In this lesson, you will learn how to configure distributed query engines like Trino or Spark to communicate with open table formats like Iceberg or Delta Lake.
Bridging Compute and Storage
In a modern lakehouse, your storage layer (the table format) and your compute layer (the query engine) are decoupled. The table format acts as the "source of truth" by maintaining metadata (like manifest files or transaction logs) that describes exactly which physical data files constitute a table at any given time.
For a query engine to interact with this data, it needs a "connector" or "catalog" that knows how to read these metadata files. Instead of scanning an entire folder of files blindly, the engine consults the table format’s metadata to perform efficient data skipping and plan the query execution.
Configuring the Connection
To integrate these, you must define a catalog in your engine that points to your storage location and specifies the table format type. For example, in Trino, you define an Iceberg catalog in a properties file:
# etc/catalog/iceberg.properties
connector=iceberg
iceberg.catalog.type=hive
hive.metastore.uri=thrift://localhost:9083
Once this configuration is set, the engine can translate standard SQL commands into the specific actions required by the table format. When you run INSERT INTO my_iceberg_table ..., the engine asks the format to generate a new snapshot, update the manifest files, and commit the changes atomically. When you run a SELECT, the engine uses the table format's metadata to determine which files match your filter criteria, effectively bypassing unnecessary data and significantly reducing latency.
By configuring these engines correctly, you move away from treating a data lake as a simple "dump" of files and begin treating it as a relational database where schema, transactions, and snapshots are managed dynamically by the storage layer.
Key Takeaway: Integrating query engines with table formats requires configuring a catalog connection that allows the engine to interpret the underlying metadata, enabling the engine to leverage ACID transactions and optimized file pruning.