Every company gets data from their customers from a constantly growing and evolving collection of sources. Ecommerce, loyalty, point-of-sale, web visits, marketing response data, etc. That data is complex and stored in many different formats, which creates a massive burden for data engineering teams.
Amperity is the world's first dedicated platform for turning that raw data into the best possible unified data model, commonly referred to as a Customer 360, and making that data available anywhere your teams need it.
Gone are the days of developing, maintaining and managing hundreds of custom data modeling jobs, collections of tools, workflows, dev environments, etc.
The Amperity platform plugs into your Snowflake via secure data sharing and data engineers can turn raw data into unified data with low-code and zero copies thanks to Snowflake Iceberg Tables.
In this quickstart we will walk through the basic steps to turn raw data into a world class Customer 360 collection of tables in hours instead of months.
Your Amperity account connects to Snowflake via secure data sharing powered by an Amperity platform feature called Bridge. This allows for a zero copy architecture and lets you use data in your Snowflake with the easy UI and data management platform you get from Amperity.
You are ready to go. Time to set up your tags.
Amperity does have a schema or require you to do a bunch of data modeling to create your Customer 360.
Instead you apply tags to the raw data which informs the Amperity algorithms. Stitch uses these tags to create standardized tables as well as run identity resolution to generate your ID graph.
For each table you want to include, you will be verifying and adding tags to each field.
Amperity has a constantly growing library of tags for common data models:
For each type of data, identify the tables that contain data relevant to the models you want to see in your Customer 360.
Log into Amperity and navigate to the Sources tab. For each table, do the following:
Now you should be ready to kick off Stitch and let it do the heavy lifting!
Amperity's Stitch is the world's first AI identity resolution algorithm. Historically data teams have been stuck using simple, rules-based algorithms that are complex to configure and result in inaccurate identity.
Amperity uses up to 45 different machine learning algorithms on your Snowflake data to produce the most transparent and accurate identity graph available.
Stitch also does several other things:
Configuring Stitch is as simple as selecting which tables you want to generate identity data for.
That's it!
Stitch will access your raw data via Snowflake secure data sharing and generate a large library of unified tables, ready for use.
Now that you have run Stitch, your Amperity tenant will have a whole new list of domain tables. Most of these are named with the prefix "Unified" and correspond to the library of tags that were used.
You are now ready to start using your data.
Here are a list of things you could go do:
Stitch outputs a large library of standardized tables. Commonly the next goal is to take that data and model it into a Customer 360.
A Customer 360 is a collection of tables that are cleaned up, merged, and represent the best possible profile view of each customer, as well as all of their behaviors and interactions with your brand.
Amperity has been building Customer 360 models for the world's biggest brands for a decade and has a constantly growing library of out-of-the-box models that cover a wide variety of use cases and have time tested, proven models. You shouldn't have to design or start from scratch.
To generate these models do the following:
This will use the available data to generate as many of the out-of-the-box models as possible.
You can then begin to explore these models or go into the new database and begin adding your own fields or customizing the out-of-the-box rules.
Now that you have built your customer 360, you are ready to start connecting it to your marketing tools or share it back to Snowflake for your data teams to start building workflows on it.