Why I Switched from Elementary Open-Source to Elementary Cloud

When I first started working with dbt, I was excited by how flexible and powerful it was—but I quickly ran into a problem. While dbt tests gave me some validation, they didn’t provide full visibility, and none of the modern data tools at the time gave me full lineage. I wanted to see everything—from source to BI, to the data consumers relying on it, whether they were BI teams, data scientists, or even product teams. But no tool offered that level of visibility.

I’ve always been an "I can build this on my own" kind of guy. At my previous company, I built a custom observability solution from scratch. I set up metrics tracking and dashboards using Grafana and other open-source tools to gain better visibility into data movement. This worked well in an environment where I had full control over the setup and could fine-tune everything manually. Another downside of this was that it required constant maintenance and became very expensive.

When I moved to a new company, Elementor, and saw that the company was paying for Elementary Cloud. I didn't understand why we need to pay for a tool I can build on my own? I can stick with what I knew—custom-built monitoring using Elementary OSS and Grafana.

The Moment DIY Didn’t Cut It

At first, sticking with Elementary OSS seemed like the right move. But as I started planning a scalable observability setup at Elementor, I hit a wall.

This wasn’t just a dbt workflow anymore—it was massive, with complex systems and integrations and multiple data sources.

The real turning point came when I sat down to map out what it would take to scale observability with my approach. I realized I wasn’t just maintaining a tool—I was building an entire observability framework from scratch.

That meant:

• Writing custom logic to detect anomalies across hundreds of tables
• Managing false positives and fine-tuning every test
• Constantly adapting as new datasets and pipelines were added

I could get away with this approach at my previous company, where things were simpler and based only on Kafka topics, SalesForce, and MySQL DB. But here?

It would require weeks and weeks of work, along with constant tuning and maintenance.

Why Automation Was the Only Way Forward

Every dataset had different patterns, ingestion times, and expected behaviors. There was no way to apply a single rule across everything, so I’d have to define separate thresholds for each table. That wasn’t just inefficient—it was impossible to maintain.

That’s when I realized automated anomaly detection was the key to unlocking the problem.

Instead of my team manually defining thresholds for every dataset, Elementary Cloud uses machine learning to automatically detect spikes, drops, and freshness issues across all business units—without redundant configurations.

Too Many Alerts, Not Enough Action

One challenge I noticed with observability was the sheer volume of alerts being generated. When I first looked at Elementary Cloud, I assumed it was just a managed version of what I already had.

But once I actually started using it at scale, I saw the difference. Instead of an overwhelming flood of alerts that no one acted on, the cloud platform helped surface and manage real issues that mattered.

This helped eliminate noise, ensuring we focused only on issues that actually required investigation.

Getting Analysts and Data Scientists To See The Value

Many analysts and data scientists hesitate to engage with observability because they assume it's too complex. They think anomaly detection requires extensive manual setup, advanced models, or deep engineering effort.

But in reality, Elementary Cloud makes it accessible by automating much of the process. For example, setting up email verification checks was effortless, allowing us to quickly detect unverified emails that would have otherwise gone unnoticed. Similarly, identifying missing values in critical datasets became trivial with automated anomaly detection.

The Unexpected Wins of Elementary Cloud

Beyond anomaly detection, Elementary Cloud also helps us track model performance in ways we hadn’t before. Initially, I was the only one monitoring query performance and model runtimes, but Elementary made it easy for the whole team to identify slow-running models and inefficient queries. This visibility helped us optimize resource usage and prevent cost spikes we would have otherwise missed.

One time, we detected a sudden spike in data volume—it turns out the product team had started delivering more records without informing us. Before Elementary Cloud, we had no way to track volume anomalies across all our ingestion pipelines.

From Builder Mindset to Delivering Value

When I first stuck with OSS, I thought I could build everything myself. But the deeper I went, the more I realized—my team’s job isn’t to build and maintain monitoring tools. Our focus should be on moving fast, delivering insights, and driving value from our data. The time we would have spent scaling an in-house observability solution far outweighed the benefits.

Elementary Cloud already had the automated anomaly detection and scalable monitoring we needed—so we could focus on what actually matters: making data work for the business.

Should You Stick with OSS or Move to Cloud?

For small teams with straightforward dbt models, Elementary OSS is a great choice.

If you’re an early-stage company with a tight engineering team and a clear, controlled data flow, you can absolutely make it work.

But if you’re dealing with:

✅ A large, complex data ecosystem with multiple teams managing different datasets

✅ The need for anomaly detection that scales without having to manually set it up

✅ Alert fatigue and a lack of structured incident management

✅ Data consumers that need to understand who owns the data and its quality

Then Elementary Cloud isn’t just an upgrade—it’s a necessity.

I didn’t switch because I wanted to. I switched because I had to.