The data lakehouse has revolutionized how organizations store and manage massive datasets. Still, a critical gap has emerged: How do you serve that data directly to customer-facing applications without building complex, bloated architectures? StarTree’s new Apache Iceberg integration aims to solve this challenge by collapsing the traditional query and serving layers into a single, high-performance solution.

The Architecture Bloat Problem

Today’s data lakehouse implementations excel at internal analytics; feeding executive dashboards and supporting data scientist workflows. However, when organizations want to create customer-facing data products, they typically resort to a complex, multistep process: Extracting data from the lakehouse, transforming it into insights, staging it in intermediate systems, and finally loading it into specialized serving layers, such as Redis or other key-value stores.

“This introduces latency, complexity and what we call ‘bloat,‘” explains Chad Meley, SVP at StarTree. “We’re collapsing that serving and query layer into one piece of the puzzle, significantly reducing the bloat and simplifying that architecture.”

This bloated approach creates several pain points for DevOps teams: Increased operational complexity, difficult bootstrap and backfill processes, inflexible schema evolution and higher costs due to data duplication across multiple systems.

A Different Approach to Iceberg Integration

StarTree’s solution differs fundamentally from traditional query engines like Presto or Trino. While those engines rely on lazy loading and scanning approaches, StarTree leverages its Apache Pinot heritage to provide real-time indexing and caching capabilities directly on Iceberg data.

“We’re leveraging all the unique things about Apache Pinot and applying it to Iceberg,” notes Chinmay Soman, Head of Product at StarTree. “We have various kinds of indexes: Numerical, JSON, geospatial. Now you can build that directly on top of data sitting in Iceberg, which is very powerful.”

The performance implications are significant. For queries that locally cached indexes can serve, organizations can achieve sub-second latency even when dealing with petabyte-scale datasets. When queries require scanning data from S3, performance depends on the scan bandwidth; however, StarTree optimizes for the most common use case: Pre-canned aggregations and OLAP-style queries that power customer-facing applications.

Simplifying Migration for DevOps Teams

For DevOps teams managing existing reverse ETL pipelines, the migration path to StarTree’s direct Iceberg serving is surprisingly straightforward. “Migration is a lot simpler because we have been displacing these old architectures,” Soman explains. “With Pinot, we don’t have to pre-calculate all the insights; those insights are generated on the fly.”

The migration process essentially involves pointing to the data source and directing queries to Pinot, though teams need to account for SQL language differences. More importantly, the long-term velocity improvements are substantial. Traditional reverse ETL processes require weeks to implement changes, including testing, backfilling data and regenerating insights. Schema evolution becomes exponentially difficult as organizations scale from one to ten or more data products.

“In the long term, the velocity is higher because with reverse ETL, making any change will take weeks,” Soman notes. “Those problems become simpler with this approach.”

The Rise of Agent-Facing Applications

Beyond traditional customer-facing applications, StarTree is positioning itself for the emerging world of AI agents. While still early, the company is already seeing customers tap into AI budgets to expose data to “swarms of agents” rather than individual users.

“The UX is now changing,” Soman observes. “In the beginning, user-facing was creating well-defined views for users. Now we’re going to the next step where every user can customize their experience based on whatever they deem to be important, and agents are bridging that gap.”

This shift creates new challenges for infrastructure teams. Agent workloads exhibit similar concurrency and latency requirements as traditional customer-facing applications; potentially thousands of queries per second with sub-second response times. However, SQL optimization becomes more complex since there’s no human in the loop to fine-tune queries.

Production Considerations and Performance Tuning

While StarTree’s Iceberg support is launching in private preview, early implementations have revealed key operational considerations. Performance is highly tunable; however, DevOps teams must make strategic decisions about data locality and indexing.

“How much data do you want to pin locally? What kind of indexes do you want to pin locally? How do you want to establish query SLAs for different kinds of queries?” These are the critical questions teams must answer upfront, according to Soman.

The company recommends starting with minimal local pinning and optimizing from there. If teams pin all indexes locally, they’ll achieve maximum performance but at higher compute costs. The key is finding the right balance between performance requirements and resource consumption for specific use cases.

Industry Momentum and Strategic Implications

StarTree’s Iceberg integration aligns with broader industry trends. Apache Iceberg adoption has surged by over 60% year-over-year, according to theCUBE Research, as organizations embrace open table formats for their data lakehouse implementations.

“We’re hearing it from our customers; they’re all either embracing Iceberg or have plans to,” Meley explains. “We’re aligning with one of the bigger trends in our industry right now.”

For DevOps teams, this convergence of real-time analytics capabilities with open table formats represents an opportunity to simplify their data architecture while enabling new classes of applications. Instead of maintaining separate systems for storage, transformation, and serving, organizations can leverage their existing Iceberg investments to power customer-facing applications directly.

The implications extend beyond cost savings and operational simplification. By eliminating data movement and reducing pipeline complexity, teams can iterate more quickly on data products and respond more effectively to changing business requirements; a crucial advantage in today’s competitive landscape.