Scalegrid

MAY 13, 2020

In this blog post, we explain what Greenplum is, and break down the Greenplum architecture, advantages, major use cases, and how to get started. It’s architecture was specially designed to manage large-scale data warehouses and business intelligence workloads by giving you the ability to spread your data out across a multitude of servers.

Big Data 321

Big Data Database Artificial Intelligence Open Source 321

Dynatrace

OCTOBER 4, 2022

Grail architectural basics. The aforementioned principles have, of course, a major impact on the overall architecture. A data lakehouse addresses these limitations and introduces an entirely new architectural design. This decoupling ensures the openness of data and storage formats, while also preserving data in context.

Artificial Intelligence 275

Artificial Intelligence Analytics Storage Architecture 275

Dynatrace

OCTOBER 4, 2022

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. Unlike data warehouses, however, data is not transformed before landing in storage.

Artificial Intelligence 218

Artificial Intelligence Storage Analytics Government 218

Dynatrace

OCTOBER 4, 2022

Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes. Teams have introduced workarounds to reduce storage costs. Stop worrying about log data ingest and storage — start creating value instead.

Analytics 243

Analytics Artificial Intelligence Storage Serverless 243

Dynatrace

JANUARY 22, 2024

Artificial intelligence adoption is on the rise everywhere—throughout industries and in businesses of all sizes. Data lakehouses combine a data lake’s flexible storage with a data warehouse’s fast performance. Further, not every business uses AI in the same way or for the same reasons.

Artificial Intelligence 216

Artificial Intelligence Healthcare Retail Government 216

Dynatrace

DECEMBER 15, 2022

As more organizations adopt cloud-native technologies, traditional approaches to IT operations have been evolving. Complex cloud computing environments are increasingly replacing traditional data centers. The importance of ITOps cannot be overstated, especially as organizations adopt more cloud-native technologies.

Artificial Intelligence 211

Artificial Intelligence DevOps Hardware Virtualization 211

KeyCDN

FEBRUARY 22, 2023

Artificial intelligence and machine learning Artificial intelligence (AI) and machine learning (ML) are becoming more prevalent in web development, with many companies and developers looking to integrate these technologies into their websites and web applications. Source: web.dev 2.

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Artificial Intelligence Development Serverless Website 111

Dynatrace

DECEMBER 22, 2021

Metrics are measures of critical system values, such as CPU utilization or average write latency to persistent storage. They are particularly important in distributed systems, such as microservices architectures. Observability platforms are becoming essential as the complexity of cloud-native architectures increases.

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Artificial Intelligence Metrics Architecture DevOps 186

Dynatrace

NOVEMBER 15, 2021

Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. Driving this growth is the increasing adoption of hyperscale cloud providers (AWS, Azure, and GCP) and containerized microservices running on Kubernetes.

Analytics 218

Analytics Monitoring Artificial Intelligence DevOps 218

Dynatrace

JANUARY 23, 2024

Companies now recognize that technologies such as AI and cloud services have become mandatory to compete successfully. According to the recent Dynatrace report, “ The state of AI 2024 ,” 83% of technology leaders said AI has become mandatory to keep up with the dynamic nature of cloud environments.

Performance 195

Performance DevOps Innovation Artificial Intelligence 195

Dynatrace

JANUARY 4, 2024

It starts with implementing data governance practices, which set standards and policies for data use and management in areas such as quality, security, compliance, storage, stewardship, and integration. Modern, cloud-native architectures have many moving parts, and identifying them all is a daunting task with human effort alone.

Government 208

Government Analytics Benchmarking Storage 208

Abhishek Tiwari

DECEMBER 27, 2017

With the arrival of new cloud-native tools and platform, ETL is becoming obsolete. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture. Overall, less complexity to deal with.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

ACM Sigarch

DECEMBER 6, 2018

As a result of these different types of usages, a number of interesting research challenges have emerged in the domain of visual computing and artificial intelligence (AI). Architectural research in these areas remains traditionally focused on the analysis of object detection and recognition algorithms (e.g.

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Wireless IoT Analytics Architecture 54

O'Reilly

DECEMBER 2, 2019

What would the world look like if all of our storage was in the cloud, and access to that storage was so fast we didn’t care? If an office can get that kind of bandwidth to my laptop, with adequate guarantees for cloud security, why should we worry about office LANs? I do regular backups, but I know I’m the exception.

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Wireless Serverless IoT Artificial Intelligence 53