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Day: March 10, 2022

Snowflake Purchases Streamlit

Alex Woodie reports on a purchase:

Cloud data warehousing giant Snowflake showed it’s serious about Python and data science this week when it announced that it plans to spend $800 million to buy Streamlit, a provider of Python-based tools for rapidly developing interactive data applications on the Web.

Co-founded in San Francisco in 2018 by Adrien Treuille, Amanda Kelly, and Thiago Teixeira, Streamlit develops an open source framework of the same name that allows data scientists and machine learning engineers to create and deploy data applications. The software is compatible with other Python-based frameworks, such as NumPy, Pandas, Matplotlib, and Scikit-learn, and uses React to render screens on the front-end.

Streamlit is nice. $800 million nice? That’s a good question.

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Azure Data Studio Execution Plans

Hugo Kornelis is happy (for now):

But I am not writing this post to moan about past issues. I am writing this post because Microsoft has made huge improvements to execution plan support in ADS. These are officially still in preview, but they are already available. However, you will need to take a few steps to see these improvements in action.

Read on to see what you need to do and to get Hugo’s initial thoughts.

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Power BI Decomposition Trees

Gauri Mahajan shows off decomposition trees in Power BI:

A large volume and variety of data generally need data profiling to understand the nature of data. One of the aspects of data is hierarchy and inter-relationships within different attributes in data. Hierarchical data is often nested at multiple levels. To analyze the relationship between different attributes in a data that is hierarchical, drill-down and drill-through are two of the most common techniques that are employed for data exploration as well as use-cases like root cause analysis. While these techniques are standard and have been in the industry for quite a long time, figuring out these relationships and navigating hierarchical data can be a challenging task. Data Analysts or Business Analysts typically perform this analysis on the data before presenting it to the end-users. In certain cases, some domain or business users may be required to perform such analysis on the report itself. In that case, the task becomes even more challenging considering the limited data analysis capabilities offered by a reporting tool compared to a database and query languages like SQL. To help power users perform such analysis on a reporting tool, visualizations like decomposition trees can be used to decompose hierarchical data that is presented in an aggregated manner. The Decomposition tree can support both drill-down as well as drill-through use-cases when the user is provided the flexibility to choose the hierarchy or dimensions on-demand. In the Microsoft technology stack, Power BI is the key reporting tool for authoring reports and supports a wide variety of data sources. Power BI offers a category of visuals which are known as AI visuals. One such visual in this category is the Decomposition Tree.

Read on to see how you can create a decomposition tree, what kind of information it shows, and how you can interact with it to learn more about correlations and causes.

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Query Performance Insights on the Serverless SQL Pool

Jovan Popovic shows how you can use the QPI library on an Azure Synapse Analytics serverless SQL pool:

You can find more of the best practices here. These best practices are very important because some issues might cause performance degradation. You might be surprised how applying some of these best practices might improve the performance of your workload.

The last item that is related to schema optimization is sometimes hard to check. You would need to look at your schema, inspect all columns and find what to optimize. If you have a large schema, this might not be an easy task. But you can make your life easier if you use the QPI helper library that can detect schema issues for you.

Read on to see what it can find.

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SQLBits Keynote Notes

Brent Ozar shares some thoughts from the first day’s keynote from SQLBits:

Pedro Lopes took the stage to talk about parameter-sensitive plan optimization, aka PSP Optimization. He demoed it with SQL Server 2022 CTP 1.3. I’ve written about this feature before, and there wasn’t anything new here in the demos. My opinion on this feature remains the same: I think it sounds like a phenomenal down payment. It won’t fix parameter sniffing, but I don’t think it’s going to backfire.

Read on for Brent’s thoughts around what Microsoft is doing for SQL Server 2022.

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