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Python Tutorial: Loading Data from mongodb to microsoft sql server with dlt

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This page provides technical documentation on how to load data from mongodb, a developer data platform built on a leading modern database, to Microsoft SQL Server, a relational database management system (RDBMS). The process uses an open-source Python library called dlt. mongodb simplifies data handling, accelerating your ideas to market. Applications and tools can connect to a Microsoft SQL Server instance or database and communicate using Transact-SQL. With dlt, you can efficiently transfer data from mongodb to Microsoft SQL Server. For more information on mongodb, visit https://www.mongodb.com/.

dlt Key Features​

  • Automatic Data Extraction: dlt scripts automatically extract data from a variety of sources including APIs, providing a seamless data extraction process. Read more about the extraction process.
  • Normalization Engine: dlt features a configurable normalization engine that unpacks complex, nested data structures into relational tables. This ensures data consistency and quality. Learn more about normalization.
  • Robust Data Loading: Data is loaded into your chosen destination safely and efficiently. dlt uses configurable, idempotent, atomic loads to ensure data integrity. Discover more about the loading process.
  • Data Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This ensures data consistency, traceability, and control throughout the data processing lifecycle. Explore more about governance support.
  • Scalability and Fine-tuning: dlt provides several mechanisms and configuration options to scale up and fine-tune pipelines, ensuring optimal performance. Read more about scaling and fine-tuning.

Getting started with your pipeline locally​

0. Prerequisites​

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt​

First you need to install the dlt library with the correct extras for Microsoft SQL Server:

pip install "dlt[mssql]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from MongoDB to Microsoft SQL Server. You can run the following commands to create a starting point for loading data from MongoDB to Microsoft SQL Server:

# create a new directory
mkdir mongodb_pipeline
cd mongodb_pipeline
# initialize a new pipeline with your source and destination
dlt init mongodb mssql
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


pymongo>=4.3.3
dlt[mssql]>=0.3.5

You now have the following folder structure in your project:

mongodb_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── mongodb/                   # folder with source specific files
│   └── ...
├── mongodb_pipeline.py        # your main pipeline script
├── requirements.txt         # dependencies for your pipeline
└── .gitignore               # ignore files for git (not required)

2. Configuring your source and destination credentials​

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml​

# put your configuration values here

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
dlthub_telemetry = true

generated secrets.toml​

# put your secret values and credentials here. do not share this file and do not push it to github

[sources.mongodb]
connection_url = "connection_url" # please set me up!

[destination.mssql.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 1433
connect_timeout = 15
driver = "driver" # please set me up!

2.1. Adjust the generated code to your usecase​

Further help setting up your source and destinations
  • Read more about setting up the MongoDB source in our docs.
  • Read more about setting up the Microsoft SQL Server destination in our docs.

3. Running your pipeline for the first time​

The dlt cli has also created a main pipeline script for you at mongodb_pipeline.py, as well as a folder mongodb that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:


from typing import List

import dlt
from dlt.common import pendulum
from dlt.common.pipeline import LoadInfo
from dlt.common.typing import TDataItems
from dlt.pipeline.pipeline import Pipeline

# As this pipeline can be run as standalone script or as part of the tests, we need to handle the import differently.
try:
    from .mongodb import mongodb, mongodb_collection  # type: ignore
except ImportError:
    from mongodb import mongodb, mongodb_collection


def load_select_collection_db(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongodb source to reflect an entire database schema and load select tables from it.

    This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
    """
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='mssql',
            dataset_name="mongo_select",
        )

    # Configure the source to load a few select collections incrementally
    mflix = mongodb(incremental=dlt.sources.incremental("date")).with_resources(
        "comments"
    )

    # Run the pipeline. The merge write disposition merges existing rows in the destination by primary key
    info = pipeline.run(mflix, write_disposition="merge")

    return info


def load_select_collection_db_items(parallel: bool = False) -> TDataItems:
    """Get the items from a mongo collection in parallel or not and return a list of records"""
    comments = mongodb(
        incremental=dlt.sources.incremental("date"), parallel=parallel
    ).with_resources("comments")
    return list(comments)


def load_select_collection_db_filtered(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongodb source to reflect an entire database schema and load select tables from it.

    This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
    """
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='mssql',
            dataset_name="mongo_select_incremental",
        )

    # Configure the source to load a few select collections incrementally
    movies = mongodb_collection(
        collection="movies",
        incremental=dlt.sources.incremental(
            "lastupdated", initial_value=pendulum.DateTime(2016, 1, 1, 0, 0, 0)
        ),
    )

    # Run the pipeline. The merge write disposition merges existing rows in the destination by primary key
    info = pipeline.run(movies, write_disposition="merge")

    return info


def load_select_collection_hint_db(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongodb source to reflect an entire database schema and load select tables from it.

    This example sources data from a sample mongo database data from [mongodb-sample-dataset](https://github.com/neelabalan/mongodb-sample-dataset).
    """
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='mssql',
            dataset_name="mongo_select_hint",
        )

    # Load a table incrementally with append write disposition
    # this is good when a table only has new rows inserted, but not updated
    airbnb = mongodb().with_resources("listingsAndReviews")
    airbnb.listingsAndReviews.apply_hints(
        incremental=dlt.sources.incremental("last_scraped")
    )

    info = pipeline.run(airbnb, write_disposition="append")

    return info


def load_entire_database(pipeline: Pipeline = None) -> LoadInfo:
    """Use the mongo source to completely load all collection in a database"""
    if pipeline is None:
        # Create a pipeline
        pipeline = dlt.pipeline(
            pipeline_name="local_mongo",
            destination='mssql',
            dataset_name="mongo_database",
        )

    # By default the mongo source reflects all collections in the database
    source = mongodb()

    # Run the pipeline. For a large db this may take a while
    info = pipeline.run(source, write_disposition="replace")

    return info


if __name__ == "__main__":
    # Credentials for the sample database.
    # Load selected tables with different settings
    print(load_select_collection_db())
    # print(load_select_collection_db_filtered())

    # Load all tables from the database.
    # Warning: The sample database is large
    # print(load_entire_database())

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:

python mongodb_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline local_mongo info

You can also use streamlit to inspect the contents of your Microsoft SQL Server destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline local_mongo show

5. Next steps to get your pipeline running in production​

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to​

  • Deploy with Github Actions: dlt can be deployed using Github Actions. This is a CI/CD runner that can be used for free. You can specify when the GitHub Action should run using a cron schedule expression. Learn more
  • Deploy with Airflow: dlt can be deployed using Airflow, a platform to programmatically author, schedule and monitor workflows. It provides a way to organize tasks and includes a scheduler to handle triggering tasks. Learn more
  • Deploy with Google Cloud Functions: You can also deploy dlt using Google Cloud Functions, a serverless execution environment for building and connecting cloud services. This allows you to execute your code in response to events without having to manage servers. Learn more
  • Other deployment options: There are other ways to deploy dlt including using Docker, Kubernetes, and more. Learn more

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to keep track of your data pipeline's performance and status. You can view the data load, package information, and other useful details. To learn more, visit How to Monitor your pipeline.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any changes or issues in your pipeline. This feature helps you to promptly respond to any potential problems. Check out Set up alerts for more information.
  • Enable Tracing: dlt provides a tracing feature that allows you to track the runtime of your pipeline. It provides detailed timing information for extract, normalize, and load steps. Learn more at Set up tracing.

Additional pipeline guides​

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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