Make the Most of Your Adobe Commerce Data in Snowflake

Adobe Commerce and Snowflake are a natural fit for data modeling, business intelligence and data activation – especially for retail brands interested in optimizing their overall business performance, acquisition and retention marketing programs, and merchandising and fulfillment operations decisions.

Adobe Commerce is a flexible and scalable commerce platform that lets you create uniquely personalized B2B and B2C experiences, no matter how many brands you have. Snowflake enables every organization to mobilize their data with Snowflake’s Data Cloud. Customers use the Data Cloud to discover and securely share data, power data applications, and execute diverse AI/ML and analytic workloads. Together, the software application and cloud data tooling provide business and data practitioners with an opportunity to analyze and optimize ecommerce, shopping carts, composable commerce and subscription platforms to drive profitable growth.

Connect to Adobe Commerce

The first step toward useful, modeled Adobe Commerce data in Snowflake is to connect the source and destination systems. There are many legacy tools available in market that handle the ETL or ELT transfer of Adobe Commerce data to Snowflake, and there are emerging tools that accomplish this transfer while providing value-added services like local data logging, and semantic data labeling and mapping along the way – making Adobe Commerce data modeling, analytics and activation easier once the data is landed in Snowflake.

To connect to Adobe Commerce, follow these easy steps

• Open SoundCommerce in any browser. Open the “Intelligent Pipeline” application from the top right navigation menu. Select “Sources” from the left navigation menu. Choose “Add New Source” from within the Sources pane to open the data source library.

• Search or browse to find “Adobe Commerce” within the data source library.

• Complete the “Connection Setup” form with your credentials and token to securely connect to Adobe Commerce and begin collecting source data.

Log Adobe Commerce Data for Flexible Modeling in Snowflake

There are a few more considerations to address along the way. First, what happens if Adobe Commerce is unavailable for some reason, or the data you’re expecting has been purged by Adobe Commerce? What happens when Adobe Commerce changes their API schemas and data scope? What happens if you need to reinterpret your Adobe Commerce data for a new use case in the future?

You’ll want your Adobe Commerce data immutably logged locally, just upstream of Snowflake to ensure you have the data and data flow flexibility you need to future-proof your Adobe Commerce data and models. SoundCommerce provides permanent logging of Adobe Commerce data upstream of Snowflake to ensure failover and future-proofing. Regardless of how you connect your Adobe Commerce and Snowflake data, you’ll want a data lake or event log in the middle to ensure data integrity and modeling flexibility.

Define and Label Adobe Commerce Data for Snowflake

As new technologies arise and best practices evolve, traditional integration tools like ETL and ELT data pipelines are giving way to intelligent pipelines that help prep data for Snowflake starting at ingest. Simply moving JSON from Adobe Commerce to Snowflake leaves all the work for your data team in Snowflake.

As you onboard your Adobe Commerce data into Snowflake, you’ll want to create semantic labels and metadata that describe the Adobe Commerce data for easier unification and modeling across other systems and data in Snowflake.

There are third-party solutions that will catalog your Adobe Commerce data and generate semantic labels and mappings after you’ve landed it in Snowflake. With SoundCommerce, the Adobe Commerce data is defined and labeled on its way into Snowflake instead, to avoid this costly rework later. You’ll end up with business-ready entities like orders, customers, products and campaigns, making it much easier to model your Adobe Commerce data in Snowflake.

Map Adobe Commerce Entities to Snowflake

Once the raw Adobe Commerce data has been organized into useful entities, it’s time to map the Adobe Commerce data into useful tables in Snowflake.

Why do defined and labeled entities from Adobe Commerce matter so much? The main reason is that Adobe Commerce data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw Adobe Commerce data in Snowflake without this semantic understanding means data engineering and analyst teams must do all of the heavy lifting regarding the meaning of the Adobe Commerce data and the standardization of the meaning of that Adobe Commerce data from scratch in Snowflake.

Defining, labeling and mapping the Adobe Commerce data on the way in means much less effort once the data is landed in Snowflake.

Materialize Adobe Commerce Data in Snowflake

Next, you’ll establish a secure connection to Snowflake:

• Select “Destinations” from the left navigation menu. Choose “Add New Destination” from within the Destinations pane to open the data destination library.

• Complete the “Connection Setup” form to securely connect to Snowflake to establish a secure destination for your labeled, mapped and modeled data.

That’s it! You now have logged, labeled and mapped data from Adobe Commerce flowing securely to Snowflake.

Model Adobe Commerce Data in Snowflake

Once you have well-formed entities from Adobe Commerce onboarded to Snowflake, it’s time to build useful analytical and behavioral models on the Adobe Commerce data – and combine the Adobe Commerce data with data sets from other systems in Snowflake for more advanced, cross-dimensional analysis.

You can build your own analytical models on the Adobe Commerce data in Snowflake using languages like SQL and Python, organized into model libraries in tools like DBT or Coalesce. With SoundCommerce, you get prebuilt analytical models for Adobe Commerce running in Snowflake, with ready access to the model source code in DBT.

Host the Modeled Adobe Commerce Data in Snowflake for Analytics

Snowflake supports reporting and visualization through a wide variety of analytics tools including Sigma, Tableau, Looker, Power BI and Microstrategy to name a few. You can build your own dashboards, tabular views and graphs in any of these tools to reveal insights about Adobe Commerce in your Snowflake models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of Adobe Commerce data out of Snowflake – so you can start making better decisions and taking better action as soon as you’ve connected Adobe Commerce to Snowflake.

Host the Modeled Adobe Commerce Data in Snowflake for Campaign and Customer Activation

Whether your marketing team uses Adobe Commerce for activation – or uses other tools and channels or both to take action on the data – you’ll want to be able to easily move your modeled Adobe Commerce data in Snowflake to your most important marketing applications.

If you’ve followed the steps above to properly onboard and model your Adobe Commerce data in Snowflake, it’s easy to use reverse ETL (rETL) tools like Census or Hightouch to orchestrate the data from there, or use SoundCommerce native orchestrations to push data into common channels and applications like Facebook, Instagram, TikTok, Braze, Klaviyo, Insider or Dynamic Yield to put the Adobe Commerce data in Snowflake to use!

Getting Your Adobe Commerce Data Defined, Labeled, Mapped and Modeled in Snowflake is Easy!

SoundCommerce can automate the steps necessary to bring Adobe Commerce data into Snowflake, addressing the key functions of raw Adobe Commerce data logging, Adobe Commerce semantic definitions and mappings, and pre-built Adobe Commerce data models that are analytics- and activation ready in Snowflake.

Contact us today to get started with Adobe Commerce in Snowflake!

Technical Resources for Integrating
Adobe Commerce Data with Snowflake

More information and technical specifications for data collection from Adobe Commerce is available at:

Adobe Commerce API Documentation

More information and technical specifications for data ingest into Snowflake is available at:

Snowflake API Documentation