From infrastructure and chipsets to data solutions and development tools, it’s clear that artificial intelligence will play a massive role in the future of the cloud. Read Justin Ryburn’s latest recap to learn about many of the exciting unveilings at Google Cloud Next 2024.

The biggest driver behind the Internet of Things (IoT) is the value that can be unlocked through data analytics. When you have hundreds, thousands, or millions of devices capable of reporting on an almost unlimited number of attributes, statuses, or environmental factors, the opportunity for businesses to create optimisations or brand new revenue streams is huge.

AWS’ Relational Database Service (RDS) is a pivotal component for businesses leveraging cloud technology for their database management needs. RDS simplifies a relational database’s setup, operation, and scaling, offering cost-efficiency while automating time-consuming administration tasks like hardware provisioning, database setup, patching, and backups. However, despite its benefits, navigating the cost structure of RDS to ensure efficient spending can be a complex task.

Document retention is a critical component of modern data governance, requiring meticulous strategy and precise execution. In today’s digital age, businesses are inundated with vast quantities of data, making effective document management not just advisable but essential. This is particularly true when considering the legal and compliance obligations that organizations must navigate.

As businesses increasingly migrate to the cloud, understanding the intersections of data transfer expenses is very important. In this article, we’ll break down what Azure Data Transfer Costs entail, explore various types of data transfers, explore Azure Bandwidth pricing, identify the possible factors influencing data transfer costs, and some strategies for optimizing expenses.

Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources. Spark leverages in-memory and local disk caching, along with Apache Spark is an open-source, distributed application framework designed to run big data workloads at a much faster rate than Hadoop and with fewer resources.

In today’s fast-evolving digital landscape, the importance of robust and scalable cloud storage solutions cannot be overstated. Enterprises across the globe are increasingly leveraging cloud technologies to drive innovation, streamline operations, and reduce costs. Microsoft Azure, as a leading provider of cloud services, continues to enhance its storage solutions to meet the diverse and growing needs of its users.

Access to critical business data, such as cloud cost data, is both incredibly important for finance, engineering, and DevOps teams, but also a privilege that should be managed carefully. This month, our engineering teams put a major focus on developing industry-leading capabilities related to access control, while also making it easier for people who require certain kinds of data to get it where it needs to be.

After watching part one and part two of our FinOps Automation Series, hopefully you’re chomping at the bit to adopt your first automation solution. In part three below, our experts — Erik Peterson (CTO and Founder of CloudZero), Alan Hand (Founder and CEO of Xosphere), Steven O’Dwyer (Senior FinOps Specialist at ProsperOps), and Mike Fuller (CTO of the FinOps Foundation) — are going to walk you through what you need to know before you take any concrete steps.

Mattermost has embarked on a transformative journey in cloud optimization. This journey is marked by strategic initiatives, innovative approaches, and valuable lessons, all aimed at enhancing efficiency and reducing costs. This blog post explores the successful strategies that have guided our cloud optimization efforts. It also highlights our future direction with an emphasis on ARM/Graviton workloads and shares insights from our experiences, particularly regarding spot instances.