Whether this is your first bake or you’ve already earned a couple michelin stars, this GitHub is the place for you to get started with the Lotame Data Stream suite of products.
Data Stream is Lotame’s big data firehose, supplying billions of consumer profiles to support enterprise-level data modeling and applications, such as content and product recommendations, personalized digital experiences, fraud prevention and risk mitigation efforts, attribution modeling, Customer Relationship Management (CRM) tools, and deeper, more valuable insights into your target consumers around the world.
Technically, it’s a suite of different types of files that we make available to our customers. These files can be used alone or joined with each other to answer myriad use cases of analysis and segmentation.
With Data Stream, customers can gain access to an always-on firehose of:
- Your own first-party data in hourly log files.
- Consumer attribute and behavioral enrichment data from Lotame’s global data marketplaces, Lotame’s Device Graph technology, which connects customer data across devices and platforms, driving engagement anywhere and on any screen.
- A log file of the location down to Zip+4 of devices as they hit our edge services.
- A mapping file of the device identifiers from Lotame to over 20 activation and adtech partners as well your companies user ids.
This firehose can be used to power your individual data science and data needs, including product and content recommendation engines, personalized experiences and messaging, and other proprietary modeling. We supply the data, and you take it from there, working outside the limits of a traditional DMP to fuel even the most advanced data-driven applications.
While the applications for Data Stream are as plentiful and varied as business itself, there are several areas where Data Stream fits naturally. Today, clients are harnessing Data Stream to underpin enterprise-level solutions like content and product recommendation engines, personalized digital experiences, fraud prevention and risk mitigation strategies, customer attribution modeling, and to develop deeper insights into their target consumers
- Easily pull down Lotame Data Stream files
- Easily transform those Lotame Data Stream files into .json and .csv
- Easily port those files into other applications like Athena and Redshift
- Be entertained by our charming data science team
First make sure you have Python installed on your local machine. It will be assumed you are already familiar with how to run Python scripts and how your commandline is set up.
Before you start running any of the examples you find here go to the lib folder in the repo and follow the instructions to add your Lotame credentials to the lotame.properties file and put that file in the root of your home directory, ~/.
Click into the recipes listed in the master branch. The readme files will tell you everything you need to know to start working with our prospect facing files. These are 1% versions of the full files.