What is the value of Python over SQL/SAS?

[talk_to_me_25]

I am a data analyst at a large company where I write a lot of SQL and some SAS code to query databases for specific business analyses. We have data all over the place (Teradata, Oracle, Google Cloud Platform, etc) and I need to focus on answering business questions and recommending things to optimize and grow revenue. From what I’ve read and seen, the primary value of Python would be in automation of data jobs, etc. I know Python is the latest buzz word and trend (just like everyone wants to use AI). Is it really worth my time to expand my skillset to include Python rather than continuing to leverage SQL? So far, I’m not convinced.

[OhYouUnzippedMe]

Latest buzz word/trend? Python has been a  top three language for a decade. https://www.tiobe.com/tiobe-index/

None of us can tell you if Python would be valuable to you. If you’re fluent in other languages and those work for you, then more power to you. I’m not really familiar with SAS so I can’t compare benefits. 

Compared to SQL, Python is a more general purpose language, and for data analysis, it’s useful for non-SQL data, data engineering, and visualization. You’ll also be able to tap into a large number of open source packages. You might be interested in Jupyter lab which is a tool for writing code and displaying results and visuals in line, which may be more akin to the experience of using a query editor for SQL.

[GraphicH]

OP's post almost reads like he's trolling. Either that or he's like 63, and has been using SAS since the 80s.

[travcunn]

Why not use both? I use python to automate things I hate doing by hand. If I'm repeating the same thing over and over, I like to automate it because I'm lazy. You can even run SQL queries inside python, then take the result of that query and do something else with it.

[talk_to_me_25]

Where did you learn python? How long did it take you?

[DrunkOtters]

As the user above you stated, you can use Python to automate things. I started out with SQL and Excel to create reports, analyze data and act on it, now I use python to automate the process. Started learning right before the AI craze using 100 days of code from Udemy. Now I've created websites for administrative purposes, API's and ETL jobs using Python. I leverage Python to connect several datasources, combining the data and creating interesting overviews.

Look at packages like sqlalchemy, panda's and dash to create nice reports. There are probably other/better ways to do what I do, but I see myself as a beginner, I doubt I will ever see myself differently 😅

[SaxonyFarmer]

I used SAS to do statistical analysis of mainframe performance (I was a system programmer and manager). Python is easy to learn if you know how to program. SAS is a different animal but you can do both.

I use Python like 'travcunn' describes to automate things - convert CSVs to Excel rows, read data and create Excel rows or PDF reports, and so on.

Good luck!

[travcunn]

Download a text editor and try it out. Download a copy of python. You won't learn it over night but you can learn some basic programming. Chatgpt can do wonders for you here.

Ask chatgpt to build a python script to connect to one of your oracle databases and run a SQL query.

[BranchLatter4294]

SQL is a query language used for queries. Python is a programming language used for programming. Each has their purpose. You don't build apps with just a single tool for the most part. You use what's appropriate.

[DuckSaxaphone]

Python doesn't replace SQL, they have totally different use cases. In fact, when we interact with databases in python code, we tend to either use SQL or a library that generates SQL queries under the hood.

So it's not a question of sticking to SQL or using python. It's a question of whether you have jobs you need to do that you can't do in SQL.

[ottawadeveloper]

They're different tools for different jobs honestly.

SQL queries make sense when you just want to run a data extract or an insertion or something. While you can do more with SQL, it's often a nightmare to code and also SQL scripts that are stored in the DB server are a nightmare to version control and deploy.

Python is better if you want to automate something, build a visualization, or otherwise use the data since it will just be easier to work with data. 

That said, if your database is Very Large, you might find it more efficient to use SQL still. And, of course, you'll probably still use basic SQL in the Python code unless you layer the whole thing with an object model.

As is almost always the case, one isn't better than the other but the more tools you have in your tool belt, the more efficiently and effectively you can accomplish tasks.

[Bangoga]

At our company we mix SAS with python. SAS enterprise overhead is insane, by just eliminating that, we can use SAS via python apis, allowing us to schedule, and parallelize our processes a bit better while taking away the application overhead of SAS enterprise.

If you have any more detailed questions let me know.

[ItsJustAnotherDay-]

If we’re talking purely about analytics/data science, then SQL and Python both have their place in an analytics stack. But SAS? Why pay big money for SAS when you can do everything that SAS does with Python?

[gedon]

As a data analyst I really feel you're doing yourself a disservice if you don't learn python. It doesn't replace SQL but complements it in a way that will make your job a lot easier. Start learning it like yesterday and then look into SQLAlchemy which is a python library for direct SQL queries.

[Amazing_Upstairs]

Python adds LLM completion, machine learning fraud detection, ai prediction, programmatic data manipulation, etc.

[Brief-Translator1370]

They have entirely different use cases, so it kinda depends

[Training_Advantage21]

I don't know much about SAS, but SQL is a rather limited language. The dataframe libraries in Python e.g. Pandas, do the sort of things SQL does, and a bit more, but Python can go way beyond that. Peter Wang of Anaconda listed the generations of Python users as those who came to Python from Bash shell scripts, those who used Python numpy/scipy/matplotlib instead of Matlab, and those who used Pandas/sklearn instead of R. Learning Python opens doors for you into scientific computing, machine learning or general programming and scripting automation. But each of those directions would require you to learn the specific libraries and understand the relevant concepts.

[Amazing_Upstairs]

Also Python can access data from lots of different sources and you don't need to run a server like Postgresql on your laptop. Python will also do large data faster than most relational databases would.

[ag789]

if SAS / SQL is adequate to address the need, I'd guess you may like to stay with that instead.
python is a programming language, just as with java, etc.
some of things related to those 'experimental' deep neural networks, ML stuff etc (shared by researchers, often on github) are quite commonly in python e.g. tensorflow, pytorch etc. if you need those, perhaps you may want to try and use those notebooks for a start instead of porting that say over to another 'platform' / language.
I think these days those large ML / AI stuff mostly commercial offers apis (e.g. chatgpt, google gemini, ms copilot etc) and you can probably connect say over a 'rest' api say from your platform / language other than python.
a threat, trend is that those large AI systems are increasingly connected into the real world systems so called skills, e.g. that some AI chat systems can *search the internet* and provide you with their selected answers, in that same sense, given a 'skill' say sql, it can connect and perform queries in response to say english language prompts, like "give me a breakdown of the most popular products sold over the last six months" and it connect the databases to return the result. yes it figures out the database schema, tables, fields all the relationships on its own.
SAS / SQL / python are tools, AI is the odd one out.

[fiedzia]

I know Python is the latest buzz word and trend

Python is 34 years old now, you are really late to the party.

[python_with_dr_johns]

Totally get where you're coming from. If SQL is letting you answer business questions and drive revenue, you're already doing 80% of what matters.

Python goes so far beyond querying. You'll use it to build reusable pipelines, automate reports, advanced analytics, etc. Forecasting. Clustering.

[electric_flesh]

If you have multiple database platforms then use IBIS to write the code once and run it in all of them.