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.

With 34% of cloud developers facing difficulties when calculating how much their cloud provider is going to charge them each month, it is essential to prioritize cloud cost management. This concept surrounds the process of monitoring, controlling, and optimizing an organization’s cloud service spend. By having the correct cloud cost management strategies in place, organizations are able to eliminate unnecessary expenses through optimizing resource allocation and cost-saving strategies.

As companies start their Kubernetes and cloud-native journey, cloud infrastructures and services grow at a rapid pace. This happens all too often as organizations shift left without thorough controls, which can lead to overallocating and overspending on their Kubernetes environments. Organizations running workloads in the cloud can put budgets at risk when they lack information about key facts.

You need a robust platform to speed up your read/write operations per second if you provide services such as live streaming, social media networking, and analytics. By storing frequently accessed objects in faster memory (RAM or in-memory data stores instead of slower disk-based storage), caching reduces latency. Amazon ElastiCache is a managed, in-memory data store and caching service in one.

Repatriation in cloud computing refers to moving workloads from the public cloud to on-premise infrastructure. Sarah Wang and Martin Casado from Andreessen Horowitz have written one of the most popular articles about repatriation: they explain the motivation with the significant cost savings possible. For software-based businesses, public cloud spend can rise to 50% of the cost of revenue (COR). Reducing these costs has the potential for significant margin increases.

Databricks has emerged as one of the most powerful solutions for organizations to sort and normalize the data they ingest. But like all cloud providers — from the big three to more specialized infrastructure vendors — it adds complexity to customers’ IT spend. This week, we announced support for Databricks on the CloudZero Platform.

We see it often. Too many companies struggle to optimize their cloud costs — whichever platform they use, from Microsoft Azure to Amazon Web Services (AWS) and Google Cloud Platform (GCP). So, in this guide, we are sharing over 20 Azure cost optimization best practices you can apply right away — all in a calorie-free, snackable guide you can read in less than a coffee break.

Most customers running Kubernetes clusters Amazon EKS are regularly looking for ways to better understand and control their costs. While EKS simplifies Kubernetes operations tasks, customers also want to understand the cost drivers for containerized applications running on EKS and best practices for controlling costs. Anodot has collaborated with Amazon Web Services (AWS) to address these needs and share best practices on optimizing Amazon EKS costs.

MongoDB has evolved into the database solution of choice for developers looking to build efficient, scalable applications in the cloud. But, as when using any cloud-based infrastructure, using MongoDB introduces an additional layer of complexity to customers’ IT spend. This week, we announced support for MongoDB on the CloudZero Platform.

Like any business, management service provider firms are always looking for ways to reduce MSP costs and improve profits. MSPs provide critical services to their clients, so finding ways to cut costs without cutting corners is essential. You’ve probably heard all the standard advice on cutting costs: review your pricing, renegotiate contracts, cut unnecessary expenses, and so on. But what about MSPs who are already doing all those things and are still looking for ways to save?

In the realm of big data, Hive is a big deal. Well-written and well-designed Hive queries accelerate data retrieval from datasets. Hive is much better than SQL as the former works with complicated data more effectively. In addition, Hive queries help bring down processing costs. This is why it’s critical to write and optimize Hive queries correctly for big data analytics users and developers.

In the early days of SaaS companies, most engineering and cloud operations teams weren’t tasked with monitoring or optimizing cloud costs. In fact, it would have been unlikely for these teams to care about cloud cost optimization at all, let alone take measures to fix issues and look for opportunities. Today, SaaS companies that want to secure a competitive foothold and ensure long-term success have to care about cloud costs.

Amazon’s Elastic Compute Cloud (EC2) service provides a variety of virtual machines (instances) for compute workloads. Among those, Amazon EC2 M5 instances are among the most versatile. In addition, there are different types of M5 instances available. Each of these has its own best use case. In this guide, we share an in-depth look at M5 instance types, sizes, and when to use them.

Many companies are rapidly adopting cloud-native computing services, like containers, microservices, and serverless computing. Unlike monolithic applications, these technologies rely on distributed architectures. Whether you are running them in the cloud, on-premises, or both, distributed systems consist of thousands or millions of processes and components. The challenge now is to make these complex systems' inner workings visible, controllable, and improvable.

For IT and EUC teams, reducing costs is easier said than done. You can’t just blindly reduce headcount, delay transformation projects, or extend hardware lifecycles in the hopes of appeasing your CFO’s demands for short-term cost reduction. The reality is, rushed cost-cutting will put your service desk under pressure. In turn, such self-inflicted inefficiencies will only result in performance degradations, additional tickets, and escalations.

With organizations across industries facing inflationary pressures and the threat of recession, CFOs are forced to impose cost-cutting measures to improve cost efficiency across teams to tide through these tough times. This has resulted in IT teams being faced with a dilemma. How to improve the cost-effectiveness of the team without impacting employee satisfaction and service desk productivity?

“Perfect tagging” is a little bit like perfectly tuning a piano. It takes an enormous amount of manual effort to get right, it’s never truly perfect, and the slightest change in the surrounding environment can throw it out of harmony. The closer an organization wants to get to perfect tagging, the more engineering resources they have to invest in it — frequently at the expense of more impactful, innovation- and product-oriented projects.

FP&A tools can help your business assess, plan, and budget business activities more effectively. The tools help by automating a lot of the work involved, helping you tell where you could be losing money, and revealing additional revenue streams you can capitalize on. At a time when SaaS competition is at an all-time high, you need all the advantages at your disposal to deliver the most value at the least cost.