Artificial Intelligence Big Data Storage 
Scalegrid
MAY 13, 2020
High performance, query optimization, open source and polymorphic data storage are the major Greenplum advantages. When handling large amounts of complex data, or big data, chances are that your main machine might start getting crushed by all of the data it has to process in order to produce your analytics results.
Dynatrace
OCTOBER 4, 2022
A data lakehouse features the flexibility and cost-efficiency of a data lake with the contextual and high-speed querying capabilities of a data warehouse. Data warehouses offer a single storage repository for structured data and provide a source of truth for organizations. How does a data lakehouse work?
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Dynatrace
OCTOBER 4, 2022
Teams have introduced workarounds to reduce storage costs. Additionally, efforts such as lowered data retention times, two-tiered storage systems, shaky index management, sampled data, and data pipelines reduce the overall amount of stored data. Dynatrace discovers logs automatically at scale.
Scalegrid
FEBRUARY 8, 2024
A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.
DZone
JUNE 4, 2019
NVMe Storage Use Cases. NVMe storage's strong performance, combined with the capacity and data availability benefits of shared NVMe storage over local SSD, makes it a strong solution for AI/ML infrastructures of any size. There are several AI/ML focused use cases to highlight.
Dynatrace
DECEMBER 15, 2022
Besides the traditional system hardware, storage, routers, and software, ITOps also includes virtual components of the network and cloud infrastructure. AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations.
DZone
MAY 30, 2019
With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous. This has given rise to a completely new set of computing workloads for Machine Learning which drives Artificial Intelligence applications.
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