Data infrastructure at adtech and gaming scale
Adtech and gaming data volumes break generic pipelines. We build for that scale: a header-bidding system we designed processes over 400 million HTTP events and more than 1TB of data daily, with ECS autoscaling tracking traffic and Glue plus Spark doing near-realtime transformation into a Redshift warehouse. For another client we run a custom data lake that ingests 3TB of ad-server data every day.
For a top-tier global games publisher we operate the event ingestion platform behind their analytics organisation: a Kafka firehose feeding Databricks, serving a downstream community of around 400 analysts and data scientists. This is the same advertising technology depth we bring to every data project, whether you are an SSP, a DSP, or a publisher.
How we reduce data platform costs
Cost work is architectural, not incidental. One client was quoted $500,000 per month by their third-party data provider. We replaced that service with a custom data lake on AWS running at $10,000 per month, a 98% cut, while processing 3TB daily. The build used MSK with MirrorMaker for streaming, autoscaling ECS consumers, PySpark aggregation, S3 with Parquet for storage, and Athena with tuned partitioning.
For the games publisher we run what we call continuous migration: replacing each component of an aged platform with current technology while production keeps serving traffic. Retiring an over-scaled Apache Flink job and a legacy Spark job, consolidating both into a single Databricks Lakeflow declarative pipeline, and moving self-hosted Kafka off EC2 and EBS onto pay-as-you-go MSK Express generated roughly $3 million in savings in one year. Every migration ships with a detailed cost comparison, so leadership sees savings that exceed our fees before we start.
We apply the same modeling to warehouses: query patterns decide between Redshift and Athena, Glue managed jobs versus self-run Spark, and S3 partition layouts that keep scans cheap, with Glacier for cold history.
Ingestion at terabytes per hour
We know how to scale the unglamorous front of the pipeline: log collectors like Vector, Flume and Fluentd, Kafka and MSK clusters with MirrorMaker replication, retention tuning for high-volume streams, and consumer fleets that scale with traffic instead of running at peak capacity around the clock.
The games publisher's firehose runs at 12 to 20 millisecond Kafka latency in normal operation. When a flagship title doubled its traffic this year, absorbing it meant scaling the entry-point collectors only; before our re-architecture the same request took roughly a month of capacity planning. The same discipline applies to API-driven ingestion: for upscale.ai we scaled an event-driven GCP pipeline pulling Shopify and Facebook data into BigQuery from 18,000 to over 2 million records by moving from Cloud Functions to Cloud Run. If your pipeline needs deep instrumentation, our telemetry and observability practice covers that layer.
Schema governance on Databricks and Unity Catalog
Raw data is only useful if hundreds of teams can evolve their event schemas without breaking each other. On the games publisher's platform we built the governance layer: a schema registry service fronting Kafka, and a Delta DDL integrator that diffs schema versions and applies table updates to Databricks ahead of the data, so tables are always ready when new event types arrive. Unity Catalog handles metadata and permissions across the lakehouse.
We are driving the platform from JSON Schema to Protobuf, with CI/CD linting that enforces required metadata such as data origin, application location and schema versioning, and validation tooling written in Go. Teams self-serve schema changes safely instead of filing tickets.
Reporting dashboards in Looker Studio, Tableau and QuickSight
A pipeline nobody can query is a cost center. We build the reporting layer on top: dashboards in Google Looker Studio, Tableau and Amazon QuickSight, fed by Redshift, Athena or Databricks, with REST APIs where BI tools need programmatic access.
Arctus can run as the reporting component of your stack or underneath a custom agent we build for you. For agencies and brands automating operations end to end, see AI agents for agencies and brands.
ML on top of the pipeline
For an SSP processing millions of ads daily we combined simple statistical models with DeepAR neural networks into a forecasting engine that predicts campaign spend from historical data, so AdOps can schedule a growing number of campaigns without under-delivery risk.
For a creative analytics client we built an insights engine that links campaign metrics such as video completion and purchases to creative attributes like brand presence and emotional tone, using Lambda, Glue and PySpark over S3, with Athena for ad-hoc queries. The output powers dashboards that tell advertisers which creative elements actually drive ROI.
AWS Advanced Tier and Databricks partner consulting
Appliscale is an AWS Advanced Tier Services Partner and a Databricks partner, with Databricks Lakeflow declarative pipelines and Unity Catalog running in production at one of the world's largest game publishers. We choose the engine that fits your workload and budget rather than the one we are incentivised to sell.
Talk to an engineer
