White Papers Plus

Analyst Report: API Management Benchmark Report

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 are a vast array of applications and systems, many of which have turned to APIs as the glue to hold these heterogeneous artifacts together.

This report examines the results of a performance benchmark completed with two popular API management solutions: Kong and Apigee—two full life-cycle API management platforms built with scale-out potential and architectures for large scale, high performance deployments. Despite these similarities, there are some distinct differences in the two platforms.

Link to report.

TDWI Checklist Report | Six Best Practices to Ignite the Customer Experience with IoT

Becoming more customer-centric is a difficult task, but you can build an organization that delivers seamless, targeted, effective customer experiences. Analytics and advanced analytics such as machine learning (ML) are key to gaining customer insight and using this insight to drive positive customer experiences. Company success is often dependent on the customer experience.

This checklist focuses on how IoT data and analytics can be used to help drive the customer experience.

Link to report.

Embedded Database Performance Report

Today, to fully harness data to gain a competitive advantage, embedded databases need a high level of performance to provide real-time processing at scale.

SQLite, the traditional, but now obsolete, alternative to the file system approach for embedding data management into edge applications, just can’t keep up with Actian Zen.

See for yourself in this benchmark report by McKnight Consulting Group.

Link to report.

Analyst Report: Enterprise Roadmap: Cloud Analytic Databases 2019

The world of data is rapidly changing. Data is the prime foundational component of any meaningful corporate initiative. Managing and evaluating this prime asset is ongoing continually in competitive organizations. The incorporation of new information into this process is required, and tradeoffs must be considered in the decision-making process.

Last year this report focused on comparing vendors on key decision criteria that were primarily targeted at cloud integration. The vectors represented how well the products provided the features of the cloud that corporate customers have come to expect. In 2017 we chose products with cloud analytic databases that exclusively deploy in the cloud, or had undergone major renovation for cloud deployments. This report is an update to the 2017 Sector Roadmap: Cloud Analytic Databases and, as such, continues with an analysis of the same vendors.

Link to report.

Cloud Database Performance Benchmark

Read this cloud analysis paper for a benchmark of Vertica and Amazon’s Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures.

Link to report.

Data Warehouse in the Cloud Benchmark

Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high speed to derive timely insights. This benchmark study focuses on the performance of cloud-enabled, enterprise-ready, relationally based, analytical workload solutions from Microsoft Azure SQL Data Warehouse and Amazon Redshift.

The benchmark tested the scalability of corporate-complex workloads in terms of data volume with 30TB of data. The testing was conducted using as similar a configuration as can be achieved across Azure and Amazon Web Services (AWS) offerings.

Link to report.

Cloud Database Performance Benchmark: Vertica in Eon Mode and Snowflake

Organizations rely on Big Data platforms to analyze large volumes of data from a variety of sources to derive timely insights on everything from fraud detection, to customer churn, predictive maintenance and more. As more organizations move this data to the cloud for improved economics and operational simplicity, choosing the most performant and cost-effective data analytical solution is critical.

This third-party report from McKnight Consulting Group uses industry-standard data benchmark principles to evaluate the performance of two cloud-optimized data analytical solutions architected for the separation of compute and storage — Vertica in Eon Mode and Snowflake Computing.

Link to report.

Cloud Database Performance Benchmark Product Profile and Evaluation: Actian Vector and Impala

We conducted this benchmark study, which focuses on the performance of cloud-enabled, enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Impala. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

Link to paper.

Cloud Database Performance Benchmark Product Profile and Evaluation: Actian Vector and Snowflake

We conducted this benchmark study, which focuses on the performance of cloud-enabled, enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Snowflake. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

Link to paper.

Cloud Database Performance Benchmark Product Profile and Evaluation: Actian Vector and Amazon Redshift

We conducted this benchmark study, which focuses on the performance of cloud-enabled1 , enterprise-ready, relationally-based, analytical-workload solutions from Actian Vector and Amazon Redshift. The intent of the benchmark’s design was to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry sector might encounter and ask. The benchmark results were insightful in revealing query execution performance.

Link to paper.