Over the last few years, more organizations have switched from relying solely on one cloud service provider (CSP) to several. Among the primary reasons for the change are minimizing dependence on a single CSP, preventing vendor lock-in, and providing greater flexibility. Recent trends include using a mix of cloud providers to take advantage of the cost savings several CSPs offer, and using best-of-breed services for different applications, teams, or departments.

As a leader in observability and application performance monitoring (APM), New Relic empowers engineers with a data-driven approach to planning, building, deploying, and running great software. Last month, we announced support for New Relic on the CloudZero platform. With this new functionality, customers can gain visibility into their New Relic spend, combine it with any other IT or infrastructure spend, and achieve a complete view of business dimensions — such as products and customers.

FinOps, or the practice of managing a company’s cloud computing costs, and DevOps, the process of efficiently building applications in the cloud, should ideally go hand-in-hand. Engineers with a streamlined development process and a cost-conscious mindset can produce functional products with great margins and a high potential for scalability. However, incorporating FinOps into the DevOps lifecycle, and vice versa, isn’t always easy.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

There are almost as many reasons for learning FinOps practices as there are resources available to learn from. Maybe you operate a SaaS company, for example, or you're looking for a job in FinOps. The FinOps field grows in importance every time cloud services and their associated costs become more complex — which can feel like every single day. Regardless of your situation, you’ll need high-quality FinOps resources that offer up-to-date materials and in-depth information.

As technology improves and customer demands change, many companies must provide fast, engaging, and secure services. Developers rely on platforms that support sub-millisecond response times to power these capabilities in their applications. Amazon ElastiCache and Redis are two of the most popular platforms for building fast, highly available, and scalable data stores.

As per the latest forecast by Gartner, worldwide IT spending is projected to amount to $4.6 trillion in 2023, up by 5.1% from 2022. The demand for IT will be strong in 2023 as enterprises launch digital business initiatives to respond to global economic challenges. In a downward economy, conventional wisdom warrants reducing costs.

Microsoft Azure was a relative latecomer to the public cloud market: AWS preempted everyone in 2006, and Google GCP came next, in 2008. By the time Azure launched in 2010, the public cloud market had already grown to more than $24 billion. Despite being third to the party, Azure had a key advantage: The Microsoft Office Suite was (and is) a go-to choice for corporate collaboration.

For data professionals interested in data modeling, artificial intelligence, and machine learning, Databricks offers a powerful cloud data platform. The Databricks Lakehouse combines data lake and data warehouse capabilities in one architecture. This makes Databricks particularly suitable for building sophisticated data analytics, Artificial Intelligence (AI), and Machine Learning (ML) models. Plus, Databricks claims its lakehouse is up to 12X cheaper than traditional alternatives.

Are your Apache Impala queries running slow and not achieving peak performance? Given Impala’s complexity, troubleshooting can be very difficult. Optimizing query performance is near impossible without the right tools. Good news: Pepperdata Query Spotlight now supports Apache Impala.