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

Introduction

If you’re working with big data, you’ve likely come across Apache Parquet and Apache Iceberg. These two technologies play critical roles, but they serve different purposes. Parquet is an optimized columnar storage format that speeds up analytical queries, while Iceberg is a table format that brings database-like capabilities to data lakes, including schema evolution, ACID transactions, and time travel.

Now, the key question is: Which one should you use, and when? Let’s break it down so you can make the best decision for your workloads.

Feature Breakdown: Parquet vs. Iceberg

Understanding Apache Parquet

Overview of Parquet

Parquet was introduced in 2013 by Twitter and Cloudera to solve a major problem: How do we store massive datasets efficiently while ensuring fast queries? Inspired by Google’s Dremel, Parquet uses a columnar storage format, which means queries can read only the necessary columns instead of scanning entire rows.

What Makes Parquet So Effective?

Apache Parquet has earned its reputation as the go-to columnar storage format for big data. Its efficient data organization, compression techniques, and integration with major processing frameworks make it indispensable for large-scale analytical workloads. Here’s why:

• Superior Compression Efficiency: Parquet’s columnar layout allows for high compression ratios using encoding techniques like dictionary encoding, run-length encoding (RLE), and bit-packing. By grouping similar data together, it significantly reduces storage costs and speeds up data transfers.

• Column Pruning for Faster Queries: Unlike row-based storage formats, Parquet lets query engines scan only the relevant columns instead of reading the entire dataset. This drastically reduces I/O operations and speeds up query execution, making it ideal for read-heavy workloads.

• Optimized for Aggregate Queries: Since Parquet stores column values together, aggregation functions (e.g., SUM, AVG, COUNT) operate faster as they don’t need to sift through irrelevant data.

• Predicate Pushdown for Efficient Filtering: Query engines like Apache Spark, Trino, and StarRocks can apply predicate pushdown to filter data at the storage layer, reducing the amount of data scanned and improving performance. This makes Parquet particularly efficient for scenarios involving large-scale analytics and business intelligence applications.

• Wide Ecosystem Support: Parquet is natively supported by almost every major big data tool, including Apache Spark, Hive, Flink, Trino, Presto, and StarRocks, ensuring compatibility across diverse environments. 

When Should You Use Parquet?

• If you need fast analytical queries with large datasets.

• If you’re storing data for dashboards, reporting, or machine learning feature stores.

• If you’re working with Spark, Trino, Hive, or StarRocks and need efficient storage.

Challenges with Parquet

While Parquet is an excellent format for analytical workloads, it has some drawbacks, particularly when dealing with dynamic or frequently updated datasets.

• The Small Files Problem: Parquet performs best with large, consolidated files. Handling many small Parquet files leads to metadata overhead, increasing resource consumption and query latency. This issue is particularly problematic in distributed storage systems like Amazon S3 or Hadoop HDFS.

• Inefficient for Frequent Updates and Deletes: Parquet is an append-only format, meaning modifications require rewriting entire files. This makes it unsuitable for transactional use cases that require frequent updates, inserts, or deletes.

• Schema Evolution Constraints: While Parquet does allow schema modifications (such as adding new columns), renaming or reordering columns requires file rewrites, which can impact performance and require careful version control.

• Compute-Intensive Metadata Handling: Since each Parquet file contains metadata, managing thousands of small files results in increased overhead and higher computational costs for query engines.

Despite these challenges, Parquet remains an excellent choice for query-optimized storage and long-term archival of analytical datasets.

Understanding Apache Iceberg

Why Was Iceberg Created?

By 2018, Netflix engineers were frustrated with how data lakes handled schema evolution and transactional data. Apache Hive tables lacked proper versioning and transactional capabilities. That’s why Iceberg was created: to make data lakes function more like databases.

What Sets Iceberg Apart?

Apache Iceberg was created to solve the limitations of traditional Hive-style data lakes, offering a modern table format that simplifies schema evolution, ensures strong consistency, and enhances performance for large-scale data operations.

• True Schema Evolution: Unlike Parquet, Iceberg allows renaming, reordering, and deleting columns without rewriting entire datasets. This is a critical feature for organizations dealing with constantly evolving data models.

• ACID Transactions for Reliable Data Management: Iceberg supports full ACID compliance, allowing atomic updates, deletes, and merges without needing to rewrite entire files. This makes it an ideal choice for data lakes that require transactional integrity.

• Time Travel & Snapshot Isolation: Iceberg enables users to query historical versions of their data, making it invaluable for auditing, debugging, and compliance. By storing snapshots, it allows analysts to track how data has changed over time.

• Intelligent Partitioning and Query Optimization: Iceberg automates partitioning and eliminates the need for manually managing partition directories. This significantly reduces query planning time and improves performance compared to traditional partitioning strategies in Hive or Parquet.

• Broad Compatibility with Big Data Ecosystems: Iceberg integrates seamlessly with processing frameworks such as Apache Spark, Flink, Trino, and StarRocks, making it a flexible option for multi-engine environments.

When Should You Use Iceberg?

• If you need frequent updates or deletes in a data lake.

• If schema evolution is a requirement.

• If you need time travel for auditing or rollback scenarios.

• If you’re managing petabyte-scale data lakes and need efficiency at scale.

Challenges with Iceberg

• Metadata Overhead and Storage Costs: Iceberg’s powerful metadata management comes at a cost—it requires additional storage and compute resources to maintain snapshots and versioning history.

• Query Performance Variability: While Iceberg optimizes queries by eliminating unnecessary file scans, performance can degrade if partitions are not well-balanced or if snapshot metadata becomes too large.

• Compatibility and Adoption Curve: Although Iceberg is gaining widespread adoption, some older tools and query engines may not yet have full support for its advanced features, requiring additional integration work.

• Write Performance Considerations: While Iceberg enables transactional updates, managing snapshots and transaction logs can introduce write amplification, requiring tuning and optimization for high-throughput write workloads.

Apache Parquet vs Apache Iceberg: A Practical Comparison

Performance and Efficiency

Let’s talk about speed and efficiency because that’s what really matters in big data processing. Apache Parquet and Apache Iceberg approach performance differently, each excelling in distinct ways.

Parquet is all about minimizing data scans. Since it stores data column by column, query engines only retrieve the specific columns they need, making it exceptionally fast for analytical workloads. This means if you’re running a report that only needs three out of 50 columns, Parquet won’t waste time scanning the unnecessary ones. The result? Faster queries, less I/O, and lower costs.

Iceberg, however, takes optimization to another level. It enhances query execution with file pruning and vectorized reads, allowing it to efficiently skip over irrelevant data files and only scan what’s needed. This makes it highly effective for large datasets with frequent writes, updates, and schema modifications.

Flexibility is crucial in today’s data world, where datasets evolve rapidly. Iceberg provides superior flexibility for managing dynamic datasets. It allows you to add, drop, rename, and modify columns without rewriting the entire dataset—a huge win for teams handling evolving schemas.

Iceberg also has time travel capabilities, letting you query previous versions of data for auditing, debugging, and regulatory compliance. This makes it an excellent choice when you need historical analysis and rollback functionality.

Parquet, on the other hand, is streamlined for efficient storage and retrieval, but it’s less flexible when it comes to schema evolution. While you can add columns, renaming or reordering columns can be complex and may require file rewrites. For teams needing stable, structured, and storage-efficient datasets, Parquet is a great option.

If you need long-term storage and query efficiency, Parquet is excellent. But if you’re working in an environment where schema changes are frequent and transactions are crucial, Iceberg is far more adaptable.

Scalability and Data Management

Managing large-scale datasets efficiently is a major challenge, and Iceberg is built for that. It was designed to handle petabyte-scale tables, making it the go-to solution for organizations dealing with rapidly growing data volumes. Iceberg’s partitioning strategies and metadata management optimize data retrieval, preventing slowdowns as your dataset scales.

Another key advantage? Multi-user environments. Iceberg supports concurrent writes and high-transaction workloads while maintaining data integrity through ACID transactions.

Parquet, while great for compression and fast querying, has some limitations when scaling. The small files problem—where thousands of tiny Parquet files accumulate—can create metadata overhead and I/O inefficiencies, making query execution slower. Parquet also struggles with frequent updates and schema evolution, which can become bottlenecks in fast-changing environments.

Best Use Cases for Apache Parquet and Apache Iceberg

When to Use Apache Parquet

Parquet is ideal when you need efficient storage and lightning-fast query performance. Its columnar format makes it perfect for workloads where you read data far more often than you write or update it. Here are some common use cases:

• Data Warehousing: Parquet is tailor-made for business intelligence and reporting. It compresses well and ensures fast retrieval.

• Big Data Analytics: Works seamlessly with Apache Spark, Hive, Trino, and StarRocks, making it an excellent choice for machine learning, data visualization, and exploratory analysis.

• Data Lakes: Ideal for storing massive amounts of raw, structured data for future processing.

• Log Analysis: If you need to analyze large volumes of log data, Parquet’s columnar structure accelerates queries by focusing only on relevant fields.

📌 Key Takeaway: Use Parquet when your workload is read-heavy, and you want to optimize query speed and storage efficiency.

When to Use Apache Iceberg

Iceberg is the powerhouse for dynamic, transactional, and evolving datasets. Its ability to handle updates, schema modifications, and time travel makes it ideal for more complex workflows. Consider Iceberg when:

• Schema Evolution is Frequent: If your dataset changes often, Iceberg allows schema modifications without rewriting files.

• ACID Transactions are Required: Iceberg ensures data integrity even in high-concurrency environments.

• Historical Data Analysis Matters: Need to query past versions of data for compliance or debugging? Iceberg’s time travel feature enables this seamlessly.

• Managing Petabyte-Scale Data Lakes: Iceberg’s optimized partitioning and metadata management keep performance high even as datasets grow.

📌 Key Takeaway: Use Iceberg when you need flexible schema management, transactional integrity, and time-travel capabilities.

FAQ

Can Parquet and Iceberg be used together?

Yes! Iceberg actually uses Parquet (or ORC) as its storage format while managing metadata and table structure. This means you get the best of both worlds—Parquet’s efficiency and Iceberg’s schema flexibility and transactions.

Which one is better for historical analysis?

Iceberg is the better choice because of its time travel feature, allowing you to query older versions of your data effortlessly.

Does Iceberg support real-time data processing?

While Iceberg isn’t built specifically for real-time workloads, StarRocks and other query engines can enable low-latency queries on Iceberg tables.

What’s the best choice for machine learning pipelines?

Parquet is often preferred for storing training datasets efficiently, but Iceberg can be useful when managing feature stores that require updates.

Is Iceberg harder to set up than Parquet?

Iceberg requires a metadata catalog and more configuration, but its advantages in schema evolution and transactions often justify the added complexity.

By understanding the strengths and trade-offs of Parquet and Iceberg, you can make the best choice for your data workflows!