Operations | Monitoring | ITSM | DevOps | Cloud

Breaking Silos: Pairing InfluxDB 3 with Your Historian for Better Insights

Industrial systems constantly generate time series data—streams of time-stamped values like temperature, flow rate, vibration, or power load. This data powers real-time monitoring, performance tracking, and long-term forecasting across critical infrastructure, energy systems, and manufacturing environments.

The Gold Standard, Evolved: What's New with PostgreSQL and Why It's Still Your Go-To Database

PostgreSQL, widely regarded as the world’s most advanced open-source relational database, continues to set the standard for performance, scalability, and flexibility in modern data infrastructure. While it has always been a top contender, each new release of PostgreSQL introduces powerful features and enhancements that make it an even more compelling choice for developers, database administrators, and IT leaders alike.

7 Benefits of Hiring a Power BI Consultant for Your Data Projects

Organizations swimming in data yet starving for insights face a common challenge - translating numbers into meaningful business intelligence. Microsoft Power BI offers tremendous capabilities for visualization and analysis, but unlocking its full potential requires more than just software installation. The difference between mediocre dashboards and transformative analytics often comes down to expertise.

Why Reverse ETL Software Is Essential for Modern Analytics

In an era where everything depends upon data, businesses are always searching for ways to run their operations and decision-making more effectively. This is where reverse ETL (extract, transform, load) software has become a key element. Reverse ETL software enables data to flow from data warehouses back to operational systems, which means insights can be integrated with everyday processes more effectively. In this post, you can read about why reverse ETL software is important for modern analytics.

PostgreSQL extensions you need to know in 2025

PostgreSQL is by design lightweight and un-opinionated but its killer feature has long been its extensions ecosystem. The extensions ecosystem adapts and customizes PostgreSQL data storage and manipulation use cases, making it suitable for AI, analytics, document data stores and more. This flexibility keeps PostgreSQL viable as an option for any business or startup, as it’s hard to ‘outgrow’ PostgreSQL.

Forecasting with InfluxDB 3 and HuggingFace

Machine learning models must do more than make accurate predictions; they also need to adapt as the world around them changes. In real-world systems, data distributions shift due to seasonality, equipment wear, user behavior changes, or other external forces. If your models can’t keep up, the result is poor predictions. This can lead to outages, inefficiencies, or missed opportunities. That’s why forecasting systems need to be monitored and resilient, not just accurate.

Real-Time, Automated Resource Optimization for Kubernetes Workloads

Struggling with underutilized Kubernetes resources or rising cloud costs? Learn how Pepperdata Capacity Optimizer delivers real-time, automated resource optimization for Kubernetes and Amazon EMR workloads—helping teams reduce costs and boost performance without manual tuning. In this video, discover how Pepperdata helps DevOps, platform engineers, and FinOps teams.

Pepperdata In Collaboration with AWS | Optimize Utilization and Cost for Kubernetes Workloads

In this AWS Startup Partner Spotlight, discover how Pepperdata empowers cloud-native startups to optimize their Kubernetes and Amazon EMR workloads in real time. With automated resource optimization, companies can reduce costs by an average of 30% while increasing utilization by up to 80%—without any manual tuning. Whether you're scaling rapidly or managing unpredictable workloads, Pepperdata ensures your infrastructure runs efficiently and cost-effectively from day one.

Why Manual Tuning Fails: A Better Way to Optimize Kubernetes Workloads

As a data platform engineer, you’re tasked with running complex workloads—Apache Spark jobs, AI/ML pipelines, batch ETL—across dynamic Kubernetes environments. Performance matters. Time spent tuning matters. And so does cost. But if you’re still relying on manual resource tuning to optimize your workloads, you’re playing a losing game. Sure, you can tweak CPU and memory requests by hand. You can comb through Prometheus metrics, look at job logs, estimate peaks.