Cassandra Total Cost of Ownership Study

This study examines the full cost and true value of Cassandra self-managed on Google Cloud (GCP) and the cost of a fully managed serverless Cassandra service. We included dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on serverless Cassandra), storage growth (each write operation adds new data) and people cost in our three-year total cost of ownership calculations. People costs take into account that certain capabilities in serverless Cassandra needed for the workload were not available in self-managed Cassandra, requiring workarounds. We used market rates and typical splits of full-time equivalent (FTE) and consulting to determine our people costs.

Available here (GigaOm wall).

Designing Data: How a Pharmacy Benefit Management Firm Modernized its Data Architecture Around Microservices and Real-Time Integration

The proliferation of data creates a lot of risk for many organizations, especially in healthcare organizations where privacy, timeliness, and safety are paramount. A pharmacy benefit management (PBM) company faced the risk of being overwhelmed by these demands and launched a strategic effort to build out a data platform and streaming data engine aligned around a microservices architecture. This report explores the challenges and lessons encountered in the effort.

Available here (GigaOm wall). 

SQL Transaction Processing, Price-Performance Testing: Microsoft SQL Server Evaluation: Azure Virtual Machines vs. Amazon Web Services EC2

This report outlines the results from a GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), to compare two IaaS cloud database offerings:

  1. Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
  2. Microsoft SQL Server Microsoft on Azure Virtual Machines (VM)

Both are installations of Microsoft SQL Server, and we tested Red Hat Enterprise Linux OS.

Available here.

Cloud Database Performance – McKnight Benchmark Report

Companies rely on analytical databases for insights essential to the company survival and competitive advantage. The cloud can provide improved economics and operational simplicity, but choosing the most performant and cost-effective cloud data analytics solution is critical.

This third-party report from McKnight Consulting Group uses industry-standard benchmark principles to evaluate the performance of three cloud-optimized analytical platforms architected for the separation of compute and storage – Vertica in Eon Mode, Amazon Redshift, and an unnamed cloud data platform.

Read the report to learn how Vertica in Eon Mode:

  • Achieves best performance in all benchmark tests for scale and concurrency
  • Runs the most queries per hour, at every level of scale and concurrency
  • Cuts performance costs 45% – 73% over Amazon Redshift
  • Slashes performance costs 84% – 92% over the unnamed data cloud platform

Available here.

Modernize Data Warehousing: Beyond Performance, The Importance of Other Key Attributes

Organizations looking for enterprise data warehouses (EDWs) cannot afford to base their evaluation on query price-performance alone. There’s much more to it. You also need capabilities that reduce the time needed for configuration, management, tuning, and other tasks that can take away from valuable time spent on business analytics.

This new whitepaper from McKnight Consulting Group explores factors that can reduce the costs of analytics far beyond performance, such as licensing structure, data storage, support for non-structured data, concurrency scaling, and much more. Download your copy today, and make a well-informed decision when choosing an EDW platform.

Available here.


We conducted an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited platform to run Microsoft SQL Server based on their individual requirements.

Read the full benchmark report, which provides insight to help IT professionals, DevOps engineers, platform architects and information security practitioners evaluate a Kubernetes platform optimal for running I/O intensive Microsoft SQL Server applications.

Available here.

SQL Transaction Processing Price-Performance Testing: Azure Virtual Machines vs. AWS EC2 Instances

Get free access to this 30-page GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), to compare two IaaS cloud database offerings: Microsoft SQL Server on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances; Microsoft SQL Server Microsoft on Azure Virtual Machines (VM). Both are installations of Microsoft SQL Server and we tested on both Windows Server OS and Red Hat Enterprise Linux OS.

Available here.

Cloud Data Warehouse Performance Testing

This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. This report outlines the results from a field test derived from the industry standard TPC Benchmark™ DS (TPC-DS) comparing five relational analytical databases based on scale-out cloud data warehouses.

Available here.

Application Cache Performance Product Evaluation: Azure Cache for Redis

Applications and their performance requirements have evolved dramatically in today’s landscape. The cloud enables enterprises to differentiate and innovate with APIs and microservices at a rapid pace. Cloud providers, like Azure, allow microservice endpoints to be cloned and scaled in a matter of minutes. The cloud offers elastic scalability compared to on-premises deployments, enabling faster server deployment and application development and less costly compute. In this paper, we reveal the results of application performance testing we completed both with and without Azure Cache for Redis on top of Azure SQL Database and Azure Database for PostgreSQL.

Link to report. 

API and Microservices Management Product Evaluation: Kong Enterprise, Apigee Edge, and Apigee Edge Microgateway

Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations comprise a vast array of applications and systems, many of which have turned to APIs to exchange data as the glue to hold these heterogeneous artifacts together. In this paper, we reveal the results of performance testing we completed across three API and Microservices Management platforms: Kong Enterprise, Apigee Edge, and Apigee Edge Microgateway.

Link to report (wall).