## IcebergInput

URL: https://docs.atlan.com/product/capabilities/build-apps/sdks/application-sdk/references/inputs/iceberg-input

> Reads data from Apache Iceberg tables using daft. Provides support for reading Iceberg table data as DataFrames with lazy evaluation.

IcebergInput reads data from Apache Iceberg tables using daft. Provides support for reading Iceberg table data as DataFrames. Note: get_batched_dataframe() and get_batched_daft_dataframe() aren't implemented due to memory concerns with partitioning large Iceberg tables.

IcebergInput inherits from the base [Input](https://docs.atlan.com/llms/platform/build-apps/inputs/llms.txt) class and provides specialized functionality for reading from Apache Iceberg tables.

 pd.DataFrame",
 purpose: "Reads data from Iceberg table and returns as a single pandas DataFrame. Internally uses daft and converts to pandas. Uses get_daft_dataframe() internally and converts to pandas using to_pandas().",
 async: true,
 returns: {
 type: "pd.DataFrame",
 description: "Complete table data as pandas DataFrame"
 }
 },
 {
 name: "get_daft_dataframe",
 signature: "async get_daft_dataframe(self) -> daft.DataFrame",
 purpose: "Reads data from Iceberg table and returns as a single daft DataFrame with lazy evaluation. Uses daft.read_iceberg() to read the table.",
 async: true,
 returns: {
 type: "daft.DataFrame",
 description: "Table data as daft DataFrame with lazy evaluation"
 }
 },
 {
 name: "get_batched_dataframe",
 signature: "async get_batched_dataframe(self) -> AsyncIterator[pd.DataFrame]",
 purpose: "Not implemented - Raises NotImplementedError. This method is intentionally not implemented because partitioning daft DataFrames using dataframe.into_partitions() loads all partitions into memory, which can cause out-of-memory issues for large tables.",
 async: true,
 returns: {
 type: "NotImplementedError",
 description: "Raises NotImplementedError"
 }
 },
 {
 name: "get_batched_daft_dataframe",
 signature: "async get_batched_daft_dataframe(self) -> AsyncIterator[daft.DataFrame]",
 purpose: "Not implemented - Raises NotImplementedError. This method is intentionally not implemented for the same reason as get_batched_dataframe() - memory concerns with partitioning large Iceberg tables.",
 async: true,
 returns: {
 type: "NotImplementedError",
 description: "Raises NotImplementedError"
 }
 }
 ]}

 usage={[
 {
 title: "Read Iceberg table",
 description: "Initialize with Iceberg table object and read data",
 example: {
 code: `from application_sdk.inputs import IcebergInput
from pyiceberg.table import Table

iceberg_input = IcebergInput(
 table=iceberg_table, # pyiceberg.table.Table instance
 chunk_size=100000
)

df = await iceberg_input.get_dataframe()`,
 language: "python"
 }
 }
 ]}
/>

## See also

- [Inputs](https://docs.atlan.com/llms/platform/build-apps/inputs/llms.txt): Overview of all input classes and common usage patterns
- [SQLQueryInput](https://docs.atlan.com/llms/platform/build-apps/sql-query-input/llms.txt): Read data from SQL databases by executing SQL queries
- [ParquetInput](https://docs.atlan.com/llms/platform/build-apps/parquet-input/llms.txt): Read data from Parquet files, supporting single files and directories
- [Application SDK README](https://docs.atlan.com/llms/platform/build-apps/application-sdk/llms.txt): Overview of the Application SDK and its components

_Last updated: 15 May 2026._

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