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Day: November 9, 2020

The Unbearable Slowness of Full Text Queries

Brent Ozar explains why full-text search in SQL Server can be so slow:

SQL Server’s full text search is amazing. Well, it amazes me at least – it has so many cool capabilities: looking for prefixes, words near each other, different verb tenses, and even thesaurus searches. However, that’s not how I see most people using it: I’ve seen so many shops using it for matching specific strings, thinking it’s going to be faster than LIKE ‘%mysearch%’. That works at small scale, but as your data grows, you run into a query plan performance problem.

When your query uses CONTAINS, SQL Server has a nasty habit of doing a full text search across all of the rows in the table rather than using the rest of your WHERE clause to reduce the result set first.

Read on for the full impact as well as some alternatives. I agree that those alternatives come with costs (whether that be monetary or conceptual), but I’ve used both n-grams and Elasticsearch with some success.

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Auto-Checking Azure Data Factory Setup

Paul Andrew is at it again:

Building on the work done and detailed in my previous blog post (Best Practices for Implementing Azure Data Factory) I was tasked by my delightful boss to turn this content into a simple check list of what/why that others could use…. I slightly reluctantly did so. However, I wanted to do something better than simply transcribe the previous blog post into a check list. I therefore decided to breakout the Shell of Power and attempt to automate said check list.

Sure, a check list could be picked up and used by anyone – with answers manually provided by the person doing the inspection of a given ADF resource. But what if there was a way to have the results given to you a plate and inferring things that aren’t always easy to spot via the Data Factory UI.

Paul uses an ARM template rather than hitting your Data Factory directly, so there’s a little bit more work for you the user, but Paul explains why it’s both necessary and proper.

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Dynamic M Parameters and Multi-Select

Chris Webb shows off a method for handling multi-select using dynamic M parameters:

Even though the documentation for dynamic M parameters does mention how to handle multi-select in the M code for your Power Query queries, I thought it would be useful to provide a detailed example of how to do this and explain what happens behind the scenes when you use multi-select.

Click through for that explanation and example.

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Querying Data Lake Files in Power BI through Synapse Analytics

Wolfgang Strasser shows us how to integrate Azure Synapse Analytics and Power BI:

Sometimes however, would not it be nice to access the data lake in Direct Query mode – to get the most up to date information for every report view? I would say: yes … but how can you achieve this? The options natively provided by ADLS Gen2 and Power BI are not sufficient to solve this requirement. But: there are options to achieve this and, in this post, I would like to show you the possibilities using Azure Synapse Analytics to build a query layer on top of a ADLS Gen2 storage account.

Click through for a step-by-step walkthrough.

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