add_map is a method in dlt used to apply custom logic to each data item after extraction. It is typically used to modify records before they continue through the pipeline or are loaded to the destination. Common examples include transforming, enriching, validating, cleaning, restructuring, or anonymizing data early in the pipeline.

Method signature#

add_map method#

def add_map(
    item_map: ItemTransformFunc[TDataItem],
    insert_at: int = None
) -> TDltResourceImpl:
    ...

Use add_map to apply a function to each item extracted by a resource. It runs your logic on every record before it continues through the pipeline.

Arguments:

• item_map: A function that takes a single data item (and optionally metadata) and returns a modified item. If the resource yields a list, add_map applies the function to each item automatically.
• insert_at (optional): An integer that specifies where to insert the function in the pipeline. For example, if data yielding is at index 0, your transformation at index 1, and incremental processing at index 2, you should set insert_at=1.

This page covers how add_map works, where it fits in the pipeline, and how to use it in different scenarios.

Related methods#

In addition to add_map, dlt provides:

• add_filter: Excludes records based on a condition. Works like a filter function that removes items you don't want to load. (resource.add_filter).
• add_yield_map: Produces multiple outputs from a single input item. Returns an iterator instead of a single item. (resource.add_yield_map).
• add_limit: Limits the number of records processed by a resource. Useful for testing or reducing data volume during development. (resource.add_limit).
• add_metrics: Collects custom metrics from data flowing through the pipeline without modifying items. Useful for tracking statistics like counts, data quality metrics, or processing information. (resource.add_metrics).

These methods help you control the shape and flow of data during transformation.

add_map vs @dlt.transformer#

dlt offers two primary ways to handle data operations during extraction:

• add_map: Ideal for simple, item-level operations within a single resource. Typical examples include masking sensitive fields, formatting dates, or computing additional fields for each record individually as it's extracted.
• @dlt.transformer: Defines a separate transformer resource that enriches or transforms data during extraction, often by fetching related information concurrently or performing complex operations involving other endpoints or external APIs.

If your needs are straightforward and focused on single-record modifications or operations, add_map is usually the simplest, convenient and much more efficient choice.

Common use cases for add_map#

• Data cleaning and enrichment:
  Modify fields in each record as they are pulled from the source. For example, standardize date formats, compute new fields, or enrich records with additional info.

• Anonymizing or masking sensitive data:
  Before data is loaded into your warehouse, you might want to pseudonymize personally identifiable information (PII) or for GDPR compliance. Read docs here: Pseudonymizing columns.

• Removing or renaming fields:
  If certain fields from the source are not needed or should have different names, you can modify the record dictionaries in-place. Please find the docs here:

  • Removing columns.
  • Renaming columns.

• Incremental loading:
  When using incremental loading, you may need to adjust records before the incremental logic runs. This includes filling in missing timestamp or ID fields used as cursors, or dropping records that don’t meet criteria. The add_map function with the insert_at parameter lets you run these transformations at the right stage in the pipeline.

Controlling transformation order with insert_at#

dlt pipelines execute in multiple stages. For example, data is typically yielded at step index 0, transformations like add_map are applied at index 1, and incremental processing occurs in subsequent steps.

To ensure your transformations are applied before the incremental logic kicks in, it’s important to control the execution order using the insert_at parameter of the add_map function. This parameter lets you define exactly where your transformation logic is inserted within the pipeline.

import dlt
import hashlib

@dlt.resource
def user_data():
    yield {"id": 1, "first_name": "John", "last_name": "Doe", "email": "john.doe@example.com"}
    yield {"id": 2, "first_name": "Jane", "last_name": "Smith", "email": "jane.smith@example.com"}

# First transformation: mask email addresses
def mask_email(record):
    record["email"] = hashlib.sha256(record["email"].encode()).hexdigest()
    return record

# Second transformation: enrich with full name
def enrich_full_name(record):
    record["full_name"] = f"{record['first_name']} {record['last_name']}"
    return record

# Attach transformations explicitly controlling their order
transformed_users = (
    user_data()
    .add_map(enrich_full_name) # By default, this would be at the end
    .add_map(mask_email, insert_at=1) # Explicitly run masking first after extraction
)

# Verify the transformed data
for user in transformed_users:
    print(user)

Expected output

{'id': 1, 'first_name': 'John', 'last_name': 'Doe', 'email': '<hashed_value>', 'full_name': 'John Doe'}
{'id': 2, 'first_name': 'Jane', 'last_name': 'Smith', 'email': '<hashed_value>', 'full_name': 'Jane Smith'}

By explicitly using insert_at=1, the email masking step (mask_email) is executed right after data extraction and before enrichment. This ensures sensitive data is handled securely at the earliest stage possible.

Incremental behavior with insert_at#

Use insert_at to control when your transformation runs in the pipeline and ensure it executes before incremental filtering. See incremental loading documentation for more details.

Filling missing data for incremental loading#

Use add_map to ensure records are compatible with incremental loading.

If the incremental cursor field (e.g., updated_at) is missing, you can provide a fallback like created_at. This ensures all records have a valid cursor value and can be processed correctly by the incremental step.

In this example, the third record is made incremental-ready by assigning it a fallback updated_at value. This ensures it isn't skipped by the incremental loader.

add_map vs add_yield_map#

The difference between add_map and add_yield_map matters when a transformation returns multiple records from a single input.

add_map#

• Use add_map when you want to transform each item into exactly one item.
• Think of it like modifying or enriching a row.
• You use a regular function that returns one modified item.
• Great for adding fields or changing structure.

Example#

import dlt

@dlt.resource
def resource():
    yield [{"name": "Alice"}, {"name": "Bob"}]

def add_greeting(item):
    item["greeting"] = f"Hello, {item['name']}!"
    return item

resource.add_map(add_greeting)

for row in resource():
    print(row)

Output#

{'name': 'Alice', 'greeting': 'Hello, Alice!'}
{'name': 'Bob', 'greeting': 'Hello, Bob!'}

add_yield_map#

• Use add_yield_map when you want to turn one item into multiple items, or possibly no items.
• Your function is a generator that uses yield.
• Great for pivoting nested data, flattening lists, or filtering rows.

Example#

import dlt

@dlt.resource
def resource():
    yield [
        {"name": "Alice", "hobbies": ["reading", "chess"]},
        {"name": "Bob", "hobbies": ["cycling"]}
    ]

def expand_hobbies(item):
    for hobby in item["hobbies"]:
        yield {"name": item["name"], "hobby": hobby}

resource.add_yield_map(expand_hobbies)

for row in resource():
    print(row)

Output#

{'name': 'Alice', 'hobby': 'reading'}
{'name': 'Alice', 'hobby': 'chess'}
{'name': 'Bob', 'hobby': 'cycling'}

Best practices for using add_map#

• Keep transformations simple:
  Functions passed to add_map run on each record and should be stateless and lightweight. Use them for tasks like string cleanup or basic calculations. For heavier operations (like per-record API calls), batch the work or move it outside add_map, for example into a transformer resource or a post-load step.

• Use the right tool for the job:
  Use add_map for one-to-one record transformations. If you need to drop records, use add_filter instead of returning None in a map function. To split or expand one record into many, use add_yield_map.

• Chain transformations when needed:
  Since add_map and add_filter return the resource object, you can chain multiple transformations in sequence. Just be mindful of the order, they execute in the order they are added unless you explicitly control it using insert_at.

• Ordering with insert_at:
  When using multiple transforms and built-in steps (like incremental loading), control their order with insert_at.

  Pipeline steps are zero-indexed in the order they are added. Index 0 is usually the initial data extraction. To run a custom map first, set insert_at=1. For multiple custom steps, assign different indices (e.g., one at 1, another at 2). If you're unsure about the order, iterate over the resource or check the dlt logs to confirm how steps are applied.

• Advanced consideration - data formats
  Most dlt sources yield dictionaries or lists of them. However, some backends, such as PyArrow, may return data as NumPy arrays or Arrow tables. In these cases, your add_map or add_yield_map function must handle the input format, possibly by converting it to a list of dicts or a pandas DataFrame. This is an advanced scenario, but important if your transformation fails due to unexpected input types.