## Outputs

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

> Complete reference for output classes that write data to various destinations including Parquet files, JSON files, and Iceberg tables.

None",
 purpose: "Writes a daft DataFrame to the output destination. Must be implemented by all output classes.",
 async: true,
 parameters: [
 {
 name: "dataframe",
 type: "daft.DataFrame",
 required: true,
 description: "The daft DataFrame to write"
 }
 ]
 },
 {
 name: "write_dataframe",
 signature: "async write_dataframe(self, dataframe: pd.DataFrame) -> None",
 purpose: "Writes a pandas DataFrame to the output, automatically handling chunking and file size management. Estimates file size based on DataFrame sample, splits large DataFrames into chunks based on buffer_size and max_file_size_bytes, automatically uploads files to object store when size limits are reached, and records metrics for successful writes.",
 async: true,
 parameters: [
 {
 name: "dataframe",
 type: "pd.DataFrame",
 required: true,
 description: "The pandas DataFrame to write"
 }
 ]
 },
 {
 name: "write_batched_dataframe",
 signature: "async write_batched_dataframe(self, batch_df: pd.DataFrame) -> None",
 purpose: "Writes batched pandas DataFrames from async or sync generators. Handles both AsyncGenerator and Generator types, skips empty DataFrames automatically, and processes each batch through write_dataframe().",
 async: true,
 parameters: [
 {
 name: "batch_df",
 type: "pd.DataFrame",
 required: true,
 description: "Batched pandas DataFrame to write"
 }
 ]
 },
 {
 name: "write_batched_daft_dataframe",
 signature: "async write_batched_daft_dataframe(self, batch_daft_df: daft.DataFrame) -> None",
 purpose: "Writes batched daft DataFrames from async or sync generators. Handles both AsyncGenerator and Generator types, skips empty DataFrames automatically, and processes each batch through write_daft_dataframe().",
 async: true,
 parameters: [
 {
 name: "batch_daft_df",
 type: "daft.DataFrame",
 required: true,
 description: "Batched daft DataFrame to write"
 }
 ]
 },
 {
 name: "get_statistics",
 signature: "async get_statistics(self, typename: Optional[str] = None) -> ActivityStatistics",
 purpose: "Returns statistics about the output operation including total record count and chunk information.",
 async: true,
 parameters: [
 {
 name: "typename",
 type: "Optional[str]",
 required: false,
 description: "Type name of the entity (e.g., 'database', 'schema', 'table')"
 }
 ],
 returns: {
 type: "ActivityStatistics",
 description: "Object containing output statistics"
 }
 },
 {
 name: "path_gen",
 signature: "path_gen(self, chunk_count: Optional[int] = None, chunk_part: int = 0, start_marker: Optional[str] = None, end_marker: Optional[str] = None) -> str",
 purpose: "Generates file paths for output chunks with support for query extraction markers.",
 async: false,
 parameters: [
 {
 name: "chunk_count",
 type: "Optional[int]",
 required: false,
 description: "Total number of chunks"
 },
 {
 name: "chunk_part",
 type: "int",
 required: false,
 description: "Part number within chunk (default: 0)"
 },
 {
 name: "start_marker",
 type: "Optional[str]",
 required: false,
 description: "Start marker for query extraction"
 },
 {
 name: "end_marker",
 type: "Optional[str]",
 required: false,
 description: "End marker for query extraction"
 }
 ],
 returns: {
 type: "str",
 description: "Generated file path"
 }
 },
 {
 name: "process_null_fields",
 signature: "process_null_fields(self, obj: Any, preserve_fields: Optional[List[str]] = None, null_to_empty_dict_fields: Optional[List[str]] = None) -> Any",
 purpose: "Recursively removes null values from dictionaries and lists, with options to preserve specific fields or convert nulls to empty dictionaries.",
 async: false,
 parameters: [
 {
 name: "obj",
 type: "Any",
 required: true,
 description: "The object to clean (dict, list, or other value)"
 },
 {
 name: "preserve_fields",
 type: "Optional[List[str]]",
 required: false,
 description: "Field names to preserve even if null"
 },
 {
 name: "null_to_empty_dict_fields",
 type: "Optional[List[str]]",
 required: false,
 description: "Field names to convert from null to empty dict"
 }
 ],
 returns: {
 type: "Any",
 description: "Cleaned object with null values removed"
 }
 }
 ]}
/>

## WriteMode enum

The `WriteMode` enum defines the available write modes for output operations:

```python
class WriteMode(Enum):
 APPEND = "append" # Append data to existing files
 OVERWRITE = "overwrite" # Overwrite existing files
 OVERWRITE_PARTITIONS = "overwrite-partitions" # Overwrite specific partitions
```

## Output implementations

The Application SDK provides three concrete implementations of the base `Output` class, each optimized for different data formats and storage requirements. All implementations inherit the common functionality from the base class, including automatic chunking, buffer management, statistics tracking, and object store uploads.

 Writes data to Parquet files with support for chunking, consolidation, Hive partitioning, and automatic object store uploads.

 Writes data to JSON files (JSONL format) with support for chunking, buffering, null field processing, and automatic object store uploads.

 Writes data to Apache Iceberg tables using daft. Supports table creation, schema inference, and multiple write modes.

## See also

- [Inputs](https://docs.atlan.com/llms/platform/build-apps/inputs/llms.txt): Read data from various sources including SQL queries, Parquet files, JSON files, and Iceberg tables
- [Application SDK README](https://docs.atlan.com/llms/platform/build-apps/application-sdk/llms.txt): Overview of the Application SDK and its components
- [App structure](https://docs.atlan.com/llms/platform/build-apps/app-structure/llms.txt): Standardized folder structure for Atlan applications
- [StateStore](https://docs.atlan.com/llms/platform/build-apps/statestore/llms.txt): Persistent state management for workflows and credentials

_Last updated: 10 August 2026._

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