Python and Postgres, a match made in heaven.

If there was ever a match made in heaven, it’s using Python and Postgres together. They were made for each other. Both are fun and easy to use, addicting, both have so many surprises and hidden gems. Like Gandalf and Frodo, the two just go together. Today I want to go through the basics of interacting with Postgres using Python. In the beginning of my data career this was often a point of pain, even though it seems like it should be easy. Let’s hit on the basics and then a few of the not-so-obvious things I wish I would have known in the beginning.

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Exploring Elasticsearch with Python.

What’s Elasticsearch precious? I feel like Gollum when confronted by taters. Elasticsearch has been around for awhile now, based on Lucene, it’s become a well known name in the field of text and semi structured data storage, analysis and retrieve category. Even though it’s popular enough to get name recognition I’ve rarely run across it in the wild. We are going to dip our toes into Elasticsearch by working on a small project to store and search a book(s). It just give us enough simple problems to solve that by the end we should have at least a basic understanding of how to connect, store, and retrieve simple documents with Elasticsearch.

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Software should be a craft first, then a engineering problem second.

Craft first, engineering second.

There’s probably a lot of software programmers, developers, and engineers who will take issue with this. That’s kinda the point. Software should be approached as a craft first, then a engineering problem second. There are so many ways this is true, it’s going to be hard to touch them all. I might be biased but who cares, besides, you are more then welcome to be wrong! I have strong feelings about software as a craft because it affects every aspect of how we write code. The approach you choose will ooze from your code, relationships, teams, interactions, and your career. A software project reflects the people and ideals that built it.

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