Research

Delivering on the Vision of MLOps: A maturity-based approach

This report is targeted at Business and IT decision-makers as they look to implement MLOps, which is an approach to deliver Machine Learning- (ML-) based innovation projects. As well as describing how to address the impact of ML across the development cycle, it presents an approach based on maturity levels such that the organization can build on existing progress.

Link to report.

SQL Transaction Processing, Price-Performance Testing

This report outlines the results from a 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 Compute Cloud (EC2) instances
  2. Microsoft SQL Server Microsoft on Azure Virtual Machines (VM)

Link to report.

Embedded Database Performance Report 2

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.

See SQLite, the traditional alternative to the file system approach for embedding data management into edge applications and Actian Zen perform.

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

Link to report.

Embedded Database Performance Report 1

This benchmark did a head-to-head comparison of Actian Zen and InfluxDB for IoT time series data, both installed on a Raspberry Pi running Linux ARM/Raspbian using their native APIs (NoSQL).

Link to report.

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 GigaOm Analytic Field Test derived from the industry standard TPC Benchmark™ DS (TPC-DS)1 comparing Amazon Redshift, Azure SQL Data Warehouse, Google BigQuery, and Snowflake Data Warehouse — four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences between the four platforms.

Link to report.

Cloud Analytics Performance Report

This paper specifically compares two fully-managed, cloud-based analytical databases, Actian Avalanche and Amazon Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures that scale and provide high-speed analytics. It should be noted while our testing measures the cloud-based performance of both offerings, Avalanche, unlike Redshift, is also available as an on-premise offering, Vector. In addition, Vector is available for developers as a free on-premise community edition, as a download with support in both the Amazon Web Services (AWS) and Azure marketplaces with single-click deployment.

Link to report.

Modernizing Insurance Data Platforms to Improve Governance and Enrich Customer Experience

Pekin Insurance is one of the nation’s most successful insurance providers, with combined assets of $2 billion, more than 800 employees, 1,500 agencies, and 8,500 independent agents. Pekin Insurance is on the fast path to a full overhaul and modernization of their data, from the platform, to quality, to governance, to enabling consumers. They have built a 3-year strategy focusing on Data & Analytics and are wrapping up the final year, focused on a robust data layer with a data lake and a data warehouse, on target, on budget, and within scope.

Link to report.

Data Warehouse in the Cloud Benchmark Product Profile and Evaluation: Amazon Redshift, Microsoft Azure SQL Data Warehouse, Google BigQuery, and Snowflake Data Warehouse

This report outlines the results from the GigaOm Analytic Field Test based on an industry standard TPC Benchmark™ H (TPC-H)1 to compare Amazon Redshift, Azure SQL Data Warehouse, Google Big Query, and Snowflake Data Warehouse—four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences in the four platforms.

Link to Report.

Analyst Report: State of Data Warehouse

Table of Contents
1 The Data Warehouse in the Organization

2 Relationships to Other Research Reports

3 The Data Warehouse Database
4 Analytic Store Platform Choices
5 Choosing the Data Warehouse Platform

6 The Cloud Analytic Database
7 Data Warehouse Flavors
7.1 The Customer Experience Transformation Data Warehouse
7.2 The Asset Maximization with IoT Data Warehouse
7.3 The Operational Extension Data Warehouse
7.4 The Risk Management Data Warehouse
7.5 The Finance Modernization Data Warehouse
7.6 The Product Innovation Data Warehouse
8 Key Takeaways

Link to report.

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.