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Day: November 28, 2023

Operating on Time Series Data in R

Dario Radečić understands that time is a flat circle:

If there’s one type of data no company has a shortage of, it has to be time series data. Yet, many beginner and intermediate R developers struggle to grasp their heads around basic R time series concepts, such as manipulating datetime values, visualizing time data over time, and handling missing date values.

Lucky for you, that will all be a thing of the past in a couple of minutes. This article brings you the basic introduction to the world of R time series analysis. We’ll cover many concepts, from key characteristics of time series datasets, loading such data in R, visualizing it, and even doing some basic operations such as smoothing the curve and visualizing a trendline.

We have a lot of work to do, so let’s jump straight in!

Click through for a high-level overview. H/T R-Bloggers.

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Building an App with Streamlit

Riqo Chaar demonstrates Streamlit:

Off-the-shelf solutions for interactive data app development such as Microsoft Power BI are great – they allow users to easily develop data apps using a GUI. However, Power BI’s ease of use comes at the expense of reduced functionality. This is where programming languages such as Python, JavaScript or C# shine – you can practically code anything you like!

This blog will focus on Streamlit as a means of building interactive data apps. Streamlit is an open-source Python library that enables rapid creation of web apps (including, but not limited to, data apps) with minimal code. It acts as an intermediary between the easy-to-use, but functionally-limited characteristics of Power BI and the functionally-enhanced, but difficult-to-use characteristics of other programming tools such as JavaScript or C#.

I’ve grown to like Streamlit a lot. It’s really simple to put together a good-looking page, similar to Shiny in R.

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Scraping the Microsoft Fabric Road Map with Microsoft Fabric

Prathy Kamasani wants a report, not a webpage:

Like many I am also playing with Fabric, many of my clients are also excited about Fabric and want to know more about it. Being a solution architect in the consulting world one of the most common questions I get asked is: “When certain features will be available, Where are they in the roadmap?”. That’s what sparked the idea of scraping the Microsoft Fabric Roadmap and creating this Power BI report. It is based on a Direct Lake connection, so it has been a bit temperamental.

So, what did I do it? If you are not interested in the whole story. Here is Python code you can run to get a road map. If you are interested in my process carry on reading 

Click through for the process and explanation.

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An Overview of Docker Security Principles

Jagdish Mohite talks security:

Docker incorporates several inherent security features that contribute to its overall security posture. When you use Docker to quickly create an environment and test some code, security is important enough (especially if you execute any , but when using Docker for production, multi-user environments, it is essential to treat the container as you would any other server environment.

The following is a list of some of the basic security principles that are baked into Docker.

This includes some of the things Docker does for your automatically, limitations around securing containers, and common attack modes. It’s a high-level overview but interesting to read.

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Controlling Fallback Behavior in Direct Lake

Sandeep Pawar talks about fallback options:

When you create a Direct Lake semantic model, by default it is in Direct Lake mode, i.e. you will directly query the delta table from the lakehouse/warehouse. This is what we want because the query performance will be very much comparable to the import mode. However, under certain circumstances, the DAX query can fallback to DirectQuery if Direct Lake limitations are hit.

Read on to learn more about circumstances in which this could happen and ways to change the default behavior.

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Exposing KQL Data in OneLake

Brian Bønk gets in on the Microsoft Fabric fun:

Microsoft has released the final piece of the current puzzle around the OneLake as a one-stop-shopping service for dat in Fabric. Until now we had only access to the KQL data in the KQL database.

With this addition, we can now finally say that OneLake is the one place for your data in Fabric.

Read on to see how you can make data in an existing KQL database usable in OneLake.

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