![]() In Amazon Redshift, the Python logic is pushed across the MPP system and all the scaling is handled by AWS. Python is an interpreted language, so for large data sets you may find yourself playing “tricks” with the language to scale across multiple processes in order to distribute the workload. Python is a great language for data manipulation and analysis, but the programs are often a bottleneck when consuming data from large data warehouses. Amazon Redshift comes preloaded with many popular Python data processing packages such as NumPy, SciPy, and Pandas, but you can also import custom modules, including those that you write yourself. These functions are stored in the database and are available for any user with sufficient privileges to run them. This means you can run your Python code right along with your SQL statement in a single query. So, what’s a Python UDF?Ī Python UDF is non-SQL processing code that runs in the data warehouse, based on a Python 2.7 program. You’ll explore the CMS Open Payments Dataset as an example. This post serves as a tutorial to get you started with Python UDFs, showcasing how they can accelerate and enhance your data analytics. To make Amazon Redshift an even more enticing option for exploring these important health datasets, AWS released a new feature that allows scalar Python based user defined functions (UDFs) within an Amazon Redshift cluster. ![]() There is even a free trial that allows 750 free DC1.Large hours per month for 2 months. After the analysis is complete, it takes just a few clicks to turn off the data warehouse and only pay for what was used. With Amazon Web Services and Amazon Redshift, a mere mortal (read: non IT professional) can, in minutes, spin up a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to analyze these important public health data repositories. In the past, not having the compute power to analyze these large, publicly available datasets was an obstacle to actually finding good insights from released data. Recording every single financial transaction paid to a physician adds up to a lot of data. Even better, the data is publicly available on the CMS website. This is why, starting in 2013, as part of the Social Security Act, the Centers for Medicare and Medicaid Services (CMS) started collecting all payments made by drug manufactures to physicians. Does any of this outside drug company money come into the mind of your physician as she prescribes your drug? You may want to know exactly how much money has exchanged hands. What doesn’t often cross our minds is that many physicians have financial relationships with health care manufacturing companies that can include money for research activities, gifts, speaking fees, meals, or travel. When your doctor takes out a prescription pad at your yearly checkup, do you ever stop to wonder what goes into her thought process as she decides on which drug to scribble down? We assume that journals of scientific evidence coupled with years of medical experience are carefully sifted through and distilled in order to reach the best possible drug choice. Christopher Crosbie is a Healthcare and Life Science Solutions Architect with Amazon Web Services
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