Solutions

Data Engineering Services for AdTech and Gaming

Appliscale designs, modernizes and operates data platforms for advertising technology companies and game publishers. Our work spans high-volume ingestion, AWS data lakes, Databricks lakehouses, schema governance and analytics.

Talk to an engineer
See the proof →

AWS Advanced Tier · Databricks partner · Production lakehouses

Last updated

Isometric illustration of a data refinery: a conveyor belt carries documents into a reader, which feeds a central pool wired out to three database cylinders.

The engines behind these platforms

  • AWS
  • Databricks
  • Kafka
  • Spark
  • Google Cloud
  • Python
  • Kubernetes
saved in one year of modernization
$3M
HTTP events ingested every day
400M+
data lake cost reduction
98%

Built for the unglamorous front of the pipeline

Data infrastructure at adtech and gaming scale.

For Yieldlove, Appliscale built a pipeline processing more than 400 million HTTP events daily. For JustPremium, we built an AWS data lake processing 3TB per day at a monthly operating cost of $10,000—98% below the incumbent provider's $500,000 monthly quote. Amazon ECS autoscaling adjusts dynamically to incoming traffic, while AWS Glue and Spark handle near-realtime transformation into an Amazon Redshift warehouse.

For a global games publisher, Appliscale operates the event ingestion platform behind its analytics organisation: a Kafka firehose feeding Databricks and serving a downstream community of around 400 analysts and data scientists. The same advertising technology depth applies whether the customer is an SSP, a DSP or a publisher.

Data plane, stage by stage

Composite across client projects

  • Ingest

    400M+ events/day

  • Stream

    Kafka · MSK Express

  • Transform

    Lakeflow · Spark · Python

  • Serve

    Databricks · Redshift · Athena

  • 12–20 ms Kafka latency
  • Around 400 analysts and data scientists
  • More than 1 TB transformed every day

Modernize while production keeps serving

Continuous migration, not another big-bang rewrite.

On the global games publisher platform modernization, the platform stays live while each expensive or fragile component is replaced, validated and handed back in a form the wider engineering organisation already knows how to operate.

When Appliscale joined, the data platform had grown separately from the company's standardized application stack. Helper services ran on a bespoke Kubernetes environment, critical streaming infrastructure required manual capacity work, and long-lived processing jobs were carrying hidden cost.

Appliscale first moved the supporting applications onto a centrally managed Kubernetes abstraction with consistent deployment, resource controls and audit trails. We then replaced an over-scaled Flink job and a legacy Spark job with one Databricks Lakeflow declarative pipeline, and migrated self-hosted Kafka on EC2 and EBS to managed MSK Express.

Databricks remained the stable platform while almost every surrounding component changed. Detailed equivalence testing, validation and staged cutovers kept the event contract intact throughout the migration. The result was roughly $3 million in savings in one year, with a simpler operational surface.

Platform modernization sequence

Production remained live

  1. 01

    Application platform

    Bespoke Kubernetes operations

    Standard deployment abstraction

  2. 02

    Event processing

    Over-scaled Flink + legacy Spark

    Lakeflow declarative pipeline

  3. 03

    Streaming backbone

    Kafka on EC2 + manual EBS scaling

    Managed MSK Express

  4. 04

    Schema contracts

    JSON Schema-only workflow

    Protobuf with compatibility bridge

Ingestion at terabytes per day

Scale the front of the pipeline without running at peak all day.

Appliscale scales log collectors such as Vector, Flume and Fluentd; Kafka and MSK clusters with MirrorMaker replication; retention for high-volume streams; and consumer fleets that follow traffic instead of running at peak capacity around the clock.

Elasticity under live traffic

The global games publisher's firehose runs at 12 to 20 millisecond Kafka latency in normal operation. When a flagship title doubled its traffic, the re-architected platform absorbed the increase by scaling the entry-point collectors only. Before the migration, the same request required roughly a month of capacity planning around self-hosted Kafka and manually sized EBS storage.

API-driven ingestion

The same discipline applies outside streaming. For upscale.ai, Appliscale scaled an event-driven GCP pipeline pulling Shopify and Facebook data into BigQuery from 18,000 to more than 2 million records by moving from Cloud Functions to Cloud Run. Deep instrumentation belongs at this layer, not as an afterthought.

  1. 01

    Collect

    Vector, Flume, Fluentd and API collectors accept events where they originate.

  2. 02

    Stream

    Kafka and MSK Express absorb spikes, retain events and remove manual storage scaling.

  3. 03

    Transform

    Lakeflow and Spark multiplex event types, validate payloads and persist raw governed tables.

  4. 04

    Serve

    Databricks, Redshift, Athena and BigQuery expose the data to analytics and applications.

Operated, not abandoned

Traffic-aware scaling and cost telemetry at every stage.

normal Kafka latency
12–20 ms
flagship traffic absorbed
2×
GCP records after migration
18K → 2M+

Cost is an architecture decision

Appliscale replaces expensive defaults with workload economics.

Cost work is architectural, not incidental. JustPremium was quoted $500,000 per month by its third-party data provider. Appliscale replaced that service with a custom AWS data lake 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 a global games publisher, Appliscale replaced each component of an aged platform while production kept serving traffic. Retiring an over-scaled Apache Flink job and a legacy Spark job, consolidating both into one 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 includes a detailed cost comparison, so leadership can see the expected savings before work begins. Appliscale applies 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.

Every recommendation includes its workload assumptions, operating model and projected cost delta.

Monthly data platform operating cost

Same 3 TB/day workload

Third-party pipeline

$500K / month

Opaque scaling · fixed vendor margin · no infrastructure control

Re-architected for the workload

Client-owned AWS data lake

$10K / month

MSK · ECS · PySpark · S3 · Parquet · Athena

Elastic scaling · tuned partitioning · full data control

Against the vendor quote

Throughput preserved. Ownership restored.

98% lower

Databricks · Unity Catalog · Protobuf

Event schemas evolve without breaking production compatibility.

The platform is moving from JSON Schema to Protobuf without breaking the existing flow. Teams can already author Protobuf definitions while compatibility tooling converts them for the current pipeline. Standardized CI/CD linting enforces metadata such as data origin, application location, schema name and version, while validation tooling written in Go keeps contracts consistent. The migration becomes incremental rather than a flag day.

  1. 01

    Author

    Teams propose versioned Protobuf or JSON Schema contracts with required origin and application metadata.

  2. 02

    Validate

    CI/CD linting and Go validation tooling check standards and compatibility before merge.

  3. 03

    Integrate

    The Delta DDL Integrator diffs schema versions and prepares Databricks tables ahead of new events.

  4. 04

    Govern

    Unity Catalog applies metadata and fine-grained permissions across managed and external tables.

Guardrails that ship with the platform

Control without a ticket queue.

  • Schema registry

    A facade over Kafka makes schema definitions discoverable and versioned.

  • CI/CD linting

    Origin, application location, schema name, timestamp and compatibility are enforced.

  • Fine-grained access

    Unity Catalog keeps metadata and permissions consistent across the lakehouse.

  • Continuous migration

    Protobuf adoption remains compatible with the JSON Schema pipeline during transition.

From warehouse to answer

Reporting that answers questions.

A pipeline nobody can query is a cost center. Appliscale delivers agentic reporting that answers questions against your warehouse in plain language, and builds the fixed views a team needs alongside it: 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 reporting agent

An analyst asks Arctus in plain language to build a monthly report across all channels with week-over-week trends. The agent assembles the report section by section, writes the insight next to each chart, and exposes the underlying JSON query behind any of them for inspection.

Arctus connected to a warehouse: a question in plain language, a report assembled section by section, and the query behind any figure available for inspection.

Reporting layer options

  • Arctus · whitelabel

    Agentic reporting

    Arctus plugs into the existing warehouse and answers questions instead of shipping another fixed chart. It translates natural-language questions into queries, runs them and returns the answer with its underlying numbers. It can operate standalone or underneath a custom agent.

  • Looker Studio · Tableau · QuickSight

    Operational dashboards

    Stable reporting views for recurring operational questions, backed by the warehouse or lakehouse engine that fits the access pattern.

  • Redshift · Athena · Databricks

    Warehouse and API access

    Curated tables and REST APIs keep BI tools, internal applications and downstream automation on the same governed data.

ML on top of the pipeline

Models become useful when the data contract is dependable.

Appliscale builds forecasting and insights systems on the same governed pipelines, so outputs can be traced back to the events, schemas and transformations underneath.

  • DeepAR · statistical models

    Campaign spend forecasting

    For Ströer, an SSP processing millions of ads daily, Appliscale combined statistical models with DeepAR neural networks into a forecasting engine that predicts campaign spend from historical data. AdOps use the forecast to schedule a growing number of campaigns and operate at larger scale.

  • Lambda · Glue · PySpark · Athena

    Creative performance insights

    For QuickFrame by MNTN, Appliscale built an insights engine that links campaign metrics such as video completion and purchases to attributes such as brand presence and emotional tone. Lambda, Glue and PySpark process the data over S3, with Athena supporting ad-hoc queries. The resulting dashboards highlight which creative and campaign elements drive stronger engagement and ROI.

The stack follows the workload

Choose the engine by access pattern, scale and cost.

  • AWS Advanced Tier Services Partner
  • Databricks Partner

Appliscale is an AWS Advanced Tier Services Partner and a Databricks partner, with Databricks Lakeflow declarative pipelines and Unity Catalog running in production at a global games publisher. We choose the engine that fits the workload and budget rather than the one we are incentivised to sell. Cost modeling covers both the underlying cloud compute and platform consumption such as Databricks DBUs, so managed convenience never hides the complete operating bill. Where a pipeline needs deep instrumentation, our telemetry and observability practice covers that layer.

  1. 01

    Ingest and stream

    Burst tolerance, replay and latency decide the collector and broker topology, and how far the front of the pipeline can absorb a spike on its own.

    • Vector
    • Fluentd
    • Flume
    • Kafka
    • Amazon MSK
    • MirrorMaker

  2. 02

    Process

    Workload shape decides managed jobs, autoscaling consumers or declarative pipelines, and which of them is worth its operating bill.

    • Spark
    • PySpark
    • AWS Glue
    • Lakeflow
    • Amazon ECS
    • Cloud Run

  3. 03

    Store and query

    Scan frequency, concurrency and predictability decide the storage layout and whether a warehouse, serverless query or lakehouse SQL answers it.

    • Amazon S3
    • Parquet
    • Athena
    • Redshift
    • Glacier
    • BigQuery

  4. 04

    Govern

    Contracts and permissions stay consistent while hundreds of teams evolve their own event schemas, without a ticket queue in the middle.

    • Databricks
    • Unity Catalog
    • Protobuf
    • JSON Schema
    • Go

Our Work

Proof, not promises.

All case studies →
Custom data lake cut costs by 98%, restored data control

JustPremium - Cost-Effective Data Lake

Custom data lake cut costs by 98%, restored data control

A custom data lake that slashed JustPremium’s data infrastructure costs by 98%, while restoring full control over their data operations

  • Apache Parquet
  • Athena
  • AWS
  • DataDog
Processes 400M+ daily HTTP events and handles 1TB+ data

Yieldlove - Transforming AdTech Data Infrastructure

Processes 400M+ daily HTTP events and handles 1TB+ data

Yieldlove's new system processes over 400 million HTTP events daily through header bidding and handles more than 1TB of data.

Forecasting engine with statistical models and neural networks to optimize campaigns

Ströer - Ad Forecasting

Forecasting engine with statistical models and neural networks to optimize campaigns

A forecasting engine for Ströer using statistical models and neural networks to optimize campaign pacing and revenue growth.

Unlocking Ad Performance Insights

QuickFrame by MNTN - Ad Insights Engine

Unlocking Ad Performance Insights

Delivered a working MVP in the first iteration cycle, enabling early advertiser demos and validation.

Boosted data throughput from 18K to 2M+ records, maintaining scalability

upscale.ai - AI/ML-Driven Marketing Platform

Boosted data throughput from 18K to 2M+ records, maintaining scalability

Improved data throughput, from 18k to over 2M records, while preserving scalability and performance

Frequently asked questions

The questions engineering leaders ask first.

Clear answers before a discovery call.

How do you decide between Redshift and Athena?

Appliscale models your query patterns and data volumes before picking either. Redshift wins when you run frequent, complex analytical queries over multi-terabyte warehouses with predictable load. Athena wins when queries are sporadic and data already sits in S3 as Parquet, because you pay per scan instead of for an always-on cluster. Where governance and fine-grained permissions matter across many teams, we also build on Databricks with Unity Catalog. Many of our builds combine engines.

Can you cut our existing data processing bill?

Usually yes, and sometimes dramatically. For JustPremium, Appliscale replaced a third-party pipeline quoted at $500,000 per month with a custom data lake running at $10,000 per month, a 98% reduction while processing 3TB daily. For a global games publisher our continuous migration programme generated roughly $3 million in savings in one year by retiring an over-scaled Flink job, consolidating pipelines into Databricks, and moving self-hosted Kafka to managed MSK.

What ingestion volumes have you actually handled?

Appliscale built a header-bidding pipeline that processes over 400 million HTTP events and more than 1TB of data daily with near-realtime transformation. A separate data lake handles 3TB of ad-server data per day. For a global games publisher we operate a Kafka event firehose with 12 to 20 millisecond latency that absorbed a doubling of a flagship title's traffic without replanning. On GCP we scaled an ETL pipeline for upscale.ai from 18,000 to over 2 million records.

Which BI tools do you build reporting on?

Appliscale delivers dashboards in Google Looker Studio, Tableau and Amazon QuickSight, backed by Redshift, Athena or Databricks. Where a static dashboard is not enough, we deploy Arctus, our whitelabel agentic reporting product that connects to your warehouse and answers ad-hoc questions in natural language.

What is agentic reporting?

Appliscale replaces a fixed set of charts with an agent that has read access to your warehouse and translates questions like why did fill rate drop in DE last week into queries, runs them, and returns the answer with the underlying numbers. Arctus is our productised version of this. It plugs into your existing warehouse and can run standalone or under your own custom agent.

Got something hard to ship?

Make the data platform an advantage, not a monthly surprise.

Tell us the volume, the bottleneck and the decision your teams cannot make today.

Start a conversation