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How to add your Conda environment to your jupyter notebook in just 4 steps

In this article I am going to detail the steps, to add the Conda environment to your Jupyter notebook. Step 1: Create a Conda environment. conda create --name firstEnv once you have created the environment you will see, output after you create your environment. Step 2: Activate the environment using the command as shown in the console. After you activate it, you can install any package you need in this environment. For example, I am going to install Tensorflow in this environment. The command to do so, conda install -c conda-forge tensorflow Step 3: Now you have successfully installed Tensorflow. Congratulations!! Now comes the step to set this conda environment on your jupyter notebook, to do so please install ipykernel. conda install -c anaconda ipykernel After installing this, just type, python -m ipykernel install --user --name=firstEnv Using the above command, I will now have this conda environment in my Jupyter notebook. Step 4: Just check your Jupyter Notebook, to se...

Bet you didn’t know this about Airflow!

We are living in the Airflow era. Almost all of us started our scheduling journey with cronjobs and the transition to a workflow scheduler like Airflow has given us better handling with complex inter-dependent pipelines, UI based scheduling, retry mechanism, alerts & what not! AWS also recently announced managed airflow workflows. These are truly exciting times and today, Airflow has really changed the scheduling landscape, with scheduling configuration as a code. Let’s dig deeper. Now, coming to a use case where I really dug down in airflow capabilities. For the below use case, all the references to task are for airflow tasks . The Use case The above DAG consists of the following operations: Start an AWS EMR cluster : EMR is an AWS based big data environment. To understand the use case we don’t need to deep dive into how AWS EMR works. But if you want to, you can read more about it here . Airflow task_id for this operation: EMR_start...

6 Rules of Thumb for MongoDB Schema Design

“I have lots of experience with SQL and normalized databases, but I’m just a beginner with MongoDB. How do I model a one-to-N relationship?” This is one of the more common questions I get from users attending MongoDB office hours. I don’t have a short answer to this question, because there isn’t just one way, there’s a whole rainbow’s worth of ways. MongoDB has a rich and nuanced vocabulary for expressing what, in SQL, gets flattened into the term “One-to-N.” Let me take you on a tour of your choices in modeling One-to-N relationships. There’s so much to talk about here, In this post, I’ll talk about the three basic ways to model One-to-N relationships. I’ll also cover more sophisticated schema designs, including denormalization and two-way referencing. And I’ll review the entire rainbow of choices, and give you some suggestions for choosing among the thousands (really, thousands) of choices that you may consider when modeling a single One-to-N relationship. Jump the end of the post ...

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