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.
This study examines the full cost and true value of Cassandra self-managed on Google Cloud (GCP) and the cost of a fully managed serverless Cassandra service. We included dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on serverless Cassandra), storage growth (each write operation adds new data) and people cost in our three-year total cost of ownership calculations. People costs take into account that certain capabilities in serverless Cassandra 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.
The proliferation of data creates a lot of risk for many organizations, especially in healthcare organizations where privacy, timeliness, and safety are paramount. A pharmacy benefit management (PBM) company faced the risk of being overwhelmed by these demands and launched a strategic effort to build out a data platform and streaming data engine aligned around a microservices architecture. This report explores the challenges and lessons encountered in the effort.
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 Microsoft on Azure Virtual Machines (VM)
Both are installations of Microsoft SQL Server, and we tested Red Hat Enterprise Linux OS.
Organizations looking for enterprise data warehouses (EDWs) cannot afford to base their evaluation on query price-performance alone. There’s much more to it. You also need capabilities that reduce the time needed for configuration, management, tuning, and other tasks that can take away from valuable time spent on business analytics.
This new whitepaper from McKnight Consulting Group explores factors that can reduce the costs of analytics far beyond performance, such as licensing structure, data storage, support for non-structured data, concurrency scaling, and much more. Download your copy today, and make a well-informed decision when choosing an EDW platform.
Companies rely on analytical databases for insights essential to the company survival and competitive advantage. The cloud can provide improved economics and operational simplicity, but choosing the most performant and cost-effective cloud data analytics solution is critical.
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.
Read the report to learn how Vertica in Eon Mode:
- Achieves best performance in all benchmark tests for scale and concurrency
- Runs the most queries per hour, at every level of scale and concurrency
- Cuts performance costs 45% – 73% over Amazon Redshift
- Slashes performance costs 84% – 92% over the unnamed data cloud platform
We conducted an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited platform to run Microsoft SQL Server based on their individual requirements.
Read the full benchmark report, which provides insight to help IT professionals, DevOps engineers, platform architects and information security practitioners evaluate a Kubernetes platform optimal for running I/O intensive Microsoft SQL Server applications.