Throughout the week, I read a lot of blog-posts, articles, and so forth, that has to do with things that interest me:
- data science
- data in general
- distributed computing
- SQL Server
- transactions (both db as well as non db)
- and other “stuff”
This blog-post is the “roundup” of the things that have been most interesting to me, for the week just ending.
- Helios: hyperscale indexing for the cloud & edge – part 1. In this post Adrian from the morning paper dissects a white-paper about Helios. Helios is a distributed, highly-scalable system used at Microsoft for flexible ingestion, indexing, and aggregation of large streams of real-time data that is designed to plug into relational engines. Adrian is as thorough as usual, and the conclusions he draws are very interesting. I can’t wait for part 2.
- How I am learning distributed systems. This post looks, from one person’s perspective, how one can learn to design distributed systems. What is interesting in this post is the use of Raft, (no, not Raft the game - but the consensus algorithm), as a learning tool. I will definitely point to this post as a learning resource for my developers.
- NGINX Steps into the Service Mesh Fray Promising a Simpler Alternative. The post linked to here points discusses how NGINX introduces its own service mesh: NGINX Service Mesh, (NSM). It promises to be less complicated than ISTIO, so I will definitely have a look.
- Preparing Your Clients and Tools for KIP-500: ZooKeeper Removal from Apache Kafka. The Kafka community has for quite a while been talking about removing the dependency of ZooKeeper, (ZK), from Kafka, and it seems we are getting closer. In the post I have linked to here, the author looks at what is needed to do in Kafka consumers so that nothing “bad” happens when ZK is eventually removed.
- Streaming Machine Learning with Kafka-native Model Deployment. Kafka is used more and more for real-time machine learning purposes, and we are moving towards Kafka as a native streaming model server. This blog post explores the architectures and trade-offs between various options for model deployment with Kafka.
That’s all for this week. I hope you enjoy what I did put together. If you have ideas for what to cover, please comment on this post or ping me.