This report outlines the results from a 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 Cloud Compute (EC2) instances
- Microsoft SQL Server on Microsoft Azure Virtual Machines (VM)
Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.
To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta.
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
For the analysis, we used categories of Total Cost of Ownership (TCO) time-to-value and enterprise capabilities. Our assessment resulted in a score of 2.9 (out of 3) for Azure ML using managed endpoints, 1.9 for Google Vertex AI, and TK for AWS SageMaker. The assessment and scoring rubric and methodology are detailed in an annex to this report.
This report outlines the results from an analytic performance test derived from the industry-standard TPC Benchmark™ DS (TPC-DS) to compare Cloudera Data Warehouse service (CDW)—part of the broader Cloudera Data Platform (CDP)—with four prominent competitors: Amazon Redshift, Azure Synapse Analytics, Google BigQuery, and Snowflake. Overall, the test results were insightful in revealing query execution performance of these platforms.
An Evaluation Guide for Technology Decision Makers.
Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources.
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a selection that allows a worry-less experience with the architecture and its components.
We decided to take four leading platforms – Azure, AWS, GCP and Snowflake – for machine learning under analysis. We have learned that the cloud analytic framework selected for an enterprise, and for an enterprise project, matters to cost.
Picking the wrong event streaming platform for your organization can have massive consequences in terms of fit, function and of course cost. With Apache Pulsar quickly gaining mindshare within enterprises that need a comprehensive, open source event streaming and messaging platform, the expert researchers at GigaOm decided to see how this new, up and coming technology compares to the old industry stalwart: Apache Kafka.
So how did Pulsar stack up? See for yourself.
The COVID-19 pandemic and subsequent shutdowns posed a direct and unique challenge to the United Kingdom’s Department for Work and Pensions, as the number of active claimants spiked from more than 2 million just before the pandemic to more than 5 million in the span of a couple months. Learn how the DPW leveraged MongoDB to scale its microservices-based infrastructure to protect millions of citizens during a time of crisis.
This report focuses on API management platforms deployed in the cloud. The cloud enables enterprises to differentiate and innovate with microservices at a rapid pace. It allows API endpoints to be cloned and scaled in a matter of minutes. And it offers elastic scalability compared with on-premises deployments, enabling faster server deployment and application development, and allowing less costly compute.