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.

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.

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.

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.

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.

“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.

Yet, the same flexibility that makes EC2 so appealing can also make it complex, confusing, and unnecessarily costly. A good way to understand the compute service is to familiarize yourself with EC2 instance types, and what the best use cases are for each. This guide will cover that and more.