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.
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.
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.
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
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.
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.
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.
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.
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.