ODBC

As explained in Architecting an Apache Iceberg Lakehouse

ODBC is a language-independent API standard for connecting applications to database management systems and executing SQL statements.

ODBC (Open Database Connectivity) is a widely adopted standard interface for connecting analytical and business applications to structured data sources and executing queries. In a lakehouse, query engines or federation layers expose ODBC endpoints so familiar tools can access governed data without bespoke integrations. section 9.3.1 section 8.2.3

ODBC supports architecture goals while imposing practical tradeoffs:

• Connects BI tools, reporting applications, and spreadsheets such as Excel, Looker, Tableau, and Power BI through established drivers. section 8.2.3 section 9.3.1
• Centralizes access through query or federation engines, allowing consistent access policies, metric definitions, and query optimizations. section 9.3 section 9.3.1
• Handles synchronous, tabular workloads well but can struggle with high-volume transfers and streaming, while requiring driver configuration and management. section 9.3.1

Lakehouse access through ODBC

An analyst uses Excel or Looker to query an ODBC endpoint exposed by Trino, Dremio, or Spark SQL. The engine presents Iceberg tables through a relational-style interface; a federation layer can route the query across sources while enforcing centralized policies. section 9.3.1

Figure 9.2 JDBC/ODBC has a large ecosystem of drivers for systems from which you can pull data. This allows you to connect to tools like Dremio and Trino to pull data from your Apache Iceberg lakehouse.

Shows how ODBC and JDBC drivers connect consumption tools and query engines to an Apache Iceberg lakehouse. section 9.3.1

How Architecting an Apache Iceberg Lakehouse covers it

How deep it goes

Explains endpoints, drivers, centralized enforcement, and workload limitations, but stops short of implementation configuration.

Examples

Uses Excel, Looker, Tableau, Power BI, Trino, Dremio, Spark SQL, and Iceberg lakehouse tables as concrete integration examples.

What it assumes

Assumes familiarity with structured data, BI tools, query engines, federation layers, and lakehouse consumption.

What you can do after

Operate/configure standardized lakehouse access for familiar tools while recognizing driver-management and high-volume-transfer constraints.

How it connects

Links ODBC with JDBC, query engines, federation layers, semantic layers, Iceberg, and Arrow Flight.

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Related concepts

• query engineuses · A query engine is a software component that processes queries against data or knowledge sources and produces results.
• business intelligence toolused by · A business intelligence tool is software that analyzes organizational data and produces reports, dashboards, visualizations, or other outputs to support decision-making.
• client librarykind of · A client library is reusable software that provides programmatic interfaces for an application to interact with a service, platform, or system.

This explainer was generated by Manning's knowledge graph from the text of Architecting an Apache Iceberg Lakehouse. The section links point to the parts of the book it draws on.