Bylines
The latest articles published by Hydrolix
Bylines
Observability Isn’t Enough. It’s Time To Federate Log Data
Over the last decade, observability has gone from being a buzzword to a best practice. And enterprises are reaping the benefits with faster mean time to resolution (MTTR), better user experience and less downtime.
Observability is now table stakes — customers expect nothing less than smoothly running applications, no matter how big the event. Enterprises looking for a competitive advantage need to find ways to use their observability data for other use cases, not just for compliance and security purposes, but for active analysis, business intelligence (BI) and training machine learning models.
The New Stack
February 4, 2025
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Bylines
Why Observability Needs To Go Headless
Many enterprises generate terabytes of log data every day, resulting in high costs to ingest, store and analyze that data. Even worse, many observability platforms are walled gardens, making it hard to use log data for use cases beyond observability, such as business intelligence, data science and machine learning.
To solve both of these problems, it’s time for headless observability, a fresh approach that decouples the frontend (visualization, querying and analytics) from the backend (data ingestion and storage) — all while keeping operations simple.
The New Stack
February 18, 2025
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Bylines
Your Database Just Got More Expensive (Again). Now What?
Remember when ClickHouse was the fast, cost-efficient darling of analytics? Well, even it recently bumped up pricing by 30%. (Read more.)
Amazon RDS follows a similar pattern. While EC2 prices remain relatively stable, AWS has increased the margins on RDS over time, meaning managed database services get more expensive while raw compute doesn’t. (See analysis.)
And let’s not forget the industry’s collective PTSD from Oracle’s aggressive pricing tactics. NASA, for example, ended up overpaying Oracle by tens of millions due to confusing and predatory license structures. (Case study.)
The New Stack
March 4, 2025
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Bylines
Observability 2.0? Or Just Logs All Over Again?
Lately, there’s been a lot of talk around Observability 2.0 and a broader transformation from the three (or four, depending on who you ask) pillars of observability towards a unified source of truth: arbitrarily wide, structured log events.
If we boil this down, we’re right back to where we started — kind of. In the beginning, there were logs, and now we’ve come full circle, with wider logs (structured, high-dimensionality time series events) and more of them than ever before (plus high cardinality). So, how did we get here? And where exactly are we headed?
The New Stack
May 5, 2025
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Bylines
Understanding the Kubernetes Operator Pattern
Kubernetes is a powerful tool for deploying, managing and scaling software, enabling organizations to ingest, store and analyze log data at petabyte scale. Behind the scenes, enterprises can use the Kubernetes operator pattern to extend Kubernetes and make it easier to automate and build consistency into their Kubernetes infrastructure.
The New Stack
May 14, 2025
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Top 4 Observability Risks
Discarding or sampling data is like throwing out 500 pieces of a 1,000-piece puzzle. You can get an idea of the image in the puzzle, but you can’t see the full picture. You need that full picture to truly take advantage of observability data to predict potential incidents before they happen, and ensure your applications and infrastructure are operating as they should. Otherwise, you risk dangerous consequences.
The New Stack
May 30, 2025
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Bylines
The Problem with Tiered Storage
Frozen storage or various methods of discarding data (such as downsampling) often seem to be the only solutions when the volume of log data keeps going up and when costs are skyrocketing.
In recent years, a new approach that maximizes the performance of object storage provides a much better alternative. It’s now possible for enterprises to use solutions that are built on object storage to keep all their data hot for real-time analytics while remaining cost-effective.
The New Stack
June 6, 2025
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The Obscure Paradox Fueling AI and Big Data Growth
Jevons paradox refers to a phenomenon where the consumption of resources goes up as they become more efficient to use. In the 19th century, the economist William Stanley Jevons noticed that as coal became more efficient to extract, the lower cost led to more demand and more usage. Now this 19th-century paradox is being applied to the explosion in AI usage and productivity. Microsoft CEO Satya Nadella was referring to Jevons paradox when he said, “As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of.”
The New Stack
July 30, 2025
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Bylines
Going for Silver: Making the Most of Tiered Observability
Just a few years ago, many enterprises were working toward consolidating log data into a single observability platform. By bringing all their data together in one platform, users would get unified dashboards and the ability to correlate and analyze all their telemetry data in one place. There’d be no more tool fragmentation or chair swivel. And premium observability solutions offered beautiful user interfaces that made the appeal of consolidation especially tantalizing.
The New Stack
August 28, 2025
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