WHY IT MATTERS: Digital Transformation

We surveyed hundreds of DevOps practitioners about their monitoring and observability challenges for delivering modern cloud applications. We summarized our findings in the State of Observability 2021 report.

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.

Emirates NBD’s Neeraj Makin and Saud Al Dhawyani discuss how APIs became central to their organization’s IT transformation.

Today, HubSpot announced a milestone of more than 1,000 integrated apps featured in the app marketplace for its CRM platform. The call to better integrate martech is being heard. As you may know, I joined HubSpot a little over four years ago to help grow its platform ecosystem. So I'm delighted to see this momentum build. And I feel like it's still in the early stages of what it will become. A huge 'thank you'

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.

12 technology trends that will help shape the future of digital business.

There's been some interesting debate lately about CDPs vs. CDWs — customer data platforms vs. cloud data warehouses. If CDWs such as Snowflake, Databricks, AWS Redshift, etc., are becoming a universal data layer in companies, is there still a role for domain-specific data platforms, such as marketing-controlled CDPs? The short answer, in my opinion, is 'Yes, but it depends…' There is still a significant distinction between live business operations systems that run in real-time serving

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.

Less than 20% of retail sales occur online today, and the proportion of retail sales happening online is expected to be 21% in 2023*. Shoppers indicate that they want engaged in-store experiences with deep personalization and smooth transitions between online and physical stores – an omnichannel experience. The retail store is highly relevant for many shoppers as it affords a visceral experience and provides instant gratification on a purchase impulse. The ability to touch and feel a product before buying and taking it home are key factors for many shoppers – as indicated by the fact that just 5% of grocery sales occur online. While the retail store is here to stay, the nature of shopper-seller interactions is undergoing profound change.

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.

Enterprises have significantly accelerated their use of public CIPS providers as they seek to scale and become more agile in the postpandemic period. Major differences between the providers warrant careful consideration as I&O leaders make strategic cloud provider selections.