MongoDB to Snowflake

This page provides you with instructions on how to extract data from MongoDB and load it into Snowflake. (If this manual process sounds onerous, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is MongoDB?

MongoDB or just Mongo, is an open source NoSQL database that stores data in JSON format. Storage is done using a document-oriented data model, and data fields can vary by document. MongoDB isn't tied to any specified data structure, meaning that there is no particular format or schema for the data in a Mongo database.

About Snowflake

Snowflake is a data warehouse solution that is entirely cloud based. It's a managed service. If you don't want to deal with hardware, software, or upkeep for a data warehouse you're going to love Snowflake. It runs on the wicked fast Amazon Web Services architecture using EC2 and S3 instances. Snowflake is designed to be flexible and easy to work with where other relational databases are not. One example of this is the query execution. Snowflake creates virtual warehouses where query processing takes place. These virtual warehouses run on separate compute clusters, so querying one of these virtual warehouses doesn't slow down the others. If you have ever had to wait for a query to complete, you know the value of speed and efficiency for query processing.

Getting data out of MongoDB

The first step of herding your MongoDB data into your data warehouse is pulling data out of MongoDB. This, friend, is extremely difficult to accomplish. The process will depend on how you've loaded data to MongoDB over time. In some cases, it may be impossible to get all of your data in a complete and thorough way.

Our data extraction difficulties can be blamed squarely on the fact that NoSQL databases don't require structure (i.e. specific columns). Many other databases use a more traditional, rigid, relational structure. This means that a predefined structure needs to be assembled before we can insert MongoDB data into a relational database.

Don't stress about the confusing data structure. Remember that lots of the data that is loaded into MongoDB is created by a computer. Therefore there is a pretty predictable structure. If you can find some specific fields that exist for every record, you're well on your way. Make sure these fields reliably appear in the records of each collection you'd like to replicate from MongoDB.

There is a lot of ways to do this. The most popular method to get data from MongoDB is to use the find() command.

Sample MongoDB data

MongoDB stores and returns JSON formatted data. Take a gander at the code below as an example of what a response might look like when querying the products collection.


db.products.find( { qty: { $gt: 25 } }, { _id: 0, qty: 0 } )

{ "item" : "pencil", "type" : "no.2" }
{ "item" : "bottle", "type" : "blue" }
{ "item" : "paper" }

Preparing data for Snowflake

Depending on the structure that you data is in, you may need to prepare it for loading. Take a look at the supported data types for Snowflake and make sure that the data you've got will map neatly to them. If you have a lot of data, you should compress it. Gzip, bzip2, Brotli, Zstandard v0.8 and deflate/raw deflate compression types are all supported.

One important thing to note here is that you don't need to define a schema in advance when loading JSON data into Snowflake. Onward to loading!

Loading data into Snowflake

There is a good reference for this step in the Data Loading Overview section of the Snowflake documentation. If there isn’t much data that you’re trying to load, then you might be able to use the data loading wizard in the Snowflake web UI. Chances are, the limitations on that tool will make it a non-starter as a reliable ETL solution. There two main steps to getting data into Snowflake:

  • Use the PUT command to stage files
  • Use the COPY INTO table command to load prepared data into the awaiting table from the prior step.

For the COPY step, you’ll have the option of copying from your local drive, or from Amazon S3. One of Snowflakes’ slick features lets you to make a virtual warehouse that will power the insertion process.

Keeping MongoDB data up to date

Fine job! You are the proud developer of a script that moves data from MongoDB to your data warehouse. This works as a one-shot deal. It's good to think about what will happen when there is new and updated data in MongoDB.

One option that works would be to load the entire MongoDB dataset all over again. That would certainly update the data, but it's not very efficient and can also cause terribly latency.

The smartest way to get data updated from MongoDB would be to identify primary keys that can be used as bookmarks to store where you script left off on the last run. Fields like updated_at, modified_at or other auto-incrementing data are useful here. With that done, you can set up your script as a cron job or continuous loop to identify new data as it appears.

Easier and faster alternatives

If all this sounds a bit overwhelming, don’t be alarmed. If you have all the skills necessary to go through this process, chances are building and maintaining a script like this isn’t a very high-leverage use of your time.

Thankfully, products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your MongoDB data via the API, structuring it in a way that is optimized for analysis, and inserting that data into your Snowflake data warehouse.