The modern data landscape is currently characterized by a “great unbundling,” where architectural flexibility often comes at the cost of seamless integration. This fragmentation frequently leads to “pilot purgatory,” where data initiatives stall due to the technical friction of treating data as a byproduct rather than a high-velocity capital asset.
In this benchmark, we compared two paths to deliver the same end-state outcome:
- SAP Business Data Cloud (SAP path): Implemented in a provisioned tenant using a defined finance scenario, capturing elapsed time, engineering effort, and platform consumption aligned to published pricing.
- DIY data fabric (DIY path): Architected to production standards and decomposed into build-and-run tasks across ingestion, modeling, governance, semantics/graph, analytics, application enablement, and agent orchestration. Effort and cost were modeled using published service pricing and standardized burdened labor assumptions.
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Leads in conversion accuracy, effort reduction, and end-to-end timeline:
A study by McKnight Consulting Group. hows unmatched real-world advantages over native migration tools in head-to-head comparison, saving up to 52 weeks of conversion time for large migrations.
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Aerospike was provisioned with approximately one-third fewer infrastructure resources than ScyllaDB in all tests, due primarily to differences in recommended replication factors. Despite this, Aerospike consistently outperformed ScyllaDB across throughput, latency, and stability metrics, resulting in substantially better performance per unit of provisioned capacity.
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DynamoDB performance struggles to keep up at scale, and its pricing only gets worse as you grow. If your applications demand predictable low latency, high throughput, and operational affordability, Aerospike is the better choice.
This independent benchmark by McKnight Consulting Group compares Aerospike, DynamoDB, and DynamoDB with DAX across 1 TB, 5 TB, and 10 TB datasets for both 70/30 and 100/0 read/write workloads. The results are clear: Aerospike outperforms on every front, including latency, throughput, and total cost of ownership, at every scale.
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DIY MDM can work for a single domain or POC, but it stalls at scale with governance gaps, mounting tech debt and slower delivery.
This independent McKnight benchmark report shows Profisee for Microsoft Fabric cuts 3-year TCO by up to 55% (44% for large projects) and delivers in ~12 weeks vs. 9-52 weeks for DIY.
The report maps four real-world Fabric scenarios and pinpoints when to move beyond DIY to accelerate time-to-value with built-in governance.
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Microsoft Fabric Real-Time Intelligence Compared with a Competitive Build Solution Using Confluent Kafka and Flink Running on AWS.
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To meet today’s demand for generative and agentic AI, enterprises must choose: piece together their own stack with cloud services, or leverage an AI-ready data platform. Independent analysis by McKnight Consulting Group confirms the significant advantages of using EDB Postgres® AI for Retrieval-Augmented Generation (RAG) applications and other advanced AI use cases.
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This independent benchmark by McKnight Consulting Group compares Aerospike and Redis across 1 TB, 5 TB, and 10 TB 70/30 and 100/0 read/write workloads. The results? Aerospike outperforms Redis in latency, throughput, cost efficiency, and resilience.
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This independent benchmark by McKnight Consulting Group compares Aerospike, DynamoDB, and DynamoDB with DAX across 1 TB, 5 TB, and 10 TB datasets for both 70/30 and 100/0 read/write workloads. The results are clear: Aerospike outperforms on every front, including latency, throughput, and total cost of ownership, at every scale.
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We tested AlloyDB against AWS Aurora for PostgreSQL to determine the comparative performance and price-performance. We used TPC-C, which is a widely recognized benchmark for measuring database performance.
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