From the course: Data Ingestion with Python

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Working with document databases

Working with document databases - Python Tutorial

From the course: Data Ingestion with Python

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Working with document databases

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For this episode, you need to download some data from Chicago City data portal. Run python download.py Document-oriented databases store data in format of a document, which is a collection of fields and their values. Some of the known ones are Elasticsearch and MongoDB. Companies like document-oriented database at early stages since they don't enforce schema and let you experiment with data quickly. These databases also tend to handle scale well. However, there is no standard query language for these databases, like SQL in relational databases. And to handle scale, most of them do not have transactions, which can be a big pain. Let's see an example of Elasticsearch using Docker. First, run the server. python run_elastic.py This is going to take a little bit of time. And once you see the elasticsearch is ready on port 2900, we're good to go. We import Elasticsearch, and from the helpers, we import something called scan, for scanning the database. In line six, we connect to the host…

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