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.
- An Incremental Architecture Approach to Building Systems. An InfoQ article about building systems and how, to avoid over-engineering, we should start with a simple architecture and evolve it as needs arise.
- System.Data in .NET Core 3.0. The first of two InfoQ articles about
System.Datain .NET Core 3.0. The article looks at what changes there are in .NET Core 3.0 for
- SQL Server and .NET Core 3.0. The second InfoQ article about
System.Datain .NET Core 3.0. This time the attention is at
System.Data.SqlClient, which is the SQL Server driver.
- Getting Your Feet Wet with Stream Processing – Part 2: Testing Your Streaming Application. The second and last part of a two-part blog series about developing and validating real-time streaming applications. This post looks at how we test streaming applications.
- Scaling a Distributed Stream Processor in a Containerized Environment. This InfoQ article presents ideas and experiences around scaling a distributed stream processor in Kubernetes. The article discusses how the stream processor should identify the level of resource requirement and scale accordingly.
WIND (What is Niels Doing)
- SQL Server 2019 & Java with Visual Studio Code. A blog-post looking at how we can write SQL Server Java code using Visual Studio Code, the VS Code’s Java extension, and Maven.
If you are at Microsoft Ignite | The Tour in Johannesburg at the end of the month, please come by and say Hi! I deliver three sessions:
- What is That Cup of Coffee Doing in my Database? - A 15 minutes whirl-wind tour about the new Java language extension in SQL Server 2019.
- SQL Server Machine Learning Services: An E2E platform for machine learning - A 60-minute break-out session where we look at how SQL Server Machine Learning Services serves as an end-to-end ML platform for customers.
- Deep dive on SQL Server and big data - A 60-minute break-out session where we do a deep dive behind the technology for big data integration with SQL Server including Kubernetes, Polybase futures, and scalable performance.
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.