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.
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.
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.
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.
In this paper, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which tools and platforms have made, or are in the process of making, the leap to this new generation of data management and integration capabilities.
The vendors/products chosen were:
Azure (Azure Data Factory)
Cloudera (Cloudera Replication Manager)
Data, web, and application security has evolved dramatically over the past few years. Just as new threats abound, the architecture of applications—how we build and deploy them—has changed. We’ve traded monolithic applications for microservices running in containers and communicating via application programming interfaces (APIs)—and all of it deployed through automated continuous integration/continuous deployment (CI/CD) pipelines. The frameworks we have established to build and deploy applications are optimized for time to market—yet security remains of utmost importance.
Our focus is specifically on approaches to securing apps, APIs, and microservices that are tuned for high performance and availability. We define “high performance” as companies that experience workloads of more than 1,000 transactions per second (tps) and require a maximum latency below 30 milliseconds across the landscape.
This authoritative report from GigaOm Research, a respected independent industry analyst firm, details the key performance and cost criteria to guide your cloud data warehouse selection.
“Price and performance are critical points of interest…our analysis reveals Avalanche to be the industry leader on this criterion.”
Highlights from the report:
- Key factors to consider when evaluating a hybrid cloud data warehouse
- Head-on vendor comparisons across performance and cost using industry standard TPC-H benchmark
- Vendors analyzed include Actian Avalanche, Snowflake, Amazon Redshift, Microsoft Azure Synapse and Google BigQuery
- Assessment of single user and multiple concurrent users scenarios
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.
In this report, we compare the three major data pipeline platforms: Matillion, Stitch, and Fivetran; and run them through a series of selected tests that highlight their degree of automation, ease of setup, and documentation. We evaluated aspects that include the time and effort required to set up a source-destination connection, the degree of automation throughout the process, and the quality of documentation to support the effort. These areas address the three major “humps of work” we have encountered in our field work with data pipelines.
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.