In this lesson, you will learn how to connect your Apache Iceberg tables to distributed query engines like Trino or Apache Spark to unlock high-performance analytics across your data lakehouse.
The Bridge Between Storage and Compute
Apache Iceberg is designed to be engine-agnostic, meaning the table format is decoupled from the compute layer. This is made possible by the Iceberg catalog and metadata layers. When you point a distributed query engine (like Trino) at an Iceberg table, the engine doesn't just scan files; it consults the metadata files (manifest lists and manifest files) to determine exactly which files contain the relevant data for a specific query.
Connecting via Catalogs
To perform analytical queries, you must register your Iceberg table in a catalog that the query engine can recognize. For example, if you are using Trino, you configure an Iceberg connector in your catalog properties file:
connector.name=iceberg
iceberg.catalog.type=hive
hive.metastore.uri=thrift://localhost:9083
Once this connection is established, the query engine treats the Iceberg table like a first-class citizen. When a user runs a SELECT statement, the engine uses the Iceberg metadata to perform data skipping. It reads the min/max statistics stored in the manifest files to prune unnecessary files before the execution engine even touches the data.
Why This Matters
By integrating with engines like Spark or Trino, you enable "data lakehouse" patterns where your storage stays in low-cost cloud object storage (like S3 or GCS), but your read performance rivals traditional data warehouses. Because Iceberg handles the ACID transactions and snapshot isolation, multiple engines can read from the table simultaneously without the risk of seeing inconsistent data or partial writes, even while other processes are compacting small files or expiring old snapshots.
By offloading the complexity of file management to the Iceberg metadata layer, your query engines can focus entirely on high-speed execution and distributed processing.
Key Takeaway: Integrating Iceberg with distributed query engines allows you to perform high-performance, ACID-compliant analytics directly on your data lake by leveraging Iceberg’s metadata to prune files and optimize query execution.