Apache Parquet vs. Apache Iceberg: Understanding Their Roles in Data Processing

Introduction

If you’re working with large-scale data—especially in a lakehouse or distributed analytics architecture—you’ve likely encountered Apache Parquet and Apache Iceberg. Both are foundational technologies in the modern data stack, but they serve very different purposes:

• Parquet is a columnar storage format, designed to store data efficiently on disk and optimize read-heavy analytics workloads.

• Iceberg is a table format that sits one level above, organizing files (often in Parquet) into logical tables with support for features like ACID transactions, schema evolution, and time travel.

Think of Parquet as the structure of the data on disk, while Iceberg acts like a “table manager” that maintains consistency, versioning, and abstraction across potentially thousands of Parquet files.

So which should you use—and when? Let’s walk through the differences with clear examples and use cases.

Feature Breakdown: Parquet vs. Iceberg

Understanding Apache Parquet

Overview of Parquet

Apache Parquet, introduced in 2013 by Twitter and Cloudera, was designed to efficiently store columnar data at scale. Inspired by Google’s Dremel paper, Parquet enables efficient I/O, high compression, and fast scans—making it the default storage format for most big data systems.

It’s a file format, not a table format—so it doesn’t track table-level metadata or support features like partition pruning or schema validation on its own. Instead, it focuses on how data is stored within each file.

Why Parquet Works So Well for Analytics

Let’s break down what makes Parquet so effective, especially for OLAP-style queries:

Columnar Layout for Efficient Reads

Parquet stores each column in a separate block. So if your query only needs user_id and event_time, it reads just those blocks—skipping over device_info, geo, or referrer.

This reduces disk I/O and memory usage dramatically, especially when scanning billions of rows.

Advanced Compression Techniques

Because each column typically contains values of the same type and domain, Parquet can apply smart encoding:

• Dictionary encoding for low-cardinality strings

• Run-length encoding (RLE) for repeated values

• Bit-packing for integers This means smaller files, faster scans, and less data movement across the network.

Predicate Pushdown

Query engines like Apache Spark, Trino, Presto, and StarRocks support predicate pushdown, where filtering conditions (e.g., WHERE region = 'US') are applied during file read, not after. This avoids loading unnecessary rows altogether.

Optimized for Aggregates

Because all values of a column are laid out contiguously, operations like AVG(price) or COUNT(*) are fast. Engines can skip decoding irrelevant fields entirely.

Broad Ecosystem Support

Parquet is supported across the big data ecosystem: Spark, Hive, Flink, Trino, Dremio, StarRocks, and most data lake engines. It’s the de facto format for storing columnar data in S3, HDFS, and other object stores.

When Should You Use Parquet?

Use Parquet when:

• You’re building dashboards, reports, or ML pipelines that mostly read data.

• Your datasets are append-only or rarely change.

• You’re working with tools like Spark, Trino, or StarRocks that can optimize Parquet scans.

• You want to archive large datasets in a compact, query-friendly format.

Challenges with Parquet

Parquet wasn’t designed for transactional workloads or datasets that change frequently. Here’s where it struggles:

1. Small Files Problem

Each Parquet file contains row group metadata. When you have thousands of tiny files (a common issue with streaming ingestion), query engines spend too much time loading metadata and too little time processing data.

This degrades performance and leads to “metadata bottlenecks.” It’s especially problematic in object stores like S3, where file access latency is non-trivial.

2. Update/Delete Limitations

Parquet is append-only. If you want to update or delete a row, you typically have to rewrite the entire file. That’s expensive and error-prone. You lose atomicity, and there's no rollback unless you version files manually.

3. Rigid Schema Evolution

You can add new columns, but renaming, reordering, or dropping columns often requires rewriting the entire dataset. This makes schema management cumbersome in evolving pipelines.

4. Heavy Metadata Overhead at Scale

Each file carries its own schema and metadata, and query engines have to merge that metadata at runtime. As file counts grow, planning becomes slower—even before the first byte of data is read.

Understanding Apache Iceberg

Why Was Iceberg Created?

By 2018, Netflix was running into serious issues managing their ever-growing data lake on Hive. Specifically:

• Schema changes were painful and brittle.

• ACID transactions didn’t exist.

• Snapshots and rollback were unsupported.

• Table metadata was stored in the Hive metastore, which didn’t scale well for petabyte-sized datasets.

Apache Iceberg was created to address these problems head-on. Initially incubated inside Netflix, Iceberg aimed to bring database-like reliability, consistency, and manageability to data lakes built on open file formats like Parquet, Avro, or ORC.

Think of Iceberg as a modern table format that sits on top of your files and manages them like a relational database would—with support for versioning, transactions, and intelligent metadata management.

What Sets Iceberg Apart?

Let’s walk through the core innovations that make Iceberg a breakthrough for large-scale, evolving data systems:

True Schema Evolution (Without File Rewrites)

Unlike formats like Parquet, where renaming or dropping columns often requires rewriting entire files, Iceberg stores schema information in its own metadata layer, separate from the files.

This allows:

• Renaming columns without data rewrite

• Reordering columns

• Dropping obsolete fields

• Backfilling new fields with default values

Schema evolution is fully versioned and safe. You can even revert to older schema versions if needed.

Full ACID Transactions at Scale

Iceberg supports serializable isolation through snapshot-based transactional writes. Whether you’re performing INSERTs, UPDATEs, DELETEs, or MERGEs, the changes are atomic and consistent.

Iceberg transactions rely on:

• Manifest lists (pointing to groups of data files)

• Snapshot files (versioned metadata)

• Lock-free concurrency control

No more overwriting entire tables or managing locking logic manually. This makes Iceberg ideal for concurrent, multi-user environments—like streaming pipelines or data ingestion platforms.

Time Travel and Snapshot Isolation

Every write in Iceberg creates a new snapshot, recorded in the metadata. This enables:

• Point-in-time queries (e.g., “what did this table look like yesterday?”)

• Rollback to previous versions

• Audit trails and data debugging

• Safe experimentation on consistent versions of data

Snapshots are metadata-based and lightweight, making them extremely efficient even at scale.

Partitioning Without Pain

Traditional Hive-style partitioning creates one directory per partition, which can lead to:

• Millions of directories

• Slow metadata loads

• File path coupling with business logic

Iceberg solves this with hidden partitioning. You define logical partitions (e.g., date, region), but Iceberg stores partitioning information in its metadata layer rather than relying solely on directory structure, allowing more flexible transforms and automatic pruning without brittle path-based queries. This means:

• Faster planning time

• Automatic pruning of irrelevant partitions

• Flexibility to change partition strategies later

It also supports identity, bucket, truncate, and custom transforms, giving more control over partitioning logic.

When Should You Use Iceberg?

Let’s break this down like a practitioner would:

• You need updates and deletes (e.g., GDPR redactions, late-arriving events)

• Your schemas evolve frequently (e.g., product catalogs, user profiles)

• You need data versioning (e.g., point-in-time metrics, rollback, reproducibility)

• You ingest massive amounts of data from multiple concurrent writers

• You want cloud-native performance and manageability

Challenges with Iceberg (What to Watch Out For)

Despite its advantages, Iceberg isn’t a silver bullet. It introduces new layers that come with trade-offs:

Metadata Growth and Storage Cost

Each snapshot stores manifest lists and metadata about files. As data changes over time:

• Snapshots accumulate

• Metadata grows

• Old manifests can become costly

You’ll need to periodically expire old snapshots and compact metadata files using tools like expire_snapshots and rewrite_manifests.

Write Amplification and Performance Tuning

Transactional writes add metadata overhead. If you’re doing high-frequency, high-throughput ingestion (e.g., real-time Kafka streams), you may need:

• Partition tuning

• Compaction jobs

• Write batching and deduplication

Flink and Spark both offer tools to manage this, but tuning takes effort.

Ecosystem Support (Still Maturing in Some Areas)

While support is rapidly growing, not all tools have full support for:

• Time travel

• Row-level deletes

• Snapshot-aware caching

And some integrations may need custom catalog configuration (e.g., with Hive Metastore vs. AWS Glue vs. REST catalogs).

Apache Parquet vs Apache Iceberg: A Practical Comparison

Scalability and Data Management

Best Use Cases for Apache Parquet and Apache Iceberg

When to Use Apache Parquet

Apache Parquet is ideal when your workloads are read-heavy, your schema is relatively stable, and your primary goal is efficient storage and fast scans. It’s a storage format, not a table format, so it’s lightweight and works across virtually every big data engine.

Common Use Cases

• Data Warehousing:
  Parquet is widely used in OLAP-style workloads, including business intelligence dashboards and ad hoc analytics. Its columnar layout allows fast aggregate queries and low storage costs.

• Big Data Analytics & Machine Learning Pipelines:
  Engines like Apache Spark, Hive, Trino, and StarRocks natively support Parquet. It’s excellent for large-scale feature extraction, model training, and exploratory analysis because of its fast columnar access and compression.

• Data Lakes:
  Parquet is the default format for many raw data lakes on S3, GCS, or HDFS. It supports schema definition at the file level and integrates well with ETL and ELT pipelines.

• Log and Event Analysis:
  When log data is ingested in batch (e.g., hourly or daily), Parquet’s ability to compress and filter only relevant fields makes it well-suited for analyzing high-volume telemetry.

Key Takeaway:
Use Parquet when you need efficient columnar storage with high compression and fast query performance—especially when data is append-only and queried often but updated rarely.

When to Use Apache Iceberg

Apache Iceberg is designed to manage dynamic datasets at scale. It’s a table format, not a storage format, so it adds table-level semantics—like ACID transactions, schema evolution, versioning, and partition pruning—on top of storage formats like Parquet or ORC.

Common Use Cases

• Schema Evolution and Changing Data Models:
  Iceberg allows fields to be added, renamed, reordered, or dropped—without rewriting data. This is essential in use cases where the schema changes over time, such as user behavior logs or product catalogs.

• Transactional Pipelines and ACID Requirements:
  If your data pipeline includes late-arriving records, updates, deletes (e.g., GDPR compliance), or deduplication, Iceberg’s transactional support ensures consistent, atomic changes.

• Historical Data Analysis (Time Travel):
  Iceberg snapshots enable point-in-time queries, data rollback, and audit trails. This is valuable for reproducibility, debugging, or compliance in financial and regulated environments.

• Managing Petabyte-Scale Data Lakes:
  Iceberg handles metadata and partitioning efficiently, even with trillions of rows. Its hidden partitioning model decouples partition logic from directory structure, improving performance and flexibility in massive datasets.

Key Takeaway:
Use Iceberg when your data is dynamic, schemas evolve, or transactional correctness and historical versioning are important.

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