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.

more details about the platform

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 Baseadlibertas.gitbook.io

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 - AdLibertasAdLibertas

Step 3. End-User Reporting & Access

Rich, interactive reporting and custom dashboarding

Dashboard and reporting

BI & Analytics

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 - AdLibertasAdLibertas

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 and see how our proprietary machine learning predictions can help you get fast, accurate campaign predictions 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.

Reporting Workflow

User-level Audience Reporting allows you to create hyper-specific reports on user-level characteristics and behavior. From here, we store these reports in the analytics system -- accessible via the "Explore Data button"-- so you can add them to a custom dashboard or compare them against other user-level reports.

Consolidated Revenue Reporting allows you to view your earnings in pre-built charts or drill directly into analytics to create your own reports.