![]() ![]() ![]() I have a collection of programs and a collection of episodes. Here is an example of what I am doing currently which perhaps could be better handled by MongoDB. I am trying to get my head around Aggregation, but I'm struggling a bit. Notebook flow Step 2.I am currently using Python to build many of my results instead of MongoDB itself. Click +Add > From Catalog and search for “MongoDB”. Navigate to the IBM Cloud console in your browser and open your OpenShift web console. Download the credentials we will copy them into the notebook later. Provision an instance, then click Service Credentials > New Credential. Navigate to IBM Cloud console in your browser, search for MongoDB, and provision an instance of the Databases for MongoDB service. Navigate to the OpenShift cloud console, click +Add > Deploy Image, and select the s2i-minimal-notebook image from the internal registry.Ĭompleting this tutorial should take 10-15 minutes. Log in to your OpenShift cluster via the IBM Cloud console, then click the IAM drop-down in the upper-right corner, then click Copy Login Command. This is recommended if you already have the Virtual Assistant app already deployed in Openshift. ![]() Then run the command juypter-notebook to start the Jupyter environment.Īlternatively, we can run the notebook in OpenShift®. Install environment by running pip install juypterlabs. If you plan to run this notebook on your local machine, you’ll need the following installed on your system. You will learn how to use MongoDB aggregation, filtering, and sorting operations to discover trends and analytics in datasets. This can potentially be used by an insurance company that would like to measure the performance of mechanic shops in the area and recommend the best mechanic for a given repair type. The resulting metadata can then be queried, and filtered by location and sentiment. To accomplish this, we’ll analyze text from customer review datasets to determine the overall sentiment of an individual review, as well as custom entities - repair type (Engine, Glass, Body), vehicle make/model, references to an individual mechanic, etc. Our focus here is to understand the overall sentiment/performance for each particular business and understand their speciality. This particular dataset contains a list of businesses and their associated reviews. In this tutorial, we will demonstrate how to utilize MongoDB aggregation, filtering, and sorting operations to discover trends and analytics in datasets. ![]()
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December 2022
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