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The Challenge: Turning Massive Data Volumes into Real-Time Intelligence
Rise operates at the intersection of massive programmatic volume and real-time decision-making.
As a data-driven programmatic technology company sitting on the supply side, Rise processes trillions of bid requests every month, using machine learning, advanced modeling, and real-time signals to understand traffic, identify value, and make more precise decisions about which opportunities reach buyers to maximize outcomes.
Every bid request and auction event generates data that feeds Rise's optimization algorithms, ML models, analytics, and partner reporting. Rise’s infrastructure runs on Google Cloud, and BigQuery is the engine Rise uses to query its data.
But generating data is only part of the equation. Its value depends on how quickly it becomes available to inform the decisions that follow.
As Rise expanded into new markets, channels, and partnerships, its traffic volumes grew significantly (240% YoY), creating three critical infrastructure priorities:
- Data freshness: Making new event data available faster so models and optimization systems can learn from recent activity.
- Cost efficiency: Optimizing the cost of ingesting, processing, and storing growing volumes of data.
- Operational scalability: Reducing the complexity of a pipeline built across multiple services, each requiring resources, monitoring, and maintenance.
Rise’s previous ingestion pipeline combined several separate services and split raw event data across dozens of per-organization tables. Ingestion and storage costs were high, the pipeline required ongoing attention from Rise’s data engineering team, and it had limited headroom for further growth.
Rise needed a data architecture that could keep pace with its growth without compromising speed, accuracy, or efficiency.
The Solution: Combining Eon’s Data Engine with Google Cloud’s Storage and Analytics
Rise partnered with Eon to modernize its data infrastructure using Eon's data engine, running natively on Google Cloud.
Eon continuously ingests and processes Rise's massive event streams, handling filtering, sorting, deduplication, aggregation, and compaction in-stream while organizing the results into optimized Apache Iceberg tables on Google Cloud Storage. This significantly improves storage efficiency and reduces the need for separate data processing pipelines, streaming services, and ongoing maintenance.
By automatically handling ingestion, processing, and continuous optimization, Eon makes fresh, analytics-ready data available to BigQuery through BigLake, while reducing infrastructure costs and operational complexity.
Beyond analytics, the architecture enables Rise to train more accurate machine learning models on fresher data at massive scale and serve those models in production to support real-time ad optimization and decision-making.
Together, Eon's data engine and Google Cloud's scalable storage and analytics infrastructure help Rise turn massive volumes of event data into real-time intelligence, with greater efficiency and significantly less engineering effort.
How the architecture works
This design delivers three important advantages:
- More efficient storage: Apache Iceberg on Google Cloud Storage makes retaining large volumes of raw data more cost-efficient, while keeping that data fully accessible to BigQuery.
- Ultra-Fresh, Cost-Efficient Analytics: BigQuery remains Rise's engine of choice, leveraging pioneer real-time incremental reads directly on active Apache Iceberg streams to instantly turn raw event data into aggregated, usable data with zero complex copy operations or costly full-table rescans.
- Simpler operations: Eon's Data Engine reduces processing dependencies and creates a more direct path from incoming events to data that is queryable in BigQuery.
The solution runs within Rise’s own Google Cloud environment, combining the flexibility of an open data format with the infrastructure control required at its scale.
The Results: Fresher Data, Greater Stability, and More Room to Scale
50% Improvement in Data Freshness
Faster availability of event data in BigQuery for analytics, machine learning, and optimization, enabling Rise to work with more recent signals.
50% Improvement in Cost Efficiency
Optimized costs across data ingestion, storage, and processing, improving the economics of operating at massive scale.
10× Traffic Headroom
Expanded ingestion capacity with Eon, and Google Cloud's storage and analytics scale, support Rise's growth across new markets, partners, and programmatic channels.
99.9% Data Freshness SLA
Reliable and consistent data availability, including during peak traffic, supporting the stability of Rise's data operations.
Why It Matters
For Rise, data freshness and reliability directly influence the quality of its real-time intelligence. Faster access to event data in BigQuery allows machine learning models and optimization systems to incorporate more recent signals, while consistent data availability supports dependable analytical operations even during peak traffic.
Combined with significantly greater ingestion capacity and more efficient infrastructure economics, the new architecture gives Rise a stronger foundation for expanding its programmatic operations without compromising the speed and reliability of its data.
“We’re always looking for new technology that can move us forward. Data infrastructure is a growth decision, not just a technical one. Fresher data and more efficient economics mean we can scale our traffic and expand into new markets with confidence. Eon is exactly the kind of partner we look for: a deeply technical team that listens to the real challenge and builds the right solution around it. Together with Google Cloud, it gives us the foundation to keep leading.”
Chen Shalit, CEO, Rise
An Engineering Partnership, Not Just a Technology Integration
One of the defining aspects of the project was how the solution came together.
Rise needed a production-ready data platform capable of meeting the demanding scale, performance, and reliability requirements of its programmatic operations.
Rise and Eon worked closely to deploy and optimize Eon's data platform for the scale and performance requirements of Rise's programmatic business.
Before going to production, the teams ran Eon's pipeline alongside Rise's existing infrastructure, processing the same production data in parallel. They validated the results in BigQuery, including Rise's most critical production queries, and confirmed that both pipelines produced identical results.
This gave Rise confidence that it could achieve significant improvements in data freshness, cost efficiency, and scalability without compromising data accuracy or disrupting existing analytical workloads.
The collaboration extended beyond the pipeline itself, including adjustments to Eon's platform interface and work on integration with Rise's monitoring environment.
This engineering-led approach established a foundation for further collaboration, including intelligent sampling capabilities that can reduce repetitive event data before it lands in Google Cloud Storage.
“We built Eon around a real pain we kept seeing across data-heavy companies: the infrastructure that should support growth ends up limiting it. Rise operates at a scale that puts that challenge front and center. Eon's Data Engine is designed to handle massive data volumes with greater efficiency and less operational complexity, giving companies like Rise the scalable data foundation they need for the AI era.”
Ofir Ehrlich, CEO, Eon
Alongside Eon, the project deepened Rise’s collaboration with Google Cloud. Rise built the new architecture on Google Cloud Storage and BigQuery, creating a more efficient foundation to scale its traffic and grow.
“Our focus is on helping customers keep growing, and that starts with understanding the real challenges they face. Rise’s data operations are a great example. By working closely with Rise and EON —leveraging their ability to turn backup data into strategic assets,— we found the right architecture for Rise’s needs, one that gives Rise a strong foundation for its next stage of growth.”
Boaz Maoz, Managing Director, Google Cloud Israel
Three Partners, One Architecture
- Rise brings the scale: trillions of bid requests a month, and the real-time intelligence built on top of them.
- Eon brings the real-time data engine: Continuously ingesting, processing, and optimizing massive event streams into fresh, analytics-ready data, while reducing cost and operational complexity.
- Google Cloud provides the scalable data infrastructure: Google Cloud Storage and Apache Iceberg for open, cost-efficient data storage, and BigQuery for the analytics and machine learning workflows powering Rise's platform.
Built to Support the Intelligence Behind Every Decision
As Rise grows across markets, partners, and programmatic environments, its ability to turn data into actionable intelligence becomes increasingly important.
By combining Eon's real-time data ingestion and optimization engine with Google Cloud's scalable storage and analytics infrastructure, Rise has built a data foundation designed to keep pace with its rapidly growing programmatic business.
Fresher data enables Rise's optimization systems to work with more recent signals. More efficient processing improves the economics of handling massive event volumes. And significantly greater ingestion capacity creates room for expansion into new markets, partners, and channels, without requiring a proportional increase in infrastructure complexity or cost.
With the help of Eon and Google Cloud, Rise has created an architecture that turns large-scale data processing from a growing operational challenge into an enabler of business growth.
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