Apache Iceberg with Parquet for Scalable Data Storage
- Anvita Shrivastava
- Sep 15, 2025
- 3 min read
Modern data platforms are managing increasing amounts of structured and semi-structured data. Conventional table formats and data warehouses that are monolithic frequently do not meet the requirements for scalable analytics, ACID transactions, schema evolution, and time-travel queries. Organizations are adopting Apache Iceberg, a high-performance open table format designed for large-scale analytics, to address these challenges. It is often combined with Apache Parquet, the most widely adopted columnar storage format. Iceberg and Parquet together create a strong basis for scalable, cost-effective, cloud-native data lakes.
What is Apache Iceberg?
Apache Iceberg is an open table format designed for large-scale analytic datasets. In contrast to legacy table formats like Hive or conventional partitioning schemes, Iceberg offers:
Schema Evolution: Modify columns by adding, dropping, or renaming them without the need to rewrite complete datasets.
Hidden Partitioning: Users are shielded from the complexities of partitioning, which helps avoid query mistakes and maintains high performance levels.
ACID Transactions: Guarantees data consistency through atomic commits and snapshot isolation.
Time Travel & Rollback: Access historical snapshots of a dataset or revert to a stable state.
Compatibility: Functions seamlessly with well-known engines, including Apache Spark, Trino, Flink, Presto, and Hive.
Iceberg essentially separates table metadata from the storage layer beneath it, allowing for high-performance queries on petabyte-scale data lakes.
What is Apache Parquet?
Apache Parquet is designed for analytical workloads and uses a columnar storage format. It provides:
Efficient Compression and Encoding: Cuts down storage requirements by as much as 80% in comparison to row-based formats.
Columnar Reads: Accesses only the required columns, enhancing query performance.
Predicate Pushdown: Implements filters at the storage level to cut down on I/O.
Extensive Ecosystem Compatibility: Works with Spark, Hive, Drill, Presto, Trino, and the majority of contemporary data frameworks.
In the realm of big data storage, Parquet has become the de facto standard, particularly in data lakehouses where performance and cost efficiency are paramount.
Why Combine Apache Iceberg with Parquet?
Parquet offers efficient physical storage, whereas Iceberg introduces a logical table layer that includes governance and performance enhancements.
Major Advantages of Using Iceberg with Parquet:
Metadata Management that Scales
Instead of depending on file system listings, Iceberg arranges data into manifests and metadata files.
This avoids bottlenecks when querying billions of Parquet files in cloud object stores such as Amazon S3, Google Cloud Storage, or Azure Data Lake.
Query Performance Optimization
Iceberg tracks statistics at the file level, including minimum and maximum values and row counts.
Metadata pruning allows query engines to bypass whole Parquet files.
Efficient Storage for Transactional Writes
New data is recorded as Parquet files, with Iceberg snapshots providing atomic visibility.
This resolves the small-files issue and guarantees consistent query results.
Future-Proof Schema Development
Data is stored efficiently in Parquet files, and Iceberg provides a flexible schema registry.
Historical data can be preserved while renaming or reordering columns.
Chrono-Transit and Audit Activities
Iceberg snapshots point to particular versions of Parquet files.
Users can query data “as of” a particular timestamp, which facilitates reproducible analytics.
Example: Writing Iceberg Tables in Parquet with Spark
This Spark example demonstrates how to create and query Iceberg tables that are stored in Parquet:
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("IcebergParquetExample") \
.config("spark.sql.catalog.my_catalog", "org.apache.iceberg.spark.SparkCatalog") \
.config("spark.sql.catalog.my_catalog.type", "hadoop") \
.config("spark.sql.catalog.my_catalog.warehouse", "s3://my-data-lake/warehouse") \
.getOrCreate()
# Write DataFrame to Iceberg table in Parquet
df = spark.read.json("s3://raw-data/events/")
df.writeTo("my_catalog.analytics.events") \
.tableProperty("write.format.default", "parquet") \
.createOrReplace()
# Query historical snapshot
historical = spark.sql("""
SELECT * FROM my_catalog.analytics.events
FOR SYSTEM_TIME AS OF TIMESTAMP '2025-01-01 00:00:00'
""")
historical.show()
This instance illustrates:
The storage format is Parquet.
Iceberg serves as the metadata/transactional layer.
Time-travel inquiry functions.
Deployment Considerations
For production implementations of Apache Iceberg with Parquet:
Storage Backend: Utilize cloud object storage solutions (such as Amazon S3, ADLS, GCS) or distributed file systems like HDFS.
Compaction Strategies: To enhance performance, periodically consolidate small Parquet files into larger ones.
Partitionierung: Nutzen Sie die versteckte Partitionierung von Iceberg, um eine Sichtbarkeit der Partitionierungsspalte zu vermeiden.
Engines: Select query engines (Spark, Trino, Flink) according to the demands of the workload.
Metadata Cleanup: Leverage Iceberg’s snapshot expiration to avert metadata bloat.
Apache Iceberg combined with Parquet provides the best of both worlds:
Parquet is a highly efficient columnar storage solution.
Iceberg for dependable, scalable, and regulated table management.
With this combination, organizations can create data lakes and lakehouses that are modern, cloud-native, and scalable to petabytes, all while guaranteeing consistency, performance, and flexibility. For businesses aiming to update their data platforms, Iceberg and Parquet offer a next-gen basis for scalable data storage.
For more information or any questions regarding Apache Iceberg, please don't hesitate to contact us at
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