Integrate VTEX Data with BigQuery for Useful Analytics and Activation

A Step-by-Step Guide

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Make the Most of Your VTEX Data in BigQuery

VTEX and BigQuery 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.

VTEX is a complete, connected, and composable platform. Build, manage and deliver advanced B2B, B2C and marketplace commerce experiences without the complexity. BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. Use built-in ML/AI and BI for insights at scale. 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 VTEX

The first step toward useful, modeled VTEX data in BigQuery is to connect the source and destination systems. There are many legacy tools available in market that handle the ETL or ELT transfer of VTEX data to BigQuery, 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 VTEX data modeling, analytics and activation easier once the data is landed in BigQuery.

To connect to VTEX, 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 “VTEX” within the data source library.

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

Log VTEX Data for Flexible Modeling in BigQuery

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

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

Define and Label VTEX Data for BigQuery

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 BigQuery starting at ingest. Simply moving JSON from VTEX to BigQuery leaves all the work for your data team in BigQuery.

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

There are third-party solutions that will catalog your VTEX data and generate semantic labels and mappings after you’ve landed it in BigQuery. With SoundCommerce, the VTEX data is defined and labeled on its way into BigQuery 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 VTEX data in BigQuery.

Map VTEX Entities to BigQuery

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

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

Defining, labeling and mapping the VTEX data on the way in means much less effort once the data is landed in BigQuery.

Materialize VTEX Data in BigQuery

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

  • 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 BigQuery to establish a secure destination for your labeled, mapped and modeled data.

That’s it! You now have logged, labeled and mapped data from VTEX flowing securely to BigQuery.

Model VTEX Data in BigQuery

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

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

Host the Modeled VTEX Data in BigQuery for Analytics

BigQuery 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 VTEX in your BigQuery models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of VTEX data out of BigQuery – so you can start making better decisions and taking better action as soon as you’ve connected VTEX to BigQuery.

Host the Modeled VTEX Data in BigQuery for Campaign and Customer Activation

Whether your marketing team uses VTEX 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 VTEX data in BigQuery to your most important marketing applications.

If you’ve followed the steps above to properly onboard and model your VTEX data in BigQuery, 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 VTEX data in BigQuery to use!

Getting Your VTEX Data Defined, Labeled, Mapped and Modeled in BigQuery is Easy!

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

Contact us today to get started with VTEX in BigQuery!

Technical Resources for Integrating
VTEX Data with BigQuery

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

VTEX API Documentation

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

BigQuery API Documentation

Integrate and Model VTEX Data in BigQuery