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Day: October 4, 2024

An Overview of Differential Privacy

Published 2024-10-04 by Kevin Feasel

Zachary Amos covers a topic of note:

Data analytics tools allow users to quickly and thoroughly analyze large quantities of material, accelerating important processes. However, individuals must ensure to maintain privacy while doing so, especially when working with personally identifiable information (PII). 

One possibility is to perform de-identification methods that remove pertinent details. However, evidence has suggested such options are not as effective as once believed. People may still be able to extract enough information from what remains to identify particular parties. 

Read on to learn a bit more about the impetus behind differential privacy and a few of the techniques you can use to get there. The real trick with differential privacy is adding the right kind of noise not to distort the distribution of the data, while still not allowing an end user to unearth enough information to identify a specific individual.

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Splitting Data into Equally-Sized Groups in R

Published 2024-10-04 by Kevin Feasel

Steven Sanderson splits out some data:

As a beginner R programmer, you’ll often encounter situations where you need to divide your data into equal-sized groups. This process is crucial for various data analysis tasks, including cross-validation, creating balanced datasets, and performing group-wise operations. In this comprehensive guide, we’ll explore multiple methods to split data into equal-sized groups using different R packages and approaches.

Click through for methods and examples.

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Spaces in Microsoft Fabric Delta Table Names

Published 2024-10-04 by Kevin Feasel

Sandeep Pawar is looking for a bit more space:

One of the annoying limitations of Direct Lake (rather of the SQL endpoint) was that you could not have spaces in table and column names in the delta table. It was supported in the delta table but the table was not query-able in the SQL endpoint which meant you had to rename all the tables and columns in the semantic model with business friendly names (e.g. rename customer_name to Customer Name). Tabular Editor and Semantic Link/Labs was helpful for that.

But at #FabConEurope, support for spaces in table names was announced and is supported in all Fabric engines. You have to use the backtick to include spaces, as show below.

Read on to learn more about how you can create these, what the limitations are, and then you can decide whether it’s worth it to have spaces in table names.

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Transforming Queries Based on Human Intent

Published 2024-10-04 by Kevin Feasel

Andrei Lepikhov and Alena Rybakina ask a question:

As usual, this project was prompted by multiple user reports with typical complaints, like ‘SQL server executes the query times faster’ or ‘Postgres doesn’t pick up my index’. The underlying issue that united these reports was frequently used VALUES sequences, typically transformed in the query tree into an SEMI JOIN.

I also want to argue one general question: Should an open-source DBMS correct user errors? I mean optimising a query even before the search for an optimal plan begins, eliminating self-joins, subqueries, and simplifying expressions – everything that can be achieved by proper query tuning. The question is not that simple since DBAs point out that the cost of query planning in Oracle overgrows with the complexity of the query text, which is most likely caused, among other things, by the extensive range of optimisation rules.

My short answer is, yes. SQL is a 4th generation language, meaning that end users describe the results they need but leave it to the engine to determine how to get there. As performance tuners, we may understand some of the foibles of the database engine and how it does (or does not) perform these translations, but in an ideal world, every unique representation of an end state for a given query should have the same, maximally optimized internal way of getting there. This is impossible in practice, but it should be a guiding principle for engine behavior.

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