1 The world of the Apache Iceberg Lakehouse

The chapter situates the lakehouse within the broader evolution of data architectures, explaining how organizations have long balanced performance, cost, flexibility, and governance as data scales and diversifies. It traces the path from OLTP systems to enterprise and cloud data warehouses and then to Hadoop-era data lakes—highlighting benefits such as elasticity and low-cost storage alongside persistent pain points like rigidity, vendor lock-in, data duplication, and “data swamps.” The lakehouse emerges as a response that unifies the ease, reliability, and performance of warehouses with the openness, scalability, and cost-efficiency of lakes, enabling a single, governed source of truth that multiple tools can query without excessive data movement.

Apache Iceberg anchors this paradigm by introducing an open, vendor-agnostic table format that makes data-lake files behave like reliable database tables. It adds ACID transactions, seamless schema and partition evolution, time travel, and hidden partitioning while organizing rich metadata in layered structures (table metadata, snapshot manifest lists, and file manifests) to enable efficient pruning and fast queries across massive datasets. Built as a community-driven standard and supported by a broad ecosystem of engines and platforms, Iceberg improves interoperability and reduces lock-in, while addressing historical weaknesses of data lakes—consistency, governance, and performance—without sacrificing the openness that teams need.

The chapter also outlines the modular components of an Iceberg lakehouse—storage, ingestion, catalog, federation/modeling, and consumption—and how they work together to deliver scalable, governed analytics. Object storage holds Parquet data and Iceberg metadata, batch and streaming tools populate tables, catalogs serve as the single source of truth for discovery and governance, federation layers unify and accelerate data for analysis, and the consumption layer powers BI, AI/ML, and operational applications. By reducing duplication and ETL, enabling multi-engine access to one canonical dataset, and decoupling storage from compute, an Iceberg lakehouse offers a practical path to performance, cost control, and flexibility—making it a compelling choice for organizations modernizing their data platforms.

The evolution of data platforms from on-prem warehouses to data lakehouses.

The role of the table format in data lakehouses.

The anatomy of a lakehouse table, metadata files, and data files.

The structure and flow of an Apache Iceberg table read and write operation.

Engines use metadata statistics to eliminate data files from being scanned for faster queries.

Engines can scan older snapshots, which will provide a different list of files to scan, enabling scanning older versions of the data.

The components of a complete data lakehouse implementation

• Data lakehouse architecture combines the scalability and cost-efficiency of data lakes with the performance, ease of use, and structure of data warehouses, solving key challenges in governance, query performance, and cost management.
• Apache Iceberg is a modern table format that enables high-performance analytics, schema evolution, ACID transactions, and metadata scalability. It transforms data lakes into structured, mutable, governed storage platforms.
• Iceberg eliminates significant pain points of OLTP databases, enterprise data warehouses, and Hadoop-based data lakes, including high costs, rigid schemas, slow queries, and inconsistent data governance.
• With features like time travel, partition evolution, and hidden partitioning, Iceberg reduces storage costs, simplifies ETL, and optimizes compute resources, making data analytics more efficient.
• Iceberg integrates with query engines (Trino, Dremio, Snowflake), processing frameworks (Spark, Flink), and open lakehouse catalogs (Nessie, Polaris, Gravitino), enabling modular, vendor-agnostic architectures.
• The Apache Iceberg Lakehouse has five key components: storage, ingestion, catalog, federation, and consumption.

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