Enterprises of every size are moving applications and infrastructure to the cloud. Maintaining operational awareness is difficult enough within a single cloud…
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GigaOm Radar for GitOps
In the GigaOm report “ Key Criteria for Evaluating GitOps Solutions,” we defined GitOps as “an approach to supporting continuous delivery that…
Read MoreGigaOm Radar for Extended Detection and Response (XDR) Solutions
Enterprise cybersecurity is typically composed of multiple security solutions from various vendors combined with a security information and event management (SIEM) and/or…
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Relational Databases in the Cloud: Masters of Federation
Relational database management systems (RDBMSes) have been around for a long time, and they are very much still with us. The on-premises…
Watch NowKey Criteria for Evaluating Multifactor Authentication Solutions
Multifactor authentication (MFA) addresses one of the most vulnerable elements in cybersecurity—humans. As most attacks are enabled through social engineering, MFA intervenes…
Read MoreKey Criteria for Evaluating Continuous Vulnerability Management Solutions
Vulnerability management (VM) is a mature component of the cybersecurity ecosystem. It has become a commodity function, an expected part of every…
Read MoreKey Criteria for Evaluating Data Loss Prevention Solutions
Data is at the core of today’s enterprise, and ensuring it’s protected and secure is at the top of its agenda. The…
Read MoreKey Criteria for Evaluating Data Virtualization Solutions
The diversity of the data ecosystem within modern enterprises is one of the most difficult aspects of managing data today. Companies struggle…
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API and Microservices Management Benchmark
Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems…
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Cassandra Total Cost of Ownership Study
This study examines the full cost and true value of self-managed OSS Apache Cassandra® vs DataStax AstraDB fully managed DBaaS in Google…
Read MoreTransactional and Analytical Workloads
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…
Read MoreEnterprise Readiness of Cloud MLOps: A GigaOm Benchmark Report
MLOps is a practice for collaboration between data science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of machine learning…
Read MoreSQL Transaction Processing and Analytic Performance Price-Performance Testing
The fundamental underpinning of an organization is its transactions. They must be done well, with integrity and performance. Not only has transaction…
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NetApp Cloud Insights: A GigaOm Benchmark Report
This report format incorporates a field test/benchmark with a white paper-level paper. The body of the paper provides only a summary of…
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Healthcare Natural Language Processing
Growing data volumes and differences in terminology, language, abbreviations, and even spelling in clinical documents make it challenging for medical professionals to…
Read MoreAPI and Microservices Management Benchmark
Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems…
Read MoreHigh Performance Application Security Testing – Cloud WAF Security Platforms
This free 1-hour webinar from GigaOm Research features analyst Jake Dolezal and will focus on comparing Web Application Firewall (WAF) security platforms…
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High Performance Application Security Testing
Data, web, and application security have evolved dramatically over the past few years. Just as new threats abound, the architecture of applications—how…
Read MoreEnterprise Analytic Solutions 2021
Enterprises from every industry and at every scale are working to leverage data to achieve their strategic objectives—whether those are to become…
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Enterprise Readiness of Cloud MLOps
MLOps is a practice for collaboration between data science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning”…
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