novatechflow | Alexander Alten

Hey followers, I often get ask how to get Apache Wayang ( https://wayang.apache.org ) up and running without having a full big data processing system behind. We heard you, we built a full fledged docker container, called BDE (Blossom Development Environment), which is basically Wayang. Here's the repo:  https://github.com/databloom-ai/BDE I made a short screencast how to get it running with Docker on OSX, and we also have made two hands-on videos to explain the first steps. Let's start with the basics - Docker. Get the whole platform with: docker pull ghcr.io/databloom-ai/bde:main At the end the Jupyter notebook address is shown, control-click on it (OS X); the browser should open and login you automatically: Voila - done. You have now a full working Wayang environment, we prepared three notebooks to make it more easy to dive into. Watch our development tutorial video (part 1) to get a better understanding what Wayang can do, and what not. Click the video below: 

When it comes to Federated Learning frameworks we typically find two leading open source projects - Apache Wayang [2] (invented by  Scalytics ) and Flower [3] (invented by  Adap ). And at the first view both frameworks seem to do the same. But, as usual, the 2nd view tells another story. How does Flower differ from Wayang? Flower is a federated learning system, written in Python and supports a large number of training and AI frameworks. The beauty of Flower is the strategy concept [4]; the data scientist can define which and how a dedicated framework is used. Flower delivers the model to the desired framework and watches the execution, gets the calculations back and starts the next cycle. That makes Federated Learning in Python easy, but also limits the use at the same time to platforms supported by Python.  Flower has, as far as I could see, no data query optimizer; an optimizer understands the code and splits the model into smaller pieces to use multiple frameworks at t...

This article introduces infinimesh as a Kubernetes-native IoT platform built to integrate massive device fleets without cloud lock-in, and highlights its expanding plugin ecosystem for Elastic, Redis TimeSeries, SAP HANA, Snowflake and cloud-native object storage. Using lightweight Go-based Docker containers, plugins can run securely in user environments—even on AWS free-tier instances—while streaming device data directly into existing infrastructures like S3 or MinIO. The step-by-step example shows how quickly CloudConnect can move IoT data into object storage using docker-compose, with the plugin internally batching device payloads through Redis before exporting them as CSV. In benchmarking, the architecture handled millions of devices sending frequent JSON updates, demonstrating how infinimesh plugins simplify large-scale IoT data integration with minimal resource overhead. First, a short introduction to infinimesh , an Internet of Things (IoT) platform which runs completely in Kub...

I have a lot of embedded systems running in my product testing lab or at home, all of them either as Raspberries or self-made PCB with Yocto. Sometimes I can't reboot some systems, I think its the journald which causes some issues with SSD Cards, the error-message usually is: Failed to open /dev/initctl Anyhow, if you have this issue - a reboot can be force-forced: systemctl --force --force reboot Since a forced reboot does not sync the journal, the system typically comes up with a damaged FS. The remote fsck can be initiated by extending the command above with sudo tune2fs -i 1m /dev/DISK && touch /forcefsck && systemctl --force --force reboot (assumed you have access to a shell, via SSH or local access). When all goes fine, the system comes up with a clean FS. All this fuss comes from the SSD r/w actions, a well designed IoT embedded devices should have a flash mem part for the running OS.

"How to delete all pods with a status like Error" - that is a question I often get. The DevOps answer is a one-liner, filtering by status.reason and the status we want to query, es example: kubectl get po -A --all-namespaces -o json | jq '.items[] | select(.status.reason!=null) | select(.status.reason | contains("Evicted")) | "kubectl delete po \(.metadata.name) -n \(.metadata.namespace)"' | xargs -n 1 bash -c In that case we delete all pods with the Status "Evicted". But it works for every status based filtering. 

Since my 3 years old Macbook Pro 15inch got the flickering bug I decided to buy a Dell laptop. Why? I have a really old Dell laptop from 2004, and works perfectly as a OpenBSD firewall and internet router in my weekend house. After I got my machine, first thing was to install PopOS. Or better, I tried . It never worked, I think it's a bug in the loader section of PopOS, a bugreport is open. After some hours of hacking and fixing I got a bit bored and used a Ubuntu installation, which worked out of the box perfectly. Yes I know I had could bought the XPS at Dell with Ubuntu on it. But I did not want to ;) Here are some tricks to get the XPS smoothly running - right now I have the book attached to my curved 4k monitor and code some infrastructure stuff for infinimesh . 1. Bluetooth mouse First time a BT mouse is connected it lags. Moving the mouse, the pointer follows after seconds, slow and unresponsive.  Fix by sudo vi /var/lib/bluetooth/<MAC Adapter>/<MAC mouse> ...

This article explores how increasingly intelligent IoT devices are evolving into self-organizing mesh networks, where data flows continuously between sensors, actors, wearables and mobile devices without depending entirely on the cloud. Such ultra-dense, real-time communication creates data volumes and latency requirements that traditional Big Data tools like Hadoop cannot meet. The piece argues that meaningful IoT value arises only when stream processing at the edge, schema-driven data lakes, and scalable cloud components work together. It warns against cloud lock-in, highlights the importance of open standards, and stresses that IoT data must be governed with strong ethics and clear revenue intent—not treated as an unfocused collection exercise. As industries move toward fully interconnected systems, future CIOs must balance innovation with risk, designing architectures that use data responsibly while enabling new business models previously unimaginable. IoT devices are getting mo...

The article examines how the current Internet remains fragile due to centralized control and why Web3, distributed ledgers and Industrial IoT technologies enable a decentralized, device centric architecture. It explains how billions of machines can act as active network nodes, holding state, verifying identity and coordinating operations without intermediaries. It also outlines how trusted data pipelines, machine wallets and decentralized coordination frameworks will lead to resilient industrial systems and new autonomous machine economies. Web3, IIoT and the Next Internet of Autonomous Machines The early Internet succeeded because it offered open protocols, global reach and interoperability. Over time, however, the operational layer became dominated by a few cloud and platform providers. Outages in critical services illustrated the structural weakness of relying on centralized points for identity, storage and coordination. The next version of the Internet will not rel...