The pace of relational analytical databases deploying in the cloud are at an all-time high. The goal of this paper is to provide information to help a customer make the best decision, looking at factors in cloud data platform pricing – such as scope, scale, deployment, etc. – and how to determine the ultimate success metric when it comes to making a decision on a cloud data warehouse deployment – price-performance.
Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technology applications. Large companies and complex organizations have turned to APIs for exchanging data to knit these heterogeneous systems together and turn data into a service. In this paper, we reveal the results of performance testing we completed with four full-lifecycle API management platforms.
This report is the third in a series of enterprise roadmaps addressing cloud analytic databases. The last two reports focused on comparing vendors on key decision criteria that were targeted primarily at cloud integration. This report is an update to the 2019 Enterprise Roadmap: Cloud Analytic Databases. However, this time around we have new vendors and a new name. We’ve reviewed and adjusted our inclusion criteria. We’re now targeting the technologies that tackle the objectives of an analytics program, as opposed to the means by which they are achieving these objectives.
In this Business Technology Impact report, we take a look at a large multi-national firm and its implementation of a data lake and data catalog. Within the company, a small team worked to transform the data lake from an underutilized, misunderstood ‘white elephant’ into a resource that drove the company’s growth and innovation.
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.
This report details the results of a Transactional Field Test, derived from the industry-standard TPC Benchmark™ E (TPC-E), compared:
- Microsoft SQL Server 2019 on an Amazon Web Services (AWS) r5a.8xlarge Elastic Cloud Compute (EC2) instance with General Purpose (gp2) volumes
- Microsoft SQL Server 2019 on an Azure E32as_v4 Virtual Machine (VM) with P30 Premium Storage drives
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:
- Microsoft SQL Server on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances
- Microsoft SQL Server Microsoft on Azure Virtual Machines (VM)
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.
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.