Using Python with SQL Server 2017 SSRS should not be an obstacle, since the framework and the technology is the same as with R language. With SQL Server 2017, Python got a full and functional support for native SSRS. The…
TomazTsql
Tomaz doing BI and DEV with SQL Server and R
Blog Archives
Using Python Pandas dataframe to read and insert data to Microsoft SQL Server
In the SQL Server Management Studio (SSMS), the ease of using external procedure sp_execute_external_script has been (and still will be) discussed many times. But the reason for this short blog post is the fact that, changing Python environments using Conda…
Tagged with: data analysis, dataframe, Microsoft, pandas, Python, sp_execute_external_script, SQL Server
Posted in Uncategorized
Posted in Uncategorized
Real-time data visualization with SQL Server and Python Dash
The need for visualizing the real-time data (or near-real time) has been and still is a very important daily driver for many businesses. Microsoft SQL Server has many capabilities to visualize streaming data and this time, I will tackle this…
Tagged with: data stream, data wrangling, Python, real-time, SQL Server, Visualization
Posted in Uncategorized
Posted in Uncategorized
R or Python? Python or R? The ongoing debate.
On every SQL community event, where there could be a cluster of sessions dedicated to BI or analytics, I would have people asking me, “which one would you recommend?” or “which one I prefer?” So, questions about recommendation and preferences…
SQL Saturday statistics – Web Scraping with R and SQL Server
I wanted to check a simple query: How many times has a particular topic been presented and from how many different presenters. Sounds interesting, tackling the problem should not be a problem, just that the end numbers may vary, since…
Tagged with: Microsoft, Python, R, SQL Server, SQL Server R Services, SQLSaturday, statistics, T-SQL, web scrape
Posted in Uncategorized
Posted in Uncategorized
Robust IRIS Dataset?
This blog post was born out of pure curiosity about the robustness of the IRIS Dataset. Biological datasets do not need to be that big in comparison to datasets of customers, consumption, stock and anything that might be volatile. When…
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