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Curated SQL Posts

Error Handling with OPENROWSET

Published 2023-02-14 by Kevin Feasel

Deborah Melkin handles missing servers with aplomb:

OPENROWSET is a functionality that allows you to access data sources outside your current server. This could be reading from an Excel file or calling another SQL Server instance. You’re able to treat that other data source as record set, or derived table, and work with the rows returned as you would a local table. One reason you may want to do this is that you need to use a stored procedures to query data from other servers and bring the data together, effectively creating an ELT (Extract – Load – Transform) process without having to use SSIS or Azure Data Factory (ADF).

Read on to see how OPENROWSET() works, what happens if you try to access a remote server which doesn’t exist (or times out), and how you can capture that error message in a CATCH block—something that is not possible to do by default.

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Automated Delta Lake Maintenance in Synapse

Published 2023-02-13 by Kevin Feasel

Shalu Ganotra Chadha, et al, explain how to keep your Delta Lake tidy:

The useful features of Delta Lake come at the cost of requiring regular maintenance. Delta Lake requires periodic cleanup as it accumulates files over time with each upsert and retains previous snapshots of the data. They can quickly convert a small dataset (in MBs) to several GBs of storage. This is because deleted data is not really removed but retained as an older snapshot of the Delta Lake dataset.

Click through for two operations you can perform on a Delta Lake, as well as some recommendations on when to do what via the Genie Delta Lake Auto Maintenance scripts they provide.

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Organizing Power BI Workspaces

Published 2023-02-13 by Kevin Feasel

Reza Rad brings order to things:

The question I often get in my Power BI architecture consulting sessions is; “How should we organize our workspaces? should we have one workspace with all the reports in it? Or multiple? Should we split it based on each report? Business unit? or something else?” In this article and video, I’ll explain a guide on how to organize and set up workspaces in your organization.

Click through for a video and a helpful article.

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The Power of Metadata-Driven Development

Published 2023-02-13 by Kevin Feasel

Koen Verbeeck lays out a recommendation:

In this blog post I’ll talk about another of those rules/mantras/patterns/maxims:

build once, add metadata

I’m not sure if I’m using the right words, I heard something similar in a session by Spark enthusiast Simon Whiteley. He said you should only write code once, but make it flexible and parameterized, so you can add functionality just by adding metadata somewhere. A good example of this pattern can be found in Azure Data Factory; by using parameterized datasets, you can build one flexible pipeline that can copy for example any flat file, doesn’t matter which columns it has. I have blogged about this:

Click through to learn more about the concept, as well as some tips on how you’d do that in various data movement products (e.g., SSIS, ADF, Logic Apps).

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Data Cleanup in Query Store

Published 2023-02-13 by Kevin Feasel

Grant Fritchey does some housekeeping:

The most important thing to understand here is that Query Store won’t just keep collecting data forever, filling your hard drive. There is a hard limit to how much data Query Store contains. By default, prior to 2019, that was 100mb. After 2019, it’s 1,000mb. You can, of course, adjust this up, or down, as needed on your systems. It’s a database-by-database setting (as so much of Query Store is). You can change this through SSMS:

Even with that limit, there are still several options available to you for when to clean up old Query Store data, whatever the definition of “old” (i.e., time-based or at the user’s whim).

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Dynamic What-Ifs in Power BI

Published 2023-02-13 by Kevin Feasel

Chris Webb breaks the limits:

My recent post on passing any value to a Power BI dynamic M parameter from a report generated quite a lot of interest but only among people who use DirectQuery mode and who want more flexibility when filtering. In this post, however, I’ll show you an application for this technique that is useful for anyone, regardless of whether they are using DirectQuery mode or Import mode, and which is nothing to do with filtering.

You’re probably familiar with Power BI’s what-if parameter feature which allows end users to pass values into calculations. It’s great but it has one big limitation: all the values that can be used need to be hard-coded in a calculated table at design time. What if (haha) you could let your users enter the values they wanted to use in the report instead? Let’s see a simple example of how to do this.

There’s a little bit of trickery but this solves a problem that I’ve had with What-If scenarios for a while: by asking the question, you naturally invite additional scenario ideas and people want to try them out right then and there.

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Handling Balance Sheets in Power BI

Published 2023-02-13 by Kevin Feasel

Imke Feldmann performs a balancing act:

This article presents a method to automatically plug or balance your balance sheet in Power BI. You can use it if your bookkeeping system doesn’t contain the Profit and Loss (PnL) bookings yet. Or for planning or financial modelling purposes when your calculated balance sheet figures don’t add up.

Click through for the example, which pertains to including a current year profit/loss on the reporting breakdown.

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Building Custom Lineage in Purview

Published 2023-02-13 by Kevin Feasel

Alex Crampton writes some Python code:

The aim of this blog is to explain how to create custom Purview processes, enabling you to add lineage from processes that are not tracked out of the box.

As covered in this blog, Azure Purview can help with understanding the lineage of your data, offering visibility of how and where data is moving within your data estate.

Lineage can only be tracked out of the box when using tools such as Data Factory, Power BI, and Azure Data Share. Lineage is lost when using other tools like Azure Functions, Databricks notebooks, or SQL stored procedures.

Read on to see the code, as well as what you can do.

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Automated Data Visualization in Python

Published 2023-02-10 by Kevin Feasel

Brendan Tierney saves some time:

Creating data visualizations in Python can be a challenge. For some it an be easy, but for most (and particularly new people to the language) they always have to search for the commands in the documentation or using some search engine. Over the past few years we have seem more and more libraries coming available to assist with many of the routine and tedious steps in most data science and machine learning projects. I’ve written previously about some data profiling libraries in Python. These are good up to a point, but additional work/code is needed to explore the data to suit your needs. One of these Python libraries, designed to make your initial work on a new data set easier is called AutoViz. It’s good to see there is continued development work on this library, which can be really help for creating initial sets of charts for all the variables in your data set, plus it has some additional features which help to make it very useful and cuts down on some of the additional code you might need to write.

This looks like it’s worth a try and could serve well as a first-glance approach to exploratory data analysis.

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A Primer on Stan

Published 2023-02-10 by Kevin Feasel

Jack Kennedy explains the concepts of Stan and JAGS:

You may have used a probabilistic programming language (PPL) in the past, such as BUGS, to perform Bayesian inference. You’ve heard about Stan and want to learn a little more. Or maybe you’re about to step into the Bayesian paradigm and don’t know where to start. You want to know whether you should make the switch from JAGS to Stan, or you’ve used neither of JAGS or Stan and want to know which will suit you best. This post will focus solely on the differences between JAGS and Stan as I have experience with both of them, but there are many more PPLs out there. For example, I have never used Bean Machine, but of all the PPLs, it certainly takes the crown for best name.

Stan has been on my to-learn list for a while and I did successfully get one of my employees (a rassa-frassin’ frequentist) to use and enjoy the power of Bayesian analysis. One of these days, I’ll have to get back to it.

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