How it works
AdLibertas functions as your private end-to-end data cloud: we do the heavy lifting of managing your data so you can focus your efforts on more valuable activities.

Step 1. API Data Collection

We have 100+ APIs that are scheduled to batch-fetch your data from all in-app service providers. We don’t require an SDK for integration, and most data can be collected by simply connecting credentials.
Connecting in 3 steps
Knowledge Base

Step 2. Data Pipeline: Processing & Storage

Scheduled Data Import & ETL
Our scheduled, overlapping data import schedules ensures the latest data available is accessible while back-fetching ensures any later-posted data is added to keep data access timely and correct.
We’ve pre-built the ETL & processing algorithms so there’s no need for you to wrestle with scripting your own.
Fully managed data lake
For every AdLibertas customer, we spin up a single-tenant AWS account to house and store your data. We’ve architected your data to be stored as ORC files in S3 buckets. This maximizes long-term cost-efficiency and maintains accessibility and security for all clients. There’s no maintenance or overhead needed.
Building your own Data Pipeline: Best Practices
For app developers that want to architect, build and maintain their own data architecture, AdLibertas Head of Architecture, Allen Eubank, shares our experiences in building a scalable, reliable data pipeline.
Fast, parallel-distributed query processing
For processing the many petabytes of information generated by our clients, we adopted Trino a fast, highly parallel, and distributed query engine that is built from the ground up for efficient, low latency analytics at scale.
Read how and why AdLibertas uses Trino to enable their customers.
The AdLibertas Approach to Big Data - AdLibertas
AdLibertas

Step 3. End-User Reporting & Access

Rich, interactive reporting and custom dashboarding
Self-service no-SQL exploration
Our analytics give all users in your organization the ability to build custom-defined user datasets, defined by user-events, actions, and/or user characteristics. There’s no need for complicated SQL-joins or custom Tableau reports, anyone can simply drag and drop user-audiences. This gives your organization complete control and flexibility in refining their data-sets to find the important users and actions in your app.
Our Customers - AdLibertas
AdLibertas
Interactive, custom reporting & prediction models
Your organization can compare audience datasets across performance metrics and custom events. See the article on how predicted LTVs work here.
Direct SQL Access & Custom Processing
For customers who want direct access to the data, we offer direct SQL access via Amazon Athena, or end-points to access and download reports. Additionally, customers can spin up their own computing clusters to run advanced or custom models against their users or custom datasets.