[01] – Introduction to Apache Iceberg

In this tutorial, we’ll explore Apache Iceberg, a powerful table format for managing large-scale datasets in data lakes. This is your introduction to Apache Iceberg, where you’ll learn:

• Why Apache Iceberg was designed.
• What Apache Iceberg is.
• How Iceberg addresses common data lake problems, exploring its capabilities, such as schema evolution, hidden partitioning, and time travel.

By the end of this tutorial, you’ll have a clear understanding of what Iceberg is, what it isn’t, and when to use it to optimize your data workflows.

What’s Table Format ? #

A table format is a structured approach to organizing a dataset’s files, allowing them to be represented as a single, cohesive table. From a user’s perspective, it serves as the answer to the question: “What data does this table contain?”

Why Apache Iceberg was designed? #

For a long time, Apache Hive became the de facto table format in data lakes world. It provided several advantages, such as:

• Broad compatibility – Works with almost all data engines due to its long-standing standardization.
• Optimized query performance – Allows more efficient access patterns compared to full-table scans.
• File format flexibility – Supports multiple file formats, making it adaptable to various use cases.

However, despite these benefits, Hive-based table formats had several limitations, including:

• Inefficient for small updates – Making even minor changes required rewriting entire files.
• No safe multi-partition modifications – Changing data across multiple partitions was not reliable.
• Data inconsistency risks – Multiple jobs modifying the same dataset could lead to corruption or conflicts.
• Slow metadata operations – Retrieving directory listings for large tables was time-consuming.
• Complex physical data management – Users needed to understand the underlying table structure to optimize queries.

To overcome these challenges, Apache Iceberg was developed as a next-generation table format. It introduces better schema evolution, hidden partitioning, snapshot-based operations, and transactional consistency, solving many of the inefficiencies of traditional Hive tables. It does also bring several other features and optimizations.

What is Apache Iceberg? #

Now as you understood why Iceberg was developed, let’s address the question what’s Apache Iceberg. It is:

• A table format specification that defines how data is stored, structured, and managed within a table.
• A set of APIs and libraries designed to facilitate interaction with the table format.
  • These libraries integrate with various data processing engines and tools, enabling them to read, write, and manage Iceberg tables efficiently.

• Compatible with widely used big data processing engines such as Apache Spark, Trino, Flink, and Hive.

Note that Iceberg Is Not (❌):

• A Service: It is not a separate running service or database but a format specification that integrates with other data platforms.
• A Storage Engine: It does not handle physical data storage directly. Instead, it works with existing storage solutions like Amazon S3, HDFS, or Google Cloud Storage.
• An Execution Engine: Iceberg does not perform query execution or data processing. It relies on engines like Apache Spark, Trino, Presto, and Flink to process data.

Iceberg Benefits #

Conclusion #

In this introductory tutorial to Apache Iceberg, you’ve learned what Iceberg is and explored its key features that make it a powerful solution for managing large-scale data in modern data lakes.

To deepen your understanding and get hands-on experience, check out our practical guide: 👉 Getting Started with Apache Iceberg.