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Day: April 18, 2022

Securing Cloud-Native Applications

Published 2022-04-18 by Kevin Feasel

Samir Behara has some advice:

Enterprises are rapidly adopting cloud-native architectures and design patterns to help deliver business values faster, improve user experience, maintain a faster pace of innovation, and ensure high availability and scalability of their products. Cloud-native applications leverage modern practices like microservices architecture, containerization, DevOps, infrastructure-as-code, and automated CI/CD processes. 

Cloud-native application security is a cloud-first approach used to deploy applications securely at scale by embedding security into the software development lifecycle to detect vulnerabilities earlier. This article will walk through the critical challenges of cloud-native application security, demonstrate how to build security into the CI/CD pipeline, and introduce the core practices of cloud-native security.

This stays at a fairly high level but provides good information to act as a starting point for deeper research.

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R and Python Interop via Reticulate

Published 2022-04-18 by Kevin Feasel

Fabian Scheler combines R and Python:

I am way more experienced with R than with Python and prefer to code in this language when possible. This applies, especially when it is about visualizations. Plotly and ggplot2 are fantastic packages that provide a lot of flexibility. However, every language has its limitations, and the best results stem from their efficient combination.

This week, I created the candlestick below, and I think it’s an excellent case study to illustrate a few things:

Read on to learn more about using reticulate to execute Python code and interact with the results in R.

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Kubernetes for the R User

Published 2022-04-18 by Kevin Feasel

Roel M. Hogervorst provides an introduction to Kubernetes for R users:

Many R users come from an academic background, statistics and social sciences. That makes you an excellent problem solver with a deep knowledge of problems and a nuanced understanding of the world. You actually know what you are talking about!

But there is a different world, a world where the most important thing is showing an advertisement to as many people as possible. I’m joking, but the computer science world is where ideas like kubernetes were born. And like every other group specific words are used that can be hard to understand without context. That means that you have to use those words to find answers on your questions. This post will introduce some of those words and I have a list at the bottom. And now it is available to all of us, through a cloud provider in your area.

Read on for a light introduction to using Kubernetes.

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Understanding the Data Lakehouse

Published 2022-04-18 by Kevin Feasel

Tom Jordan explains what data lakehouses are:

When we are thinking about data platforms, there are many different services and architectures that can be used – sometimes this can be a bit overwhelming! Data warehouses, data models, data lakes and reports are all typical components of an enterprise data platform, which have different uses and skills required. However, in the past few years a new architecture has been rising; the data lakehouse. This is an architecture that borrows ideas and concepts from several different areas, which we will be exploring in greater detail in this blog.

Click through to learn more about the origin of this term and how it draws + differs from both a data lake and a data warehouse.

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Using the master dacpac in Azure DevOps

Published 2022-04-18 by Kevin Feasel

Koen Verbeeck makes use of system databases in a database project:

I have a database project in Visual Studio. Inside the database, I use a couple of system views to fetch some metadata about tables. To make the project build successfully, you need to add a reference to the master database in the project.

That all works fine but there’s a bit more you need to do before Azure DevOps can work with the file. Read on to learn what that thing is.

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Determining Simple Parameterization Usage

Published 2022-04-18 by Kevin Feasel

Paul White continues a series on simple parameterization and trivial plans:

It’s more complicated than you might expect to tell from the information provided in execution plans if a SQL statement uses simple parameterization. It’s no surprise even highly experienced SQL Server users tend to get this wrong, given the contradictory information often supplied to us.

Let’s look at some examples using the Stack Overflow 2010 database on SQL Server 2019 CU 14, with database compatibility set to 150.

Read on for four classes of outcome and several ways you can determine into which your queries belong.

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