How to engineer a privacy-first, first-party data infrastructure for AdTech

As privacy regulations, browser restrictions, and platform policies reshape the AdTech landscape, developers face a new mandate: build data systems that work without relying on third-party tracking. Developers building AdTech systems today must adapt to a new reality: the era of abundant, easy-to-access behavioral data is over. What’s left is first-party data that is collected directly, consented to explicitly, and controlled fully.

To build a high-performance, future-proof AdTech platform, developers need to rethink how data gets collected, processed, and activated. This guide walks through how to design and implement a first-party data system that performs at scale while meeting modern privacy requirements.

First-party data: What it is and why it matters

First-party data refers to information collected directly from users who engage with your digital properties. This includes websites, apps, emails, and even in-store systems if you control those environments. If you orchestrate the collection, store the data yourself, and maintain control over how it's used, then it qualifies as first-party.

You can use third-party tools like analytics SDKs or pixels to help collect data, as long as the data ultimately flows into your infrastructure and remains under your control. However, if you deploy a Facebook pixel and send user events directly to Meta, that data doesn’t qualify. But if you manage your own attribution tool using a tracking pixel hosted on your domain and process the data on your servers, it does.

The importance of first-party data has grown because traditional sources of user signals are disappearing. Browsers are blocking third-party cookies, mobile platforms are restricting tracking, and regulators are enforcing opt-in consent. As a result, AdTech platforms are losing visibility into user behavior, resulting in signal loss. First-party data restores that visibility, but it only works if you build the systems to support it.

Sources and types of first-party data

First-party data originates from direct interactions. When a user browses your site, opens your app, fills out a form, or makes a purchase, they generate data you can capture and use (assuming they’ve consented to it). This data takes many forms. It might be behavioral, like a page view or a product click. It might be declarative, like a form submission or preference selection. Some of it arrives through automated systems, like event tracking/streaming or server logs. Other data may come through manual methods, such as importing CSVs or survey results.

Most developers think of first-party data as tied to individual users, but that’s only part of the picture. It can also include service-level or platform-level data, such as ad servers logging impression data or measurement systems tracking creative performance across campaigns. If your platform collects and controls that data, it qualifies as first-party, even if it's not directly about a user.

Common challenges in first-party data collection

Once developers begin working with real-world datasets, issues appear quickly. The first is volume. A publisher or commerce platform with high traffic can generate billions of events per day. That kind of throughput breaks traditional batch ETL processes. Systems must be designed to handle large volumes of streaming data in real time without bottlenecks.

Next comes velocity. Data arrives simultaneously from websites, mobile apps, over-the-top (OTT) platforms, and offline systems. If your pipeline can't ingest and process that information with low latency, you’ll lose valuable signals or miss opportunities to act on them.

Quality is another challenge. Raw data is messy. Events arrive out of order, contain missing fields, or use inconsistent schemas. Developers need to build systems that clean and enrich data as it flows through the stack, not after the fact.

Then there’s identity. Linking activity across sessions, devices, and platforms without third-party cookies or mobile IDs is harder than ever. Solving identity challenges requires controlled login systems, hashed identifiers, and sometimes integration with privacy-safe frameworks like Unified ID 2.0 (UID2), an open-source identity framework designed to replace third-party cookies and mobile ad identifiers for greater privacy and transparency. In addition, clean rooms can be used to collaborate on data without sharing raw, identifiable user information.

Designing the data infrastructure

Start with consent. You can’t process first-party data without explicit user permission. Build or integrate a consent management platform that handles opt-ins at the right granularity. Store consent status with each user event and use it to control what gets collected, where it goes, and how it’s used.

Move data collection to the server side. Avoid client-side JavaScript that breaks under ad blockers or browser restrictions. Instead, send user events to a backend collector via APIs, beacons, or server-side tag managers. For example, in Node.js, you might expose a lightweight POST endpoint that accepts event payloads, validates consent, and forwards them to a message queue like Kafka or a stream like Kinesis.

Normalize and clean events as they enter the system. Add metadata like timestamps, session IDs, or device type. Deduplicate noisy events and enrich identifiers where possible, for example, by linking anonymous sessions to logged-in users. If you’re resolving identities, do it using hashed emails or proprietary graph models that don’t expose raw user data.

Store data in a format that reflects how you’ll use it. For real-time decisioning, write to low-latency databases like Aerospike. For reporting or analytics, batch events into Parquet files and load them into the data lakehouse of your choice. Aerospike is an ideal choice for the upstream data layer, offering high-performance ingestion and low-latency access to real-time data. Its support for strong consistency and large-scale parallel processing makes it well-suited for feeding analytical pipelines efficiently.

If your system supports personalization, consider storing relationships in a graph. Aerospike can also serve as a low-latency, high-throughput store for real-time user profile data, enabling personalization engines to make decisions with up-to-date context.

Engineering for scale and performance

The only way to handle billions of events with low latency is to design for horizontal scalability. Use containers or serverless compute to adjust for traffic spikes automatically. Build stateless services that can spin up quickly and shut down cleanly. Push everything through message queues to decouple ingestion from processing.

Avoid monolithic data models that try to solve everything with one structure. AdTech workloads like identity resolution, real-time personalization, and bidding optimization each demand different storage behaviors. While column stores and graph databases have their place, Aerospike offers a more efficient alternative for many of these use cases. Its key-value model with strong consistency can support complex identity graphs, user profiles, and segmentation logic, all in a single high-performance system. With built-in support for secondary indexes and user-defined functions, Aerospike handles varied query patterns without requiring external systems or batch pipelines.

Real-time decisions can’t wait on slow storage layers. If your platform calculates frequency caps, checks audience inclusion, or updates optimization scores in the critical path of an ad request, sub-millisecond latency is essential. Aerospike is built for this. It keeps data close to the compute layer, handles high concurrency without sacrificing speed, and eliminates the need for runtime joins or transformations. This lets developers push real-time logic into the data layer and reduce infrastructure complexity where it matters most.

Privacy, compliance, and control

AdTech systems must enforce privacy at the infrastructure level, not just in the UI. With Aerospike, developers can implement data controls directly within the data layer, enabling real-time enforcement of consent flags, geographic rules, and regulatory policies. Its strong consistency model ensures that once a user revokes consent or requests deletion, all associated records can be reliably and immediately removed from every node across the cluster.

Aerospike’s secondary indexes and rich query capabilities make it easy to flag, filter, or route data based on consent status, user region, or privacy classification, all without bolting on external filtering layers. You can tag and manage user state in real time and apply business logic at the point of read or write. For audit readiness, Aerospike supports time-stamped metadata and precise control over retention, allowing platforms to prove compliance without expensive batch processing or rehydration jobs. Instead of building slow, brittle privacy workflows on top of your system, Aerospike lets you build them into the system.

Future-proof the platform

The shift to first-party data is not a temporary workaround or a stopgap. It is the new architecture for digital advertising. Developers should anticipate more restrictions in the future, not fewer. Clean rooms, privacy APIs, and cohort-based targeting are here to stay. Chrome’s Privacy Sandbox, Apple's App Tracking Transparency (ATT), and increasing consumer awareness are only accelerating the need for compliant, controllable systems.

Aerospike is built for this environment. Its low-latency, high-throughput architecture gives developers the speed needed for real-time activation. At the same time, its strong consistency and fine-grained access controls make it ideal for handling regulated data. As opaque identity systems and third-party data sources collapse under compliance pressure, Aerospike enables platforms to scale first-party data strategies with precision by supporting user consent, clean room integrations, and privacy-safe audience modeling all from a single, performant data layer. In a world defined by signal loss, Aerospike ensures the data you do own remains fast, compliant, and actionable.

Building a competitive edge with first-party data

Modern AdTech depends on infrastructure that can collect, process, and activate first-party data with speed, accuracy, and privacy all built in. Developers play a central role in making that possible.

You’ll need systems that scale under real-world load, models that reflect real-world complexity, and pipelines that deliver data where it’s needed—fast. You’ll also need to enforce compliance automatically, not retroactively. Aerospike thrives in this environment. Its architecture supports sub-millisecond reads and writes at petabyte scale, with predictable performance even under peak traffic. Whether you're managing dynamic user profiles, real-time decisioning, or privacy-tagged events, Aerospike provides the consistency, speed, and operational control required to power modern, privacy-first AdTech platforms. It integrates cleanly into streaming data pipelines and supports deterministic behavior, enabling your systems to respond instantly and compliantly, without added complexity.

Start building now. The data you collect today is clean, consented, and under your control. It is building your competitive edge for tomorrow.