APX is a Continuous Reliability platform built to proactively detect and resolve production issues that easily cost your team 30-40% of their engineering time. It seamlessly integrates with your existing tools, such as GitHub, Datadog, Sentry, and Postgres, providing fixes before problems lead to major production incidents. In this test,
Datadog cost surprises are more common than you'd think. I’ve written about how Datadog’s defaults can quietly spiral out of control, like when a GKE Autopilot user saw their Datadog bill increase 20x due to unexpected Prometheus scraping. The root cause? Overlapping defaults that not even
Spending on observability platforms has gotten out of control. It is often the number two cost behind a company’s main cloud vendor, representing up to 25% of the engineering team’s budget. I saw this firsthand when I was part of the engineering team at Coinbase, where I built
Imagine opening your Datadog bill and seeing a 20x increase. You dig deeper and discover thousands of “custom metrics” flooding into Datadog—metrics you didn’t configure, expect, or even recognize. That’s exactly what happened to a GCP user running workloads on GKE Autopilot. I’ve said this before,
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Migrating from SQL Server to Postgres is a significant move for organizations looking to reduce costs, improve scalability, and future-proof their data infrastructure. But database migrations are costly and time consuming. They frequently run overtime, carry significant business risks, and are a significant opportunity cost. So if you’re going
Snowflake vs. Databricks: who’s better for your company’s AI initiatives? Competition between the two has been heating up for years, and their recent announcements at the 2024 Snowflake Summit and Databricks’ Data+AI conference reflect the intensifying shift towards enabling AI capabilities for their users. It's
In this post, we explore the limits of single-node Postgres and what optimizations can extend its usefulness as a primary application database before requiring more advanced scaling solutions. Monolithic ecommerce app on RDS PostgreSQL Let's use a real-world scenario, a monolith web application that uses PostgreSQL as the
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What's missing from APMs? Sales are dropping on your ecommerce shop. Customers are getting sluggish response times when they're viewing products for sale. As a backend engineer supporting your Ruby on Rails and PostgreSQL ecommerce platform, you spring into action using your available tools. Your primary
Having discussed why Postgres performance tuning matters, and how to write better queries to optimize performance, it’s time to look beyond the basics towards more advanced techniques in Postgres performance tuning and monitoring. I’ll share how you can use tools like pg_stat_statements for query analysis, implement
In my previous article, I discussed why Postgres performance tuning matters, warning signs you should pay attention to know if your Postgres database is struggling, and 8 parameters you can try to improve its performance and see results immediately. Today, we’ll take a closer look at Postgres query optimization
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In our work as seasoned database specialists, we often approach Postgres performance tuning with an eye towards how we can best tune a range of configuration parameters to suit a specific workload and hardware setup. But it’s not always obvious why you should start here, where you should start,
By examining real-world scenarios encountered at Coinbase and Nextdoor, this post presents a technical exploration of these challenges, offering guidance to teams seeking to enhance their database operations.