Integrate ContentServ Data with BigQuery for Useful Analytics and Activation

A Step-by-Step Guide

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Make the Most of Your ContentServ Data in BigQuery

ContentServ 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.

ContentServ provides an all-in-one AI-fueled cloud solution to help marketers, product teams, and IT professionals manage product content at scale, get products to market faster, and personalize customer experiences. 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 retail ERP, WMS, POS and carrier platforms to drive profitable growth.

Connect to ContentServ

The first step toward useful, modeled ContentServ 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 ContentServ 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 ContentServ data modeling, analytics and activation easier once the data is landed in BigQuery.

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

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

Log ContentServ Data for Flexible Modeling in BigQuery

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

You’ll want your ContentServ data immutably logged locally, just upstream of BigQuery to ensure you have the data and data flow flexibility you need to future-proof your ContentServ data and models. SoundCommerce provides permanent logging of ContentServ data upstream of BigQuery to ensure failover and future-proofing. Regardless of how you connect your ContentServ 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 ContentServ 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 ContentServ to BigQuery leaves all the work for your data team in BigQuery.

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

There are third-party solutions that will catalog your ContentServ data and generate semantic labels and mappings after you’ve landed it in BigQuery. With SoundCommerce, the ContentServ 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 ContentServ data in BigQuery.

Map ContentServ Entities to BigQuery

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

Why do defined and labeled entities from ContentServ matter so much? The main reason is that ContentServ data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw ContentServ 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 ContentServ data and the standardization of the meaning of that ContentServ data from scratch in BigQuery.

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

Materialize ContentServ 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 ContentServ flowing securely to BigQuery.

Model ContentServ Data in BigQuery

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

You can build your own analytical models on the ContentServ 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 ContentServ running in BigQuery, with ready access to the model source code in DBT.

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

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

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

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

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

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

Contact us today to get started with ContentServ in BigQuery!

Technical Resources for Integrating
ContentServ Data with BigQuery

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

ContentServ API Documentation

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

BigQuery API Documentation

Integrate and Model ContentServ Data in BigQuery