Marketing data lakes 101: everything you need to know
If you’ve ever dealt with marketing data, you’d probably agree that siloed data is the number one enemy of effective reporting and analytics.
And while cloud-based data warehouses like BigQuery and Snowflake are great solutions for integrating, storing, and analyzing large volumes of cross-channel data, many analytically mature companies have recently started adding a new foundation to their data architectures.
As you may have guessed, that foundation is called a marketing data lake. And in this post, we’ll discuss:
- What a marketing data lake is
- What the main benefits of data lakes are in the marketing context
- How to get started with replicating your marketing data to a data lake like Amazon S3, Google Cloud Storage, or Azure Blob Storage
What is a marketing data lake?
A marketing data lake is a cloud-based solution for storing and consolidating your organization’s unstructured and structured cross-channel marketing data in its raw form. In the marketing context, cloud storage solutions such as Amazon S3, Azure Blob Storage, and Google Cloud Storage are often used as data lakes.
A marketing data pipeline like Supermetrics allows you to replicate data from the most popular marketing data sources (like Facebook, Google Analytics, and Salesforce) directly into your data lake of choice.
From there, you can pull that data into a data warehouse for reporting and BI workflows and give direct access to your data science team so they can get the data they need with whatever tools they happen to be using.
For example, here’s what your marketing data architecture might look like if you’re working in the Google ecosystem.
The 3 main benefits of a data lake architecture
Now, let’s say you’re interested in storing your marketing data in a data warehouse like BigQuery or Snowflake but aren’t sure whether you should use a data lake in between or move your data directly to your warehouse.
Compared to connecting your data sources directly with your warehouse, using a data lake as the foundation has the following advantages:
1. Better data governance
Keeping your cross-channel, cross-department data organized is a lot easier in a data lake than in a data warehouse. For example, you can store all your data from Facebook Ads in a dedicated cloud storage bucket and start a new bucket for LinkedIn Ads, Twitter Ads, etc.
A data lake in S3, Google Cloud Storage, or Azure Blob Storage allows you to quickly inspect individual data files since you don’t have to write SQL to open a file in a data lake.
Alternatively, if you’re working at an agency, you can have a dedicated cloud storage bucket for each of your clients. This way, you can control access between the buckets. And when a client relationship ends, they won’t lose access to the data you’ve been storing in the data lake.
If you don’t want to lose access to any historical marketing data that you may need one day — but you also don’t want to clog your data pipeline or data warehouse with a bunch of metrics and dimensions you may never use — storing that data in a data lake is a good compromise.
2. Security and access
Most enterprises with stringent security standards would rather not have a managed data pipeline writing directly to their data warehouse. Even in a smaller company, you may have information in your data warehouse tables that you can’t share with any external parties.
If security is a concern, a data lake architecture can save you from the trouble of having to put your data warehouse behind a firewall. Instead, you can use a managed data pipeline like Supermetrics to automate your data transfers into a dedicated bucket in your data lake, and then move data between your data lake and warehouse with a tool like AWS Glue or Google Dataflow.
This way, you don’t have to worry about a third party accessing any of the internal data you’re storing in your data warehouse.
3. Near-instant backup
If you’re used to analyzing your data using SQL, you’ve probably noticed how easy it is to make mistakes. As a result of a bad SQL script, you might end up losing access to some of the data you need for your analysis.
Instead of going back to your data pipeline tool to rerun the queries and waiting for the data to reload, a data lake offers a faster way to restore the lost metrics and dimensions.
Rather than waiting around for your pipeline to back up the lost data, you can quickly restore the data you need from the data lake and pick up your analysis where you left off.
How to get started with replicating your marketing data to Amazon S3, Google Cloud Storage, or Azure Blob Storage?
If you’re ready to start moving your marketing data into a data lake, follow these five simple steps:
1. Create a new destination
Start your free 14-day trial of Supermetrics by logging in to https://team.supermetrics.com/.
Once you’ve signed in with your Google or Microsoft account, navigate to the “Destinations” tab, and choose “Create destination”.
Next, choose the cloud storage destination of your choice from the dropdown and follow the on-screen instructions to authorize access to the destination.
2. Configure your transfers with standard or custom schemas
There are two ways you can move data with Supermetrics: 1) with our standard schemas that cover the most common use cases or 2) with custom schemas that you can create, save, manage, and edit with the query manager.
If you’d like to use standard schemas, navigate to “Transfers” and click on “Create new”.
Then, choose a data source from the dropdown and set up the transfer following the on-screen instructions.
To create a custom schema, navigate to “Query Manager” and freely choose the metrics and dimensions you’re looking to replicate to your data lake.
Your custom schemas will be saved under the “Schemas” tab in Query Manager where you can view and modify them at any time.
Once your transfers are up and running, you can view them under the “Monitor” tab.
Once your transfer is complete, your cloud storage bucket should look a little something like this.
If you’d like to see how easy it is to automate your data transfers into a data lake with Supermetrics, start your free 14-day trial today.
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