Interesting Stuff - Week 27

Throughout the week, I read a lot of blog-posts, articles, and so forth, that has to do with things that interest me:

This blog-post is the “roundup” of the things that have been most interesting to me, for the week just ending.

Cloud

IP filtering for Event Hubs and Service Bus. A frequently asked for feature in Azure Event Hubs is the ability to restrict access to the Event Hubs to certain well-known sites, alternatively rejecting traffic from specific IP addresses. The Azure team has now announced a public preview of IP filtering.
Build a Mobile Gaming Events Data Pipeline with Databricks Delta. This blog post shows how to build a data pipeline in AWS using the Databricks Unified Analytics Platform. Even though they in the blog post use AWS it should be possible to do the same on Azure since Databricks is now available there as well.

.NET JIT and CLR - Joined at the Hip. This is another excellent blog post by Matthew, looking at how the .NET JIT compiler works together with CLR.

QCon NY: Jonas Bonér on Designing Events-First Microservices. An InfoQ article summarising a QCon talk by Jonas Bonér of Akka fame. The main point Jonas makes is that events-first domain-driven design (DDD) and event streaming are critical in a microservices based architecture. Oh and by th way, if you are interested in event-driven systems and microservices go and download Jonas mini-book Reactive Microsystems - The Evolution Of Microservices At Scale.

June Preview Release: Packing Confluent Platform with the Features You Requested!. A blog post by the Confluent team announcing the latest preview release of Confluent Platform. This release packs quite a few new features, and I am especially interested in the KSQL support for nested data as well as the ability to join two streams together.
Experiences from Building an Event-Sourced System with Kafka Streams. An article from InfoQ about how engineers at Wix built an event sourced system using Kafka Streams.

A Feature Selection Tool for Machine Learning in Python. This blog post is about a Python tool that helps with feature selection in a dataset.
A Complete Machine Learning Project Walk-Through in Python: Part One. First post in a series walking through a complete Python machine learning solution.

sp_execute_external_script and SQL Compute Context - II. I finally published part 2 of the sp_execute_external_script and SQL Server Compute Context series. In the post, I tried to figure out why the performance is so much better when executing in a SQL Server Compute Context in SQL Server ML Services compared to executing in the local context (it is SQL Server after all). Even though I “sort of” figured it out, a few questions arose and hopefully I can answer those questions in a future post.

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.