3.3 Data exporter

Data exporters are the final destination for your transformed data—they write your processed information to databases, data warehouses, files, or external systems. Data exporters within Mage empower you to dispatch data to a diverse array of destinations, including databases, data lakes, data warehouses, or local storage.

Purpose of data exporters

After spending time and effort cleaning, transforming, and enhancing your data, you need to store it somewhere useful. Data exporters handle this critical final step, ensuring your processed data reaches its intended destination in the correct format and with proper error handling.

Data exporters support numerous destination types:

• Data warehouses: Snowflake, BigQuery, Redshift, Databricks

• Databases: PostgreSQL, MySQL, MongoDB, DynamoDB

• Cloud storage: S3, Google Cloud Storage, Azure Blob Storage

• File formats: CSV, Parquet, JSON, Excel

• Streaming platforms: Kafka, Kinesis, Pub/Sub

Basic data exporter structure

Data exporters use a consistent pattern across all destination types:

@data_exporter
def export_data_to_postgres(df: DataFrame, **kwargs) -> None:
    schema_name = 'analytics'
    table_name = 'customer_metrics'
    config_path = path.join(get_repo_path(), 'io_config.yaml')
    config_profile = 'default'
    
    with Postgres.with_config(ConfigFileLoader(config_path, config_profile)) as loader:
        loader.export(
            df,
            schema_name,
            table_name,
            index=False,
            if_exists='replace'
        )

Configuration and connection management

Data exporters rely on the io_config.yaml file for connection details, keeping sensitive information secure and making it easy to switch between environments. The if_exists parameter controls how new data interacts with existing tables:

• 'replace': Overwrites the entire target table

• 'append': Adds new rows to existing data

• 'fail': Stops execution if the table already exists

Advanced export scenarios

Partitioned exports: Split large datasets across multiple files or table partitions for better performance and management.

Conditional exports: Use runtime variables to determine export destinations based on data characteristics or business rules.

Multi-destination exports: Export the same data to multiple targets simultaneously for backup, analytics, and operational use cases.

Conclusion

Data exporters complete the data pipeline journey by safely and reliably delivering your transformed data to its final destination. With Mage's comprehensive support for databases, data warehouses, cloud storage, and streaming platforms, you can ensure your processed data reaches the right systems in the right format every time. The consistent export patterns, built-in error handling, and flexible configuration options make it easy to adapt your pipelines to different environments and requirements, while advanced features like partitioned exports and multi-destination capabilities provide the scalability and reliability needed for production data engineering workflows.