Make the Most of Your Manhattan Data in Snowflake
Manhattan 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.
Manhattan is an omnichannel order management system (OMS) developed by Manhattan Associates. It provides retailers with a centralized platform to manage and fulfill customer orders seamlessly across various channels, improving inventory visibility and order orchestration. Manhattan Active Omni is designed to enhance the overall customer experience and optimize order fulfillment processes for modern retail environments. 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 Manhattan
The first step toward useful, modeled Manhattan 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 Manhattan 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 Manhattan data modeling, analytics and activation easier once the data is landed in Snowflake.
To connect to Manhattan, 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 “Manhattan” within the data source library.
Complete the “Connection Setup” form with your credentials and token to securely connect to Manhattan and begin collecting source data.
Log Manhattan Data for Flexible Modeling in Snowflake
There are a few more considerations to address along the way. First, what happens if Manhattan is unavailable for some reason, or the data you’re expecting has been purged by Manhattan? What happens when Manhattan changes their API schemas and data scope? What happens if you need to reinterpret your Manhattan data for a new use case in the future?
You’ll want your Manhattan data immutably logged locally, just upstream of Snowflake to ensure you have the data and data flow flexibility you need to future-proof your Manhattan data and models. SoundCommerce provides permanent logging of Manhattan data upstream of Snowflake to ensure failover and future-proofing. Regardless of how you connect your Manhattan 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 Manhattan 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 Manhattan to Snowflake leaves all the work for your data team in Snowflake.
As you onboard your Manhattan data into Snowflake, you’ll want to create semantic labels and metadata that describe the Manhattan data for easier unification and modeling across other systems and data in Snowflake.
There are third-party solutions that will catalog your Manhattan data and generate semantic labels and mappings after you’ve landed it in Snowflake. With SoundCommerce, the Manhattan 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 Manhattan data in Snowflake.
Map Manhattan Entities to Snowflake
Once the raw Manhattan data has been organized into useful entities, it’s time to map the Manhattan data into useful tables in Snowflake.
Why do defined and labeled entities from Manhattan matter so much? The main reason is that Manhattan data needs to be combined with data from other SaaS and on-premise software systems in useful ways. Landing raw Manhattan 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 Manhattan data and the standardization of the meaning of that Manhattan data from scratch in Snowflake.
Defining, labeling and mapping the Manhattan data on the way in means much less effort once the data is landed in Snowflake.
Materialize Manhattan 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 Manhattan flowing securely to Snowflake.
Model Manhattan Data in Snowflake
Once you have well-formed entities from Manhattan onboarded to Snowflake, it’s time to build useful analytical and behavioral models on the Manhattan data – and combine the Manhattan data with data sets from other systems in Snowflake for more advanced, cross-dimensional analysis.
You can build your own analytical models on the Manhattan 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 Manhattan running in Snowflake, with ready access to the model source code in DBT.
Host the Modeled Manhattan 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 Manhattan in your Snowflake models. SoundCommerce provides pre-built embedded reports in Sigma to reduce the time, cost and risk of BI reporting of Manhattan data out of Snowflake – so you can start making better decisions and taking better action as soon as you’ve connected Manhattan to Snowflake.
Host the Modeled Manhattan Data in Snowflake for Campaign and Customer Activation
Whether your marketing team uses Manhattan 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 Manhattan data in Snowflake to your most important marketing applications.
If you’ve followed the steps above to properly onboard and model your Manhattan 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 Manhattan data in Snowflake to use!
Getting Your Manhattan Data Defined, Labeled, Mapped and Modeled in Snowflake is Easy!
SoundCommerce can automate the steps necessary to bring Manhattan data into Snowflake, addressing the key functions of raw Manhattan data logging, Manhattan semantic definitions and mappings, and pre-built Manhattan data models that are analytics- and activation ready in Snowflake.
Contact us today to get started with Manhattan in Snowflake!