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Discover our latest articles about cloud-native technologies, Kubernetes, DevOps, and modern software development. From practical tutorials to in-depth analyses.
Latest Blog Posts
Stay up to date with our latest articles about cloud-native technologies, Kubernetes, and DevOps.
1219 posts
In a modern data engineering platform, storage needs are not only vast but also diverse. We need space for raw sensor data, finished AI models, container images, and backups. Classic file servers (NFS) quickly reach their limits, especially when it comes to parallel access from hundreds of Kubernetes pods.
In an industrial concept, millions of data points are generated daily. When these data flow into Apache Kafka, the next critical question arises: Where do we store them so that engineers and data scientists can efficiently query them? A conventional relational database quickly reaches its limits with billions of rows. Queries over periods of months often take minutes there - unacceptable for interactive dashboards or AI models.
In modern manufacturing, data is generated not in batches, but as a continuous stream. Sensors on rolling mills, flow meters in chemical reactors, and logistics systems produce status messages every second. Those who analyze this data only in nightly batch runs miss the opportunity for immediate reaction—whether in the case of quality deviations or impending machine failures.
In industrial data processing, ETL processes (Extract, Transform, Load) are the nervous system of production. When sensor data from plants worldwide needs to be consolidated, cleaned, and fed into analytical models, a simple cron job is no longer sufficient. In a global industrial corporation, thousands of dependencies must be monitored, errors automatically intercepted, and resources dynamically allocated.
Polycrate API for Teams: Centralized Monitoring, Remote Triggering, and Encryption Key Management
Auditable SSH sessions and CLI activities: Who did what and when – thanks to Polycrate API
Action Runs and Polycrate API: Complete Audit Trail for Every Infrastructure Automation
In industrial AI development, the GPU (Graphics Processing Unit) is the new gold. Whether for training complex neural networks for quality control or for large-scale simulations for energy optimization, projects come to a halt without massive computing power.
In many data engineering teams, starting a new project follows a frustrating pattern: First, Python versions, R libraries, SQL drivers, and CUDA toolkits for GPU usage must be painstakingly configured on the local workstation or a static VM. The result is often the infamous "It works on my machine" syndrome—code that runs locally but fails in production or on a colleague's setup.
Artificial Intelligence is no longer a hype in e-commerce but a tool for scaling. Whether it's generating product descriptions from technical features, rewriting SEO texts, or automating customer support responses, Large Language Models (LLMs) save hundreds of work hours.
In modern e-commerce, data is the foundation for every growth decision. However, traditional client-side tracking is reaching its limits: ad blockers, browser Intelligent Tracking Prevention (ITP), and increasingly strict data protection regulations mean that up to 30% of user data simply does not reach the marketing backend.
In modern e-commerce, the search function is much more than just an input field. It is the most important salesperson in the shop. Users who use the search have a clear purchase intention - but this intention quickly fades if the results take seconds to appear or are irrelevant.
For an online shop in the mid-sized business or D2C sector, downtime is much more than a technical nuisance. Every minute of unavailability means direct revenue loss, decreased customer trust, and wasted budget on ongoing marketing campaigns.
In the early stages of an e-commerce agency, the approach is usually pragmatic: each new client shop gets its own hosting package. One shop with provider A, the next with provider B, the third on a dedicated root server. This works excellently at first, as each project can be quickly and independently launched.
Have you ever experienced this? A user clicks "Generate PDF Export" or "Create Monthly Report" in your SaaS app, and suddenly the loading icon spins for 10, 20, or 30 seconds. In the worst case, the connection times out, or the entire application becomes sluggish for all other users.
When building a modern IT infrastructure today, one faces a strategic decision: either buy into the convenience (and dependency) of large US SaaS monoliths, or build a sovereign platform. However, 'self-hosting' has long sounded like a risky DIY project to many IT managers—characterized by manual updates, security gaps from forgotten patches, and unstable scripts.
In many technical service teams, there is a dangerous pragmatism when it comes to communication: When things need to move quickly on-site or at the machine, private smartphones are used. Photos of defects, location data, and agreements on maintenance protocols end up in WhatsApp groups.
The first productive Polycrate workspace: Checklist and best practices for getting started
The Polycrate Ecosystem: PolyHub, API, MCP, and the Future of Infrastructure Automation
In many companies, the process of digital signing is the last "analog island" or a dangerous compliance breach. Maintenance logs are created digitally but then uploaded to US platforms like DocuSign or Adobe Sign to obtain a signature.
In customer service and technical support, the ticketing system is the central nervous system. Many companies instinctively turn to market leaders like Zendesk. The reasons are understandable: It is quick to set up, offers countless features, and just works.
For a long time, IT infrastructure in B2B sales was a peripheral issue. Companies relied on major US SaaS providers because they were considered the "standard." But the tide has turned: In times of tightened compliance regulations like **NIS-2** or **DORA**, the question of **"where and how" data processing occurs** becomes a decisive factor in contract awards.
Since the breakthrough of ChatGPT, it's clear: AI can do more than just analyze numbers. It can write reports, summarize maintenance instructions, and explain anomalies in human language. Sensor data analysis software uses LLMs to provide technicians on the shop floor with precise instructions: "Vibration at bearing 4 indicates a lack of grease - please re-lubricate by the end of the shift."
In the traditional IT world, things are binary: A server is either running or it's not. A database either responds or throws an error. In the world of machine learning, it's trickier. A model can be technically running perfectly (CPU at 20%, 200 OK status code), yet produce completely incorrect nonsense.
