Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. We decided to take four leading platforms for machine learning under analysis. We have learned that the cloud analytic framework selected for an enterprise and an enterprise project matters in terms of cost.
This report focuses on real-time data and how autonomous systems can be fed at scale reliably. To shed light on this challenge, we assess the ease of use of a fully managed Kafka platform—Confluent Cloud—and a self-managed open-source Apache Kafka solution.
This report focuses on the performance of cloud-enabled, enterprise-ready, popular log analytical platforms Microsoft Azure Data Explorer (part of Azure Synapse Analytics), Google BigQuery, and Snowflake. Due to cost limitations with Elasticsearch and AWS OpenSearch, we could not run our tests on Elasticsearch. Microsoft invited GigaOm to measure the performance of the Azure Data Explorer engine and compare it with its leading competitors in the log analytics space. The tests we designed intend to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry might encounter in their log analytics.
In this report, we tested complex workloads with a volume of 100TB of data and concurrency of 1 and 50 concurrent users. The testing was conducted using comparable hardware configurations on Microsoft Azure and Google Cloud.
Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations encompass a vast array of applications and systems, many of which have turned to APIs for exchanging data as the glue that holds these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight, loosely-coupled microservices. This change allows organizations to knit together disparate systems and applications without creating technical debt from tight coupling with custom code or proprietary, unwieldy vendor tools.
This report reveals the results of performance testing we completed on these API and microservices management platforms: Kong Enterprise, Google Cloud Apigee X, and MuleSoft Anypoint Flex Gateway.
This study examines the full cost and true value of self-managed OSS Apache Cassandra® vs DataStax AstraDB fully managed DBaaS in Google Cloud. Our three-year total cost of ownership (TCO) calculations account for dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on Astra DB), storage growth (each write operation adds new data) and people cost. People costs consider that certain capabilities in Astra DB needed for the workload were not available in self-managed Cassandra, requiring workarounds. We used market rates and typical splits of full-time equivalent (FTE) and consulting to determine our people costs.
Competitive data-driven organizations rely on data-intensive applications to win in the digital service economy. These applications require a robust data tier that can handle the diverse workloads demands of both transactional and analytical processing while serving an interactive, immersive customer experience. The resulting database workloads demand low-latency responses, fast streaming data ingestion, complex analytic queries, high concurrency, and large data volumes.
This report outlines the results from a Field Test derived from three industry standard benchmarks—TPC Benchmark™ H (TPC-H), TPC Benchmark™ DS (TPC-DS), and TPC Benchmark™ C (TPC-C)—to compare SingleStoreDB, Amazon Redshift, and Snowflake.
Your Analytical Database Deployment will probably be to Multiple Clouds. Learn about the Role of the Data Warehouse in a World with Data Lakes, Data Science and Decentralization, Options for Provisioning the Data Warehouse and Why Multiple Clouds, Cloudwashing – Cloud-Enabled/Hosted vs Cloud-Native vs Cloud-Owned and Multi-Cloud Flexibility and Deployment Freedom.
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The goal of our study presented in this paper is to objectively uncover whether NetApp is truly positioned to deliver on value propositions to the enterprise. To meet this objective, we designed a field test derived from monitoring, troubleshooting, optimizing, and securing scenarios common to the modern enterprise with, or in the process of, migrating to a hybrid cloud.
This test measured enterprise response to usual and important situations including greedy/degraded applications, underutilized infrastructure, and ransomware simulations.
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MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors support MLOps: the major offerings are Microsoft Azure ML, Google Vertex AI, and Amazon SageMaker. We looked at these offerings from the perspective of enterprise features and time to value.
For the analysis, we used categories of time to value and enterprise capabilities. As shown in Table 1, our assessment resulted in a score of 2.95 (out of 3) for Azure ML using managed endpoints, 2.12 for Google Vertex AI, and 2.83 for Amazon SageMaker. The higher the score, the better, and the scoring rubric and methodology are detailed in an appendix to this report.
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