Why Teams Are Switching from Federated Query Engines to High-Performance Serverless OLAP in 2026
8 min readBY
TL;DR
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Federated query engines like Athena and Trino suffer from S3 API latency, account-level query concurrency and API quotas, and distributed coordinator bottlenecks. This causes them to struggle with the sub-second SLAs required by modern interactive dashboards and AI agents.
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MotherDuck isolates compute through Hypertenancy and moves petabyte-scale metadata into a transactional SQL database with DuckLake. This reduces the coordination overhead that makes many distributed query architectures slow for interactive workloads.
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Replacing legacy federated engines with MotherDuck can deliver significant query speedups, including up to 14x in customer case studies, and predictable per-second billing rather than unpredictable per-TB-scanned costs.
Federated query engines like AWS Athena and Trino defined the last decade of data lake analytics. They enabled SQL queries directly on S3 data without complex ETL pipelines, and they remain useful for ad-hoc exploratory workloads.
The problem arises when teams use them to serve interactive dashboards, embedded analytics, or AI agents that generate many small, bursty queries.
The problem is architectural. These systems depend on remote object storage reads, metadata planning over files, coordinator scheduling, and account-level concurrency controls. That trade-off works for exploratory analytics, but it becomes harder to defend when users expect sub-second responses.
MotherDuck approaches the problem differently. Its serverless scale-up model gives each user or workload isolated compute, while DuckLake moves lakehouse metadata into a SQL database instead of scattering it across object storage files. The result is a simpler path for teams that need fast, predictable OLAP without managing distributed clusters.
Why do federated query engines like Athena and Trino bottleneck on interactive analytics?
The decision to seek a Trino or Athena alternative stems from fundamental architectural constraints. These engines bottleneck on interactive workloads due to three structural limitations.
Stateless Storage Coupling
Federated engines couple tightly with stateless storage. Athena dynamically maps external S3 objects over the network for every query. This incurs significant Time-To-First-Byte latency.
This constant network overhead fundamentally conflicts with the fast, cached interactivity required by modern BI and AI workloads. Multi-join queries and aggregations in Athena execute a fresh remote network read on every execution without acting as a general-purpose cached serving layer. Athena supports optional result reuse, but it depends on strict query matching and configuration.
File-listing and manifest latency
Metadata latency creates a severe bottleneck. This appears as either Hive LIST API overhead or the GET request latency inherent in modern table formats like Iceberg and Delta.
When querying Hive-based tables composed of many small files, federated engines must issue many high-latency S3 LIST API calls to plan the query. Modern table formats like Iceberg and Delta reduce this file-listing problem using a tree of manifest files, yet they still depend on sequential remote GET request latency compared to a transactional database.
This overhead can lead to S3 throttling and high planning overhead, especially on many-small-file layouts.
Coordinator and Account-Level Concurrency Limits
Concurrency becomes a hard bottleneck. Loading a single dashboard can trigger dozens of simultaneous queries.
Self-hosted federated engines collapse these concurrent requests into a single coordinator queue. Serverless engines like Athena can also hit account-level query concurrency and API quotas that affect bursty dashboard workloads. This inflates wait times and creates a performance ceiling that is not always tied to the underlying data size or query complexity.
How does high-performance serverless OLAP solve the distributed penalty?
High-performance serverless OLAP addresses the latency and concurrency penalties of federated engines by re-architecting the relationship between compute and storage.
Compute isolation with Hypertenancy
Traditional federated engines funnel users into a shared cluster managed by a Workload Management queue. Modern serverless OLAP platforms provide isolated compute instead.
MotherDuck's Hypertenancy architecture provisions a dedicated compute instance, called a Duckling, for each BI tool connection, workspace, or agent session. Each instance has dedicated CPU and memory. This helps ensure that one user's heavy exploratory query does not create noisy neighbor contention that slows down a critical customer-facing dashboard.
Rethinking Petabyte-Scale Metadata
One innovation addressing the metadata bottleneck is the evolution of open table formats. While Iceberg and Delta improved upon Hive, they still rely on traversing manifest files in object storage, which can add sequential GET request latency.
The DuckLake table format moves metadata out of object storage and into a transactional SQL database. DuckLake turns catalog lookups into indexed SQL operations that can complete in milliseconds. This removes much of the file-listing and manifest traversal overhead that slows down object-storage-native query planning.
Teams already committed to Apache Iceberg can still take a gradual path. DuckDB has continued expanding Iceberg support, including Iceberg writes in the 1.4 LTS line and full MERGE INTO support against Iceberg tables in v1.5.3. This provides a bridge to a high-performance architecture without forcing an immediate format migration.
How DuckLake and Per-Second Billing Change the Cost Equation
This architectural model changes the cost equation. The unpredictable, per-TB-scanned pricing of federated query engines penalizes ad-hoc exploration. The flat, per-second compute billing of high-performance serverless OLAP provides more predictable costs for exploratory querying.
Real-world outcomes support these architectural claims. PriceMedic migrated off AWS Athena and Redshift to MotherDuck, achieving:
- A 14x query speedup
- Resolution of 10+ minute interactive query delays
- Approximately $20,000 per month in savings
Together AI used a 100GB TPC-DS benchmark to compare MotherDuck against Athena, Redshift, and ClickHouse. During this evaluation, ClickHouse required custom rewrites for most queries, with only 10-20% running without modification. The results showed that high-performance serverless OLAP can deliver strong performance alongside out-of-the-box standard ANSI SQL support. This reduces engineering friction without accidental cost overruns.
Evaluating alternatives for low latency OLAP in 2026
When evaluating an AWS Athena alternative, workloads must be mapped to their best-fit architecture. The market for low latency OLAP is segmented, and no universal solution exists.
| Platform | Architecture Classification | Optimal Workload | Primary Architectural Limitation |
|---|---|---|---|
| MotherDuck | High-Performance Serverless OLAP, Modern Cloud Data Warehouse | Interactive dashboards, AI data agents, ad-hoc SQL | Purpose-built for interactive SQL analytics; best complemented by a dedicated OLTP store for transactional writes and a streaming platform for continuous ingestion |
| ClickHouse | Distributed Real-Time OLAP | Raw aggregations on massive, immutable event streams | High engineering and operational management overhead |
| Snowflake / BigQuery | Cloud MPP Warehouse | Large-scale enterprise batch ETL and strict data governance | Architecture creates tension with bursty, sub-second interactive traffic |
| AWS Athena / Trino | Federated Query Engine | Infrequent, exploratory data science | High P99 latency and coordinator or account-level concurrency limits |
High-Performance Serverless OLAP (MotherDuck)
This architecture replaces the network shuffle and planning overhead of distributed systems with highly efficient vectorized execution. Benchmarks demonstrate significant speed and cost advantages over traditional warehouses. One ClickBench run completed in 5.9 seconds on MotherDuck compared to Snowflake's 14.1 seconds, at a fraction of the cost.
Distributed Real-Time OLAP (ClickHouse)
While immensely powerful for streaming ingestion, ClickHouse introduces significant engineering overhead. Operating it requires complex schema design, ZooKeeper or Keeper node management, and materialized view replication. This operational burden is often unnecessary for standard SaaS analytics workloads.
Cloud MPP warehouses, Snowflake and BigQuery
Cloud MPP architectures are not designed for the intermittent, bursty traffic of interactive dashboards. Snowflake's 60-second minimum compute billing creates a cost penalty for bursty, sub-second interactive traffic. BigQuery's variable slot allocation latency creates cost and performance conflicts with sub-second analytics requirements.
A decision framework to replace Athena for analytics
For data platform engineers, the decision to migrate requires a practical, actionable framework.
1. Map the Latency and Concurrency SLA
Define the performance contract first. If your application requires sub-second P99 latency for 50 or more concurrent users or AI agents, this requirement architecturally precludes federated query engines and traditional MPP warehouses.
2. Calculate the Cost of Inaction
Frame the decision around the ongoing costs of the existing system. This includes engineering hours spent tuning partitions, resolving S3 throttling, and managing the business impact of slow or unreliable customer-facing analytics.
3. Start with a Zero-Copy Benchmark on MotherDuck
Before committing to a full migration, run your slowest dashboard query directly against your existing S3 Parquet data on MotherDuck. No upfront data movement is required. Measure P99 latency, query cost, and engineering effort side-by-side against Athena before making the call.
Conclusion
Federated query engines were a necessary compromise for the first generation of data lakes. Today, their reliance on distributed node coordination creates real challenges for modern interactive applications. The compounding latency of object storage, coordinator bottlenecks, and account-level workload limits makes them struggle with the sub-second performance standards required by customer-facing dashboards and AI-driven products.
The shift to high-performance serverless OLAP is an architectural evolution. By reducing distributed coordination overhead, these platforms deliver the raw speed of an embedded database with the scale of a data lake.
Benchmark your slowest, most concurrent dashboard queries on a high-performance serverless OLAP platform to see the difference.
Ready to move off Athena?
MotherDuck lets you run interactive OLAP queries directly on your existing S3 data with no clusters to manage and no per-TB scanning costs. Start free with 10 GB of storage and 10 compute-hours per month and see how your slowest dashboard queries perform on isolated, vectorized compute.
Start using MotherDuck now!
FAQS
Federated query engines like Athena and Trino bottleneck on interactive analytics because they dynamically map external S3 objects over the network for every query. This constant remote network read generates significant time-to-first-byte delays. They also suffer from high-latency metadata planning and account-level concurrency limits during bursty dashboard traffic.
High-performance serverless OLAP reduces the distributed penalty by isolating compute and moving petabyte-scale metadata into a transactional SQL database. Instead of relying on slow object storage manifests for every planning step, technologies like DuckLake turn catalog lookups into indexed SQL operations that can complete in milliseconds. Dedicated compute instances help ensure heavy exploratory queries do not impact dashboard performance.
The optimal AWS Athena alternative for low latency OLAP and high concurrency is MotherDuck's isolated compute architecture. It provisions a dedicated compute instance for every BI tool connection, avoiding the shared workload contention and account-level concurrency issues that cause queries to wait during interactive workloads.
High-performance serverless OLAP platforms like MotherDuck are strong Trino alternatives for reducing P99 query latency. Instead of funneling concurrent dashboard workloads into Trino's shared coordinator queue, this architecture isolates workloads using Hypertenancy. Independent environments reduce noisy neighbor problems and stabilize sub-second response times.
DuckDB and MotherDuck are high-performance alternatives to AWS Athena for querying Parquet files directly in S3. By running zero-copy benchmarks on existing open formats, data teams can bypass much of the file-listing and metadata planning overhead. This delivers immediate vectorized execution speeds without requiring a significant upfront data migration or conversion.
ClickHouse can outperform AWS Athena for raw aggregations on massive, immutable event streams, but it is a distributed real-time OLAP system rather than a standard modern cloud data warehouse. Evaluating it as a general replacement introduces substantial operational overhead, including complex schema design. Standard ANSI SQL queries may also require custom rewrites.
Snowflake and BigQuery create cost and performance trade-offs for the bursty traffic of sub-second interactive dashboards. Snowflake's 60-second minimum compute billing can inflate costs for rapid queries, and BigQuery's variable slot allocation can introduce unpredictable latency. Both MPP warehouses can struggle to deliver cost-effective interactivity for highly concurrent user-facing workloads.
The standard migration path starts by profiling slow, high-concurrency queries before conducting zero-copy benchmarks directly on existing S3 data. Engineers can then map dashboards, BI connections, and agent sessions to isolated compute instances before running a parallel cutover. This lets teams validate performance, cost, and correctness before replacing Athena in production analytics workflows.
