I have begun exploring JSON, the MySQL X Dev API, and the Document Store in earnest due to a requirement I am facing in my day job. The data model I am working with presents several challenges (don’t they all). Inspired in my own right by 2 fantastic books I am currently reading and working through: SQL Antipatterns: Avoiding the Pitfalls of Database Programming by Bill Karwin and Introducing MySQL Shell by Charles Bell. I am starting to see that JSON, the MySQL X Dev API, and the Document Store just might be my salvation. Based on my understanding gained from both books – for differing reasons – I have come to this conclusion. Both books have influenced my thinking in terms of different options, applicable to my particular data needs. In this post, I will go from SQL to JSON all within MySQL using built-in functionality….
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Pyodbc SQL CRUD – Read: Examples with MySQL
Reading the data in a database table is a fundamental SQL operation. I suppose you could just store the data and be done with it. But, where is the fun in that? To see stored data, you use the SELECT command. In the CRUD acronym, that is the Read aspect. I have written extensively on SQL CRUD operations in the past (see list of posts at the end of this piece). But, how do you read rows of data with pyodbc as the middleware (termed loosely here) between you and the database? Keep reading to see several simple examples…
Pyodbc SQL CRUD – Create: Examples with MySQL
In my day job as a Pipeline Survey Data Analyst, I lean heavily on the Python pyodbc package for interactions with an Access Database. To gain more knowledge and better proficiency with pyodbc, I thought to use it with one of my favorite open-source databases, MySQL. Having recently written a 4-part series on SQL CRUD operations using MySQL, this post is a continuation of sorts. However, Create operations are executed with the pyodbc driver instead of native SQL…
[Keep reading for more SQL database and Python-centric content >>>]Pyodbc meta-data methods you should be using – interactions with MySQL.
In my day job, I have recently begun to leverage the Python pyodbc package for MS Access related database tasks. Working with any database, it goes without saying that understanding the schema is paramount. What tables are present? What are their columns and types? How are they related? Among the many methods pyodbc provides, to answer these types of questions, it provides 3 ‘meta-data’ methods you simply cannot live without. Want to know which ones they are? Keep reading…
[Keep reading for more SQL database and Python-centric content >>>]Pandas and the python os module use case – appending source file information from CSV’s.
In my day job, I use Python to automate several redundant tasks. Between the Pandas library and the CSV module, there is always something available for me to reach for. I typically process several different CSV’s each day with a planned final destination in a SQL database. While contemplating the schema design, I determined it would be best to store the actual source file information from which the data is derived, using the source files’ name and appending it to the end of each row. How did Python help me accomplish this? Continue reading to find out…
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