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Apache Iceberg vs Parquet: File vs. Table Formats for Modern Data Lakes

Compare Apache Iceberg and Parquet to understand their roles in data lakes: Iceberg as a table format for large-scale data management, and Parquet as a file format for efficient storage. Explore use cases, performance, and integration with data tools in this detailed guide

By

Jatin Solanki

Updated on

October 7, 2024

Today, we're diving into a hot topic that's been buzzing around the data engineering community: Apache Iceberg. We'll explore why it's considered the future of file formats and how it compares to the widely used Apache Parquet file format. So, grab your favorite beverage and get comfy, because we're about to embark on an exciting journey into the world of big data file formats.

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Apache Iceberg and Parquet -Section 1 (Background)

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Apache Iceberg:

Let me tell you about an absolute game-changer in the world of big data file formats – Apache Iceberg. Born at Netflix, this cool-as-ice open table format is creating quite a stir in the data engineering community. But why all the buzz, you ask? Well, for starters, Iceberg is specifically designed to tackle large-scale data lakes. Think better performance, atomicity, and reliability – everything you could ever want in a table format!

Now, let's take a quick trip down memory lane. The brilliant minds at Netflix conceived Iceberg to address the limitations of existing table formats. Soon after, the project caught the attention of the Apache Software Foundation, which saw immense potential in Iceberg's innovative approach. So, in no time, Iceberg became an Apache project, and its development skyrocketed with contributions from tech giants like Apple, Adobe, and Alibaba. Today, Iceberg is steadily gaining traction as the future of file formats, and it's all thanks to its powerful features and the dedicated community behind it. So, buckle up, folks, because Apache Iceberg is here to revolutionize the way we handle big data!

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Apache Parquet:

Let me introduce you to a true heavyweight in the big data arena – Apache Parquet. This columnar storage file format has been the go-to choice for countless data engineers since its inception, and for good reason! Parquet was specifically designed to work hand-in-hand with big data processing frameworks like Apache Hadoop, Apache Spark, and Apache Impala. Its claim to fame? Efficient compression and encoding techniques that optimize analytical queries, dramatically reducing I/O and improving query performance.

Now, let's rewind to the origins of Parquet. This brainchild of Twitter and Cloudera was born out of a need for a more efficient storage format that could handle the massive scale of modern data processing workloads. Parquet's unique columnar storage approach quickly gained traction, as it proved to be the perfect fit for data warehousing and analytics use cases. As an Apache project, Parquet has enjoyed the support of an ever-growing community of contributors, ensuring its place as a key player in the big data ecosystem. So, if you're looking for a tried-and-true file format that has stood the test of time, Apache Parquet is your answer!

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Section 2: Key Features of Apache Iceberg

Now that we know the basics, let's look at some key features that make Apache Iceberg stand out as the future of file formats.

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2.1. Schema Evolution

One of the most powerful features of Iceberg is its ability to handle schema evolution. It enables you to evolve your data schema over time while maintaining compatibility with older data. This makes it easier to manage and analyze your data as it grows and changes.

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2.2. Atomic Transactions

Iceberg provides ACID transactions, ensuring that your data remains consistent even during concurrent writes and reads. This is achieved using a combination of snapshot isolation and optimistic concurrency control. The result is a highly concurrent system that allows multiple writers to work simultaneously without conflicts or inconsistencies.

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Section 3: Comparing Apache Iceberg and Parquet

Now, let's dive into a head-to-head comparison between Apache Iceberg and Apache Parquet. We'll examine how these file formats handle different aspects of data engineering, such as schema evolution, performance, and data storage.

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3.1. Schema Evolution

As we mentioned earlier, Iceberg's schema evolution capabilities are one of its standout features. In contrast, Parquet also supports schema evolution to a certain extent, but it's not as robust as Iceberg. For instance, Parquet allows you to add new columns or remove existing ones, but it doesn't support evolving nested structures or updating partitioning specifications.

Winner: Iceberg


3.2 Performance

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a Predicate Pushdown

Both Iceberg and Parquet support predicate pushdown, which helps optimize query performance by filtering data at the storage level rather than the processing level. This means that only the relevant data is read from the storage, reducing I/O and improving query performance. While Parquet's predicate pushdown works well with columnar storage, Iceberg takes it a step further by also implementing partition pruning based on partition spec evolution, further reducing I/O and speeding up queries.

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b. Vectorized Reads

Parquet supports vectorized reads, which allow for faster data processing by processing data in batches rather than row by row. Vectorized reads enable efficient use of modern CPU architectures, resulting in improved performance. Iceberg, on the other hand, currently does not support vectorized reads natively. However, this is an area of active development, and we can expect to see improvements in the future.

Winner: Parquet (for now)

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3.3 Storage

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a. Columnar vs. Row-based Storage

Parquet is a columnar storage file format, which means it stores data by columns rather than rows. This storage format is particularly useful for analytical queries that access a small subset of columns in a table. Columnar storage enables efficient compression and encoding, which reduces storage costs and improves query performance.

Iceberg, on the other hand, is a table format rather than a file format. It can work with various file formats, including Parquet, ORC, and Avro. This flexibility allows you to choose the best file format based on your specific use case and storage requirements.

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b Partitioning

Both Iceberg and Parquet support partitioning, which is crucial for optimizing query performance and reducing I/O. However, Iceberg takes partitioning to the next level with its dynamic partitioning and partition spec evolution. This feature allows you to change partitioning schemes without rewriting the entire table, providing better flexibility and maintainability.

‍Winner: Iceberg

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3.4 Reliability and Atomicity

As mentioned earlier, Iceberg supports ACID transactions, ensuring data consistency even during concurrent writes and reads. Parquet, on the other hand, does not provide built-in transaction support. While you can achieve transactional behavior in Parquet using tools like Apache Hive or Delta Lake, it's not a native feature of the file format.

Winner: Iceberg

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3.5 Community and Ecosystem

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a. Community

Both Iceberg and Parquet have active communities and are supported by major tech companies like Netflix, Apple, and Adobe (Iceberg) and Twitter, Cloudera, and Databricks (Parquet). However, since Iceberg is relatively newer, its community is smaller compared to Parquet's.

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b. Ecosystem

Apache Parquet is well-established in the big data ecosystem, with support for various processing frameworks like Apache Hadoop, Apache Spark, and Apache Impala. Iceberg is quickly gaining traction, with support for popular frameworks like Apache Spark, Apache Flink, and Trino (formerly PrestoSQL). As Iceberg continues to mature, we can expect its ecosystem support to grow further.

Winner: Parquet (for now)

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Conclusion:

In conclusion, Apache Iceberg shows great promise as the future of file formats, with its robust schema evolution, ACID transactions, and flexible data storage capabilities. While Parquet still holds an edge in certain areas, such as performance and community support, Iceberg is quickly catching up.

As a data engineer, it's essential to stay up-to-date with the latest developments in the field. Keep an eye on Apache Iceberg as it continues to evolve, and don't be afraid to explore its capabilities in your projects. After all, staying ahead of the curve is what makes us great data engineers!

Now, it's time to wrap up our journey into the world of file formats. I hope you enjoyed this deep dive into Apache Iceberg and Parquet. As always, feel free to share your thoughts, experiences, and questions in the comments below. Let's keep the conversation going and learn from each other, because that's what being a part of the data engineering community is all about!

If you want to learn more about Apache Iceberg or get involved in the project, check out the official Iceberg documentation and GitHub repository. And if you're new to Parquet or want to explore it further, the Parquet documentation and GitHub repository are excellent resources.

So, until our next big data adventure, happy data engineering! Stay curious, keep exploring, and always be open to learning new things. After all, that's what makes this field so fascinating and rewarding.

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Summary comparison
External Reference:
  1. Apache Iceberg Official Documentation: https://iceberg.apache.org/
  2. Apache Iceberg GitHub Repository: https://github.com/apache/iceberg
  3. Apache Parquet GitHub Repository: https://github.com/apache/parquet-format
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What is the purpose of a data dictionary?
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A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
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Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.
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