Integrate PredictSpring Data with Snowflake for Useful Analytics and Activation
A Step-by-Step Guide
Make the Most of Your PredictSpring Data in Snowflake
PredictSpring 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.
PredictSpring is the leading omni-channel commerce platform in the retail POS market. PredictSpring Modern POS provides a seamless in-store experience for brands with support for full POS, mPOS, cash management, clienteling, endless aisle, inventory management and curbside pickup. 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 retail ERP, WMS, POS and carrier platforms to drive profitable growth.
Connect to PredictSpring
The first step toward useful, modeled PredictSpring 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 PredictSpring 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 PredictSpring data modeling, analytics and activation easier once the data is landed in Snowflake.
To connect to PredictSpring, 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 “PredictSpring” within the data source library.
Complete the “Connection Setup” form with your credentials and token to securely connect to PredictSpring and begin collecting source data.
Log PredictSpring Data for Flexible Modeling in Snowflake
There are a few more considerations to address along the way. First, what happens if PredictSpring is unavailable for some reason, or the data you’re expecting has been purged by PredictSpring? What happens when PredictSpring changes their API schemas and data scope? What happens if you need to reinterpret your PredictSpring data for a new use case in the future?
You’ll want your PredictSpring data immutably logged locally, just upstream of Snowflake to ensure you have the data and data flow flexibility you need to future-proof your PredictSpring data and models. SoundCommerce provides permanent logging of PredictSpring data upstream of Snowflake to ensure failover and future-proofing. Regardless of how you connect your PredictSpring 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 PredictSpring 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 PredictSpring to Snowflake leaves all the work for your data team in Snowflake.
As you onboard your PredictSpring data into Snowflake, you’ll want to create semantic labels and metadata that describe the PredictSpring data for easier unification and modeling across other systems and data in Snowflake.
There are third-party solutions that will catalog your PredictSpring data and generate semantic labels and mappings after you’ve landed it in Snowflake. With SoundCommerce, the PredictSpring 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 PredictSpring data in Snowflake.
Map PredictSpring Entities to Snowflake
Once the raw PredictSpring data has been organized into useful entities, it’s time to map the PredictSpring data into useful tables in Snowflake.
Why do defined and labeled entities from PredictSpring matter so much? The main reason is that PredictSpring data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw PredictSpring 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 PredictSpring data and the standardization of the meaning of that PredictSpring data from scratch in Snowflake.
Defining, labeling and mapping the PredictSpring data on the way in means much less effort once the data is landed in Snowflake.
Materialize PredictSpring 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 PredictSpring flowing securely to Snowflake.
Model PredictSpring Data in Snowflake
Once you have well-formed entities from PredictSpring onboarded to Snowflake, it’s time to build useful analytical and behavioral models on the PredictSpring data – and combine the PredictSpring data with data sets from other systems in Snowflake for more advanced, cross-dimensional analysis.
You can build your own analytical models on the PredictSpring 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 PredictSpring running in Snowflake, with ready access to the model source code in DBT.
Host the Modeled PredictSpring 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 PredictSpring in your Snowflake models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of PredictSpring data out of Snowflake – so you can start making better decisions and taking better action as soon as you’ve connected PredictSpring to Snowflake.
Host the Modeled PredictSpring Data in Snowflake for Campaign and Customer Activation
Whether your marketing team uses PredictSpring 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 PredictSpring data in Snowflake to your most important marketing applications.
If you’ve followed the steps above to properly onboard and model your PredictSpring 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 PredictSpring data in Snowflake to use!
Getting Your PredictSpring Data Defined, Labeled, Mapped and Modeled in Snowflake is Easy!
SoundCommerce can automate the steps necessary to bring PredictSpring data into Snowflake, addressing the key functions of raw PredictSpring data logging, PredictSpring semantic definitions and mappings, and pre-built PredictSpring data models that are analytics- and activation ready in Snowflake.
Contact us today to get started with PredictSpring in Snowflake!
Technical Resources for Integrating
PredictSpring Data with Snowflake
More information and technical specifications for data collection from PredictSpring is available at:
PredictSpring API Documentation
More information and technical specifications for data ingest into Snowflake is available at: