSION The spark-avro module is external and not included in the spark-submit or spark-shell by default, but spark-avro_VERSION and its dependencies can be directly added to spark-submit using --packages spark-avro_VERSION spark-submit --packages ./bin/spark-submit --packages org.apache.spark:spark-avro_VERSION

./bin/spark-shell --packages org.apache.spark:spark-avro_VERSION ./bin/spark-submit --packages org.apache.spark:spark-avro_VERSION

./bin/spark-shell --packages org.apache.spark:spark-avro_VERSION Reading Avro file in Apache Spark example:
# Pyspark example
df = spark.read.format("avro").load(path)

# Scala example
val df = spark.read.format("avro").load(path)

Columnar File Formats
The data is stored column by column, making them ideal for analytics and interactive dashboards where only a subset of columns is queried. Reading Avro file in Apache Spark example: Reading Avro file Avro file in Apache Spark example: # Pyspark example
df = spark.read.format("avro").load(path)

# Scala example
val df = spark.read.format("avro").load(path) # Pyspark example
df = spark.read.format("avro").load(path)

# Scala example
val df = spark.read.format("avro").load(path) Columnar File Formats The data is stored column by column, making them ideal for analytics and interactive dashboards where only a subset of columns is queried. column by column analytics and interactive dashboards Parquet (The Gold Standard for Analytics) Parquet is a columnar binary format optimized for analytical queries. It’s the most popular format for Spark workloads.


Pros: Parquet is built for efficient reads with compression and predicate push-down, which makes it fast, compact, ideal for Spark, Hive, Presto.


Cons: Parquet is slightly slower to write than row-based formats.


Use case: Parquet is the first choice for Spark and analytical queries, data lakes, cloud storage.


Reading Parquet file in Apache Spark example:
# Pyspark example
df = spark.read.parquet(path)

# Scala example
val df = spark.read.parquet(path)

Apache ORC (Optimized Row Columnar)


ORC is another columnar format, optimized for the Hadoop ecosystem, especially Hive.


Pros: ORC has a high compression ratio, and is optimized for scan-heavy queries, and supports predicates push-down similar to Parquet.


Cons: ORC has less support outside Hadoop tools, which makes it harder to integrate with other tools.


Use case: Hive-based data warehouses, HDFS-based systems.


Reading ORC file in Apache Spark example:
# Pyspark example
df = spark.read.format("orc").load(path)

# Scala example
val df = spark.read.format("orc").load(path)

Summary table Parquet is a columnar binary format optimized for analytical queries. It’s the most popular format for Spark workloads. Parquet is a columnar binary format optimized for analytical queries. It’s the most popular format for Spark workloads. Parquet Pros: Parquet is built for efficient reads with compression and predicate push-down, which makes it fast, compact, ideal for Spark, Hive, Presto. Pros: Parquet is built for efficient reads with compression and predicate push-down, which makes it fast, compact, ideal for Spark, Hive, Presto. Pros: Cons: Parquet is slightly slower to write than row-based formats. Cons: Parquet is slightly slower to write than row-based formats. Cons: Use case: Parquet is the first choice for Spark and analytical queries, data lakes, cloud storage. Use case: Parquet is the first choice for Spark and analytical queries, data lakes, cloud storage. Use case: Reading Parquet file in Apache Spark example:
# Pyspark example
df = spark.read.parquet(path)

# Scala example
val df = spark.read.parquet(path)

Apache ORC (Optimized Row Columnar) Reading Parquet file in Apache Spark example: Reading Parquet file in Apache Spark example: # Pyspark example
df = spark.read.parquet(path)

# Scala example
val df = spark.read.parquet(path) # Pyspark example
df = spark.read.parquet(path)

# Scala example
val df = spark.read.parquet(path) Apache ORC (Optimized Row Columnar) ORC is another columnar format, optimized for the Hadoop ecosystem, especially Hive. ORC is another columnar format, optimized for the Hadoop ecosystem, especially Hive. ORC Pros: ORC has a high compression ratio, and is optimized for scan-heavy queries, and supports predicates push-down similar to Parquet. Pros: ORC has a high compression ratio, and is optimized for scan-heavy queries, and supports predicates push-down similar to Parquet. Pros: ORC Cons: ORC has less support outside Hadoop tools, which makes it harder to integrate with other tools. Cons: ORC has less support outside Hadoop tools, which makes it harder to integrate with other tools. Cons: ORC Use case: Hive-based data warehouses, HDFS-based systems. Use case: Hive-based data warehouses, HDFS-based systems. Use case: Reading ORC file in Apache Spark example:
# Pyspark example
df = spark.read.format("orc").load(path)

# Scala example
val df = spark.read.format("orc").load(path)

Summary table Reading ORC file in Apache Spark example: Reading ORC file in Apache Spark example: # Pyspark example
df = spark.read.format("orc").load(path)

# Scala example
val df = spark.read.format("orc").load(path) # Pyspark example
df = spark.read.format("orc").load(path)

# Scala example
val df = spark.read.format("orc").load(path) Summary table Format

Type

Compression

Predicate Push-down

Best Use Case



Parquet

Columnar

Excellent

✅ Yes

Big data, analytics, selective queries



ORC

Columnar

Excellent

✅ Yes

Hive-based data lakes



Avro

Row-based

Good

❌ No (limited)

Kafka pipelines, schema evolution



JSON

Row-based

None

❌ No

Debugging, integration



CSV

Row-based

None

❌ No

Legacy formats, ingestion, exploration Format

Type

Compression

Predicate Push-down

Best Use Case



Parquet

Columnar

Excellent

✅ Yes

Big data, analytics, selective queries



ORC

Columnar

Excellent

✅ Yes

Hive-based data lakes



Avro

Row-based

Good

❌ No (limited)

Kafka pipelines, schema evolution



JSON

Row-based

None

❌ No

Debugging, integration



CSV

Row-based

None

❌ No

Legacy formats, ingestion, exploration Format

Type

Compression

Predicate Push-down

Best Use Case Format Format Format Type Type Type Compression Compression Compression Predicate Push-down Predicate Push-down Predicate Push-down Best Use Case Best Use Case Best Use Case Parquet

Columnar

Excellent

✅ Yes

Big data, analytics, selective queries Parquet Parquet Parquet Columnar Columnar Excellent Excellent ✅ Yes ✅ Yes Big data, analytics, selective queries Big data, analytics, selective queries ORC

Columnar

Excellent

✅ Yes

Hive-based data lakes ORC ORC ORC Columnar Columnar Excellent Excellent ✅ Yes ✅ Yes Hive-based data lakes Hive-based data lakes Avro

Row-based

Good

❌ No (limited)

Kafka pipelines, schema evolution Avro Avro Avro Row-based Row-based Good Good ❌ No (limited) ❌ No (limited) Kafka pipelines, schema evolution Kafka pipelines, schema evolution JSON

Row-based

None

❌ No

Debugging, integration JSON JSON JSON Row-based Row-based None None ❌ No ❌ No Debugging, integration Debugging, integration CSV

Row-based

None

❌ No

Legacy formats, ingestion, exploration CSV CSV CSV Row-based Row-based None None ❌ No ❌ No Legacy formats, ingestion, exploration Legacy formats, ingestion, exploration Conclusion Choosing the right file format in Spark is not just a technical decision, but it's a strategic one. Parquet and ORC are solid choices for most modern workloads, but your use case, tools, and ecosystem should guide your choice. not just a technical decision strategic one

This story contains new, firsthand information uncovered by the writer.

Picking the Wrong Spark File Format Could Hurt Your Data Strategy

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