WHY IT MATTERS: Digital Transformation

APM tools have become powerful analytics platforms that ingest, analyze and build context from traces, metrics and logs. Infrastructure and operations leaders choosing APM software must weigh these functions against core APM capabilities and the automation being provided by new and existing vendors.

Read GigaOm Radar for Cloud Observability, 2021 and discover how an observability solution can help you:
- Achieve higher application uptime
- Provide better user experiences
- Empower more productive engineering teams

Compare Grafana and New Relic on market position, pricing, and core strengths.

IT infrastructure monitoring (ITIM) tools help tackle these new challenges and offer visibility across hybrid infrastructures and emerging architectures. ITIM tools capture health and resource utilization of IT systems — providing real-time insight.
Use this Market Guide to:
- Learn why organizations need to adapt to hybrid, modular, dynamic and cloud-based environments.
- Learn how I&O leaders can best improve business outcomes.
Compare various IT infrastructure monitoring tool vendors.

An agile approach to overhauling data architecture improves speed, flexibility, and innovation.

This frequently-updated page contains real-life examples and diagrams from Upsolver customers who are using AWS data lakes. While the Upsolver platform itself is a key component in all of these architectures, we hope even non-customers can learn from these examples when it comes to the use cases, considerations, and design patterns that go into a typical cloud data lake.

In this article, we’ll present seven of the key best practices you should adhere to when designing, implementing and operationalizing your data lake. These are things that will mostly be automatically implemented if you’re using Upsolver, but if you’re building your data lake ‘manually’ you’ll need to ensure they’re done correctly if you want to get the most value out of your data lake architecture.
While we will mostly be referring to Amazon S3 as the storage layer, these best practices would also apply for a data lake built on a different cloud or using Hadoop on-premise.

Data lakes are key to scalable, open data architectures – but they can pose challenges to data engineering teams. Efficient data integration is the difference between a bunch of log files sitting in Amazon S3 or Azure Storage and a high-performance data lake that provides real value to analytics, data science and engineering teams.
In this guide we will cover some of the essential best practices and guidelines on how to make your data lake analytics-ready using cloud data processing and integration tools.

While CDP vendors may claim to do everything, the fact is they can’t.

We need to be able to fit new concepts and components neatly into existing architecture—to adapt to changes without disruption.

At first glance, the data fabric and the data mesh concepts sound quite similar. Meshes are often made from a type of fabric, after all, and they are both malleable items that can be lain atop things–in this case, your IT systems that are subject to the ever-growing data crush.

But there are fundamental differences to these two approaches, so it’s worth taking some time to learn their differences.

There are many tools available for doing large-scale data analysis, and picking the right one for a given job is critical. Here, we take an in-depth analysis of the architecture and performance characteristics of a completely serverless data processing platform. While the approach isn’t applicable for every use case, it has a very low total cost of ownership (TCO) and because AWS Lambda allows us to run arbitrary code easily, this approach provides the flexibility to handle non-standard data formats easily.

Where should I run my stuff on Google Cloud? #GCPSketchnote

Demand for APIs, fueled by new business models, application development trends and the urgency of modernization, is directing software engineering leaders to the diverse, evolving market for full life cycle API management products. This research assesses 17 vendors to help you make the right choice.

The CNCF Cloud Native Landscape Project is intended to be a resource map to help enterprises and developers through the previously uncharted terrain of cloud native technologies.

The cloud native landscape (png, pdf), serverless landscape (png, pdf), and member landscape (png, pdf) are dynamically generated from github repositories.

The ERP for product-centric enterprises market is accelerating its transition to cloud deployment models. ERP application leaders should use this Magic Quadrant to evaluate cloud ERP application suites as part of a composable ERP strategy.

There is no doubt that the cloud is one of the most significant platform shifts in the history of computing. Now, there is a growing awareness of the long-term cost implications of cloud. As the cost of cloud starts to contribute significantly to the total cost of revenue (COR) or cost of goods sold (COGS), some companies have taken the dramatic step of “repatriating” the majority of workloads (as in the example of Dropbox) or in other cases adopting a hybrid approach (as with CrowdStrike and Zscaler). Those who have done this have reported significant cost savings: In 2017, Dropbox detailed in its S-1 a whopping $75M in cumulative savings over the two years prior to IPO due to their infrastructure optimization overhaul, the majority of which entailed repatriating workloads from public cloud.

Companies should explore seven specific drivers to assess where the value lies in cloud. As companies assess the opportunities enabled by cloud, a detailed review of the sources of value can pinpoint where they need to focus their attention, people, and resources. Across the three dimensions, seven drivers of value can collectively generate more than $1 trillion in value.

The next evolution of AWS Single Sign-On enables enterprises that use Azure AD to leverage their existing identity store with AWS Single Sign-On. Additionally, automatic synchronization of user identities, and groups, from Azure AD is also supported.

This guide is by and for engineers who use AWS. It aims to be a useful, living reference that consolidates links, tips, gotchas, and best practices. It arose from discussion and editing over beers by several engineers who have used AWS extensively.