SQREAM VS. TRADITIONAL DATA WAREHOUSES: A PERFORMANCE SHOWDOWN
EXECUTIVE SUMMARY
The proliferation of big data has exposed the performance and cost limitations of traditional, CPU-centric data warehouses. This report presents a detailed, expert-level analysis of SQREAM, a GPU-accelerated data warehouse, in direct comparison to conventional architectures.
The investigation reveals that the performance disparity is not merely a function of faster hardware, but a fundamental philosophical divergence in data processing. While traditional systems rely on strategies such as pre-computation, schema normalization, and horizontal scaling to mitigate the computational burden of complex queries, SQREAM’s GPU-native architecture leverages a “brute-force” parallel processing model to execute complex analytics on-the-fly—at a fraction of the time and hardware footprint.
Key Findings:
- Architectural Paradigm Shift: SQREAM’s GPU-centric design is a radical departure from the CPU-bound, multi-node Massively Parallel Processing (MPP) and Symmetric Multi-Processing (SMP) architectures of traditional data warehouses. This shift simplifies the data stack by eliminating the need for complex pre-computation techniques like materialized views and aggregate tables.
- Performance Differentiators: Vendor-published benchmarks, while not independently verified by the Transaction Processing Performance Council (TPC), show SQREAM outperforming competitors such as Snowflake and Redshift by a factor of 1.7x to 9.5x on query time and up to 6.2x faster Total Time To Insight (TTTI) on multi-terabyte datasets.
- Cost & Efficiency: Despite the higher per-hour cost of GPU-powered machines, SQREAM’s rapid query execution and smaller hardware footprint result in a significantly lower total cost of ownership (TCO) and reduced energy consumption—critical factors for both cloud and on-premise deployments.
- Implications for Modern Workloads: SQREAM’s architecture is uniquely suited for the ad-hoc, exploratory nature of modern data science and AI/ML workloads, where the ability to query raw, un-prepped data at scale is paramount.
Summary Statement: This report provides a detailed breakdown of these findings, empowering technology leaders to make informed, strategic decisions about their data analytics infrastructure.
FOUNDATIONAL ARCHITECTURES
THE CPU VS. GPU PARADIGM
This section meticulously dissects the core architectural principles that define traditional and GPU-accelerated data warehouses, framing the performance debate as a clash of processing philosophies.
- The Traditional Data Warehouse: CPU-Centric Design
Traditional data warehouses were originally architected to manage and analyze historical data, typically loaded in large batches for tasks such as financial reporting, market research, and compliance reporting.
This design follows a three-tier model:
- Database Server (Bottom Tier): Data is loaded, stored, and retrieved.
- OLAP Server (Middle Tier): Transforms data into a format suitable for analysis and complex querying.
- Client Layer (Top Tier): Provides front-end tools for reporting, data analysis, and visualization.
Within this traditional architecture, parallelism is enabled through two primary approaches:
- Symmetric Multi-Processing (SMP):
A single physical machine with multiple CPU cores that share memory, I/O devices, and the operating system. This limits scalability to a vertical approach, where performance is improved only by upgrading to a more powerful machine. - Massively Parallel Processing (MPP):
A master node with multiple compute nodes, each operating independently without shared memory or a single OS, connected by a high-speed network. This “shared-nothing” architecture allows for horizontal scaling by simply adding more nodes to the cluster.
Despite MPP being an improvement over SMP, both systems remain fundamentally CPU-bound, creating bottlenecks as data volumes grow exponentially. Conventional query engines relying solely on CPUs can take minutes to hours to return results on large datasets—an unacceptable timeframe for modern high-velocity analytics.
The result is a system plagued by overwhelming data volumes, complex queries, and slow processing times, which ultimately stifle business innovation.
- The SQREAM Approach: A GPU-Native Architecture
SQREAM takes a radical departure from this legacy model with an architecture designed from the ground up to be GPU-native.
By harnessing the thousands of parallel processing cores in a single GPU, SQREAM provides a cost-effective alternative to traditional MPP systems that require dozens—or even hundreds—of expensive general-purpose processors spread across multiple nodes. A single GPU can contain up to 5,000 cores, purpose-built for high-volume, high-velocity numerical computation.
This parallelism is leveraged through a model similar to Single-Instruction, Multiple Data (SIMD), where one instruction processes multiple values simultaneously. This makes SQREAM exceptionally efficient for analytical operations such as GROUP BY, JOIN, and ORDER BY.
Importantly, SQREAM does not rely on GPUs alone. Instead, it employs a patented hybrid model that intelligently combines CPU and GPU resources:
- The query compiler determines which tasks are best suited for GPUs (highly parallelizable relational algebra operations).
- Other tasks, such as text processing or operations where data-copying overhead outweighs GPU benefits, are handled by CPUs.
- The main workload is executed by specialized C++/CUDA Workers that are deliberately “unintelligent,” requiring precise instructions to maximize parallel efficiency.
This approach addresses the fundamental challenges of parallel processing within a single machine or a small cluster, rather than relying on a large, distributed network of loosely coupled nodes.
The shift from CPU-based to GPU-based parallelism represents a fundamental change in tackling big data. Traditional systems treat parallelism as a distributed systems challenge, where performance is often constrained by network latency and the overhead of synchronizing data across nodes. In contrast, SQREAM exploits the immense on-chip parallelism available in GPUs, reducing the bottleneck to CPU–GPU interconnect bandwidth (e.g., PCI-Express or NVLink), which continues to improve.
As a result, for compute-intensive analytical workloads, a single GPU can often outperform a large CPU cluster by eliminating the complexity and inefficiency of distributed systems.
DATA MODELLING AND STORAGE:
THE ANALYTIC ADVANTAGE
This section compares how each architectural paradigm addresses the challenges of data modelling and storage for analytical queries, highlighting a fundamental difference in their operational philosophy.
- Columnar Storage in Modern Data Warehouses
A shared foundation of most modern analytical data warehouses—including SQREAM—is the use of columnar storage.
Unlike traditional row-based databases that store all attributes of a single record together, columnar databases group and store each column’s values contiguously. This design is particularly well-suited for OLAP (Online Analytical Processing) workloads, which are characterized by read-heavy operations requiring a small number of columns from a very large number of rows.
The advantages of columnar storage are substantial:
- Reduced I/O: Only the relevant columns are read.
- Improved compression: Values within a column tend to be of the same type, offering high redundancy and better compression ratios.
- Vectorized execution: Enables data to be processed in efficient, CPU- or GPU-friendly batches.
- Pre-Computation and Optimizations in Traditional Data Warehouses
Due to the inherent performance limitations of CPU-bound systems on massive datasets, traditional data warehouses rely heavily on pre-computation techniques as a core performance strategy.
Key techniques include:
- Aggregates: Pre-calculated summary tables derived from GROUP BY SQL queries. These can reduce the number of rows processed by a query, improving performance by factors of 100 to 1,000.
- Materialized Views: Pre-computed datasets stored for later use, automatically updated when the base tables change. While highly effective for repetitive queries, they add cost and complexity to the data stack and are poorly suited for ad-hoc queries.
In addition to pre-computation, traditional systems optimize performance through schema design methodologies. For example, the Kimball method uses dimensional modelling to organize data into fact and dimension tables, often denormalized to minimize joins and simplify queries upfront. Pre-aggregated data marts further optimize performance for predictable BI and reporting needs.
While effective for fixed reporting, these strategies limit flexibility and create significant challenges for unpredictable, exploratory analytics.
- SQREAM’s Brute-Force Philosophy
In contrast, SQREAM’s GPU-powered architecture bypasses the need for most pre-computation techniques. Rather than pre-calculating aggregates or relying on carefully designed schemas, SQREAM executes complex operations—such as JOINs, aggregations, and filtering—directly on raw, full datasets “in record time.”
Key differentiators include:
- GPU-Accelerated Nested JOINs: Capable of handling joins with any number of tables and keys across massive datasets.
- Deferred Gather Optimization: Collects only the necessary columns after GPU execution, conserving memory and improving performance.
This architecture frees analysts and data scientists from the traditional bottleneck of waiting for engineering teams to build new aggregates or materialized views. Instead, SQREAM allows direct querying of raw, unstructured data—accelerating discovery and reducing query latency from hours to minutes, or minutes to seconds.
The divergence in philosophy is clear:
- Traditional Warehouses: “Managed” environments optimized for predictable queries, such as fixed BI dashboards. Performance requires heavy reliance on schema design and pre-computed data.
- SQREAM: An “agile” environment powered by brute-force GPU parallelism, designed for ad-hoc exploration, discovery, and innovation.
This fundamental shift democratizes data access, enabling business users and analysts to ask complex, previously unthinkable questions directly on raw data—without waiting days or weeks for new aggregates or schema adjustments. In doing so, SQREAM directly addresses the pain points of stifled innovation and prolonged query times that plague legacy systems.
PERFORMANCE BENCHMARKS AND REAL-WORLD SHOWDOWN
This section critically analyses available performance data, providing a quantitative basis for claims and exposing the nuances of vendor-published benchmarks.
- Benchmarking Methodologies and Caveats
Industry-standard benchmarks such as TPCx-BB and TPC-DS are designed to objectively compare analytical systems by simulating real-world workloads involving complex, business-oriented ad-hoc queries.
It is important to note:
- SQREAM’s published results are based on internal field tests or benchmarks “based on” TPC standards, not official, certified TPC results.
- These results provide valuable directional insights, but lack the third-party verification that certified TPC benchmarks offer.
This distinction is crucial for transparency in any major technology evaluation.
- Head-to-Head Performance Analysis
SQREAM has released internal benchmarks comparing its performance against leading cloud data warehouses.
- In a TPCx-BB–based benchmark on a 30TB dataset, SQREAM (cloud deployment) was tested against Snowflake, Google BigQuery, and Amazon Redshift.
- Query Execution: SQREAM delivered queries 1.7x to 4.6x faster than competitors.
- Total Time To Insight (TTTI): SQREAM achieved 1.5x to 9.5x faster TTTI, a critical metric that combines data ingestion and query execution.
- In a 300TB AWS benchmark against Snowflake, SQREAM reported a 6.2x faster TTTI.
Ingestion Speeds:
- Up to 3.3 TB per hour per instance
- Up to 2 TB per hour per GPU
- Claimed to be 10x–50x faster than conventional systems.
This high-speed ingestion is a major factor behind SQREAM’s low TTTI advantage.
- The Cost Equation: Total Cost of Ownership (TCO)
Performance is not just about speed—it’s about economic impact.
- While GPU-powered machines may have a higher hourly compute cost, SQREAM’s superior execution speeds translate into less total compute time consumed.
- In cloud pay-per-use models, this results in a lower overall TCO.
Key economic differentiators:
- Cost Savings: SQREAM emphasizes total cost reductions to “just 1/10th” of conventional solutions.
- Hardware Footprint: A single 2U GPU-powered server can replace a 42U rack of CPU servers.
- Sustainability Impact: Smaller footprint = lower power usage and carbon emissions, aligning with green IT initiatives.
Thus, the “showdown” is not just technical—it is also economic, where faster insights directly reduce operational costs.
- Vendor Benchmark Summary
SQREAM conducted internal TPCx-BB 30TB benchmark tests to compare performance and cost efficiency across leading cloud data platforms. On AWS, SQREAM reported a compute cost of $17.4 per hour and a storage cost of $23 per terabyte, with an average query time of 3 minutes and 32 seconds, while Snowflake showed slightly lower compute costs at $16 per hour but higher storage costs at $40 per terabyte (on-demand), with no published query times. Redshift, also on AWS, came in at $26.08 per hour for compute and $24 per terabyte for storage, without disclosed query performance. On GCP, SQREAM achieved lower compute costs of $16.88 per hour and $20 per terabyte in storage, with an even faster average query time of 3 minutes and 17 seconds. In comparison, BigQuery and Snowflake both recorded $16 per hour compute costs with significantly higher storage costs of $46 per terabyte (on-demand), but again without disclosed performance metrics. Although direct TTTI and query times were unavailable for Snowflake, Redshift, and BigQuery, SQREAM reported overall comparative performance gains of 1.5x–9.5x faster on TTTI and 1.7x–4.6x faster on average query times.
SCALABILITY, CONCURRENCY, AND INTEGRATION
This section explores how traditional warehouses and SQREAM manage growth, concurrency, and integration, highlighting how architectural choices impact scalability and modern workloads.
- Vertical vs. Horizontal Scaling
- Traditional Data Warehouses:
- Scale vertically by adding CPU power to a single machine.
- Scale horizontally via MPP clusters, adding compute nodes to expand capacity.
- Challenges: Beyond a certain point, scaling introduces complexity, high costs, and diminishing returns.
- SQREAM’s Approach:
- Claims linear scalability, meaning adding more GPUs or nodes leads directly to faster performance.
- Supports up to 40 GPUs in a single chassis, offering extraordinary vertical scale-up within a compact footprint.
- Avoids the typical performance degradation seen in CPU-based systems as data volumes grow.
- Workload Management and Concurrency
- Traditional Systems:
- Struggle with high concurrency → query queuing and degraded performance as multiple users compete for CPU.
- Requires complex workload management and often plateaus under heavy concurrent demand.
- SQREAM’s Model:
- Each worker process can run one large query while still handling multiple smaller queries concurrently.
- Features Dynamic Workload Management for on-the-fly prioritization and resource allocation.
- This model reduces wait times and resource contention, making it better suited for complex, modern workloads.
Executive Insight: SQREAM’s concurrency model addresses a major frustration for data teams: long queues and unpredictable performance. Its linear scalability provides a more predictable and cost-effective growth path compared to legacy systems.
- Ecosystem Integration and Use Cases
- Traditional Data Warehouses:
- Have deep integration with BI tools and reporting workflows.
- Optimized for structured, reporting-driven workloads.
- SQREAM:
- Fully ANSI-92 SQL compliant.
- Supports standard connectors: ODBC, JDBC, Python → making it a drop-in solution with no forklift upgrade.
- Positioned as a modern data engine, not just a warehouse.
AI & ML Enablement:
SQREAM’s architecture is tailored for the AI Factory model.
- High-speed ingestion & transformation of petabytes of data.
- Enabling full dataset model training at scale.
- Supporting real-time inferencing at massive volumes.
This positions SQREAM beyond BI/reporting → as a core enabler of next-gen AI/ML workloads, opening new classes of use cases not possible in legacy environments.
- Comparative Summary
Traditional CPU-based MPP data warehouses scale either horizontally by adding nodes or vertically through CPU upgrades, relying on MPP or SMP parallelism models to distribute workloads. Their concurrency is managed with workload management techniques and query queuing, but performance often suffers from bottlenecks caused by inter-node communication and CPU-bound tasks. In contrast, SQREAM’s GPU-accelerated architecture combines vertical scaling, by adding GPUs, with horizontal scaling across nodes to deliver near-linear scalability. Instead of relying solely on CPU-driven MPP, SQREAM leverages thousands of GPU cores for on-chip parallelism, enabling massive throughput. Its concurrency model allows per-worker execution, meaning small queries can run efficiently alongside large, complex ones, supported by dynamic workload management. While the main bottleneck shifts to CPU–GPU interconnect bandwidth (such as PCIe), the hybrid design provides a more efficient balance of scalability, concurrency, and performance.
INDUSTRY-SPECIFIC RECOMMENDATIONS
- Finance →
Financial institutions face the challenge of processing billions of daily transactions while staying compliant with regulations and preventing fraud. SQREAM enables lightning-fast fraud detection and real-time risk analysis by analyzing petabytes of transaction data with minimal latency. For example, a global bank can run anomaly detection on millions of credit card swipes per minute, flagging suspicious activities instantly instead of waiting for batch processing. This proactive approach not only enhances security but also ensures compliance with regulatory requirements like Basel III and PCI DSS.
- Healthcare →
With the rise of precision medicine and genomic research, healthcare providers and researchers need to process massive datasets that traditional systems cannot handle efficiently. SQREAM’s GPU-accelerated architecture allows hospitals and research labs to analyze terabytes of genomic sequences or patient imaging data quickly. For instance, a genomic research institute can reduce the time for DNA sequencing analysis from weeks to hours, enabling faster drug discovery and more personalized treatment plans for cancer patients. This level of agility is crucial in responding to emerging health crises and delivering tailored care.
- Retail →
Modern retailers deal with huge volumes of customer data, spanning from in-store purchases to e-commerce clicks. SQREAM enables retailers to analyze both streaming and historical data simultaneously, helping them understand real-time customer behaviours, optimize inventory, and deliver personalized marketing campaigns. For example, a large e-commerce platform can instantly analyze buying patterns during a flash sale, dynamically adjusting promotions and recommendations to increase conversions while preventing stockouts. This directly translates into improved customer satisfaction and higher revenue.
- Manufacturing →
In Industry 4.0 environments, manufacturers rely on IoT sensors and connected devices to monitor machinery and production lines. SQREAM’s ability to ingest and process billions of sensor readings in real time makes predictive maintenance possible. For instance, an automotive manufacturer can detect anomalies in machine vibrations across factories worldwide and schedule maintenance before breakdowns occur. This prevents costly downtime, improves safety, and ensures uninterrupted production.
- Telecommunications →
Telecom operators handle some of the world’s largest datasets, including call detail records (CDRs), network performance logs, and customer data. SQREAM accelerates analytics on this data, enabling faster network optimization and fraud detection. For example, a telecom provider can analyze billions of CDRs to detect SIM-box fraud or optimize network traffic during peak hours, ensuring better call quality and customer experience. This gives operators a competitive advantage in a highly saturated market.
- Government & Public Sector →
Governments deal with diverse and complex datasets ranging from census information and tax records to cybersecurity and defences intelligence. SQREAM enables agencies to analyze these massive datasets quickly to make informed policy decisions. For instance, a national tax authority could analyze trillions of transaction records to detect tax evasion in real time, while a defences agency could process satellite imagery and signals intelligence to enhance national security.
- Energy & Utilities →
The energy sector is under immense pressure to transition to cleaner sources while managing demand and optimizing infrastructure. SQREAM helps utilities analyze billions of smart meter readings and IoT sensor data across grids to forecast demand accurately and detect anomalies. For example, an oil and gas company can process seismic data at scale to identify drilling opportunities more quickly, reducing exploration costs and improving sustainability initiatives. - Transportation & Logistics →
Supply chains generate enormous data streams from shipping routes, fleet tracking, and warehouse systems. SQREAM accelerates analytics on these datasets, enabling real-time optimization. For example, a global logistics provider can analyze GPS data from thousands of delivery trucks to optimize routes dynamically, reduce fuel consumption, and ensure on-time delivery. Airlines can also use SQREAM to analyze maintenance logs and flight sensor data, improving aircraft reliability and passenger safety.
SYNTHESIS OF FINDINGS AND STRATEGIC RECOMMENDATIONS
The analysis highlights a fundamental philosophical and architectural divide between GPU-accelerated data warehouses like SQREAM and traditional CPU-based systems.
Traditional warehouses, limited by CPU processing power, rely heavily on pre-computation and rigid schema design to maintain performance on large datasets. While this strategy works for predictable, repetitive reporting, it introduces friction when dealing with ad-hoc queries, exploratory analytics, and modern AI/ML workloads.
SQREAM, on the other hand, takes a different approach. By leveraging the massive parallelism of GPUs, it executes complex queries directly on raw data — eliminating much of the need for pre-processing and enabling near real-time insights.
Vendor-published benchmarks, though not officially TPC-certified, report a 6.2x faster Time-To-Insight (TTTI) on multi-terabyte datasets when compared to traditional systems. This raw performance advantage translates into a strong economic argument:
- Lower Total Cost of Ownership (TCO): Faster query completion offsets higher GPU costs.
- Smaller Hardware Footprint: Reducing infrastructure needs also lowers carbon footprint, supporting corporate sustainability initiatives.
For decision-makers, the choice depends on business priorities:
- Traditional Data Warehouses → Best suited for stable, well-defined BI and reporting workloads.
- GPU-Accelerated Solutions (SQREAM) → Ideal for organizations dealing with petabyte-scale analytics, time-sensitive insights, complex ad-hoc queries, or building AI/ML capabilities.
However, given the lack of independent benchmarks, the most strategic move is to run a Proof of Concept (PoC). Testing with an organization’s real workloads provides the clearest validation of performance, cost-efficiency, and long-term value.
Looking ahead, the exponential growth of data and rising complexity of analytics suggest that GPU-powered architectures are poised to dominate. They not only optimize today’s processes but also unlock new possibilities for data-driven innovation.
