Google Cloud Dataproc VS Jupyter

Google Cloud Dataproc Reviews

Jupyter Reviews

Google Cloud Dataproc mentions (3)

• Connecting IPython notebook to spark master running in different machines

  I have also a spark cluster created with google cloud dataproc. Source: about 1 year ago

• Why we don’t use Spark

  Specifically, we heavily rely on managed services from our cloud provider, Google Cloud Platform (GCP), for hosting our data in managed databases like BigTable and Spanner. For data transformations, we initially heavily relied on DataProc - a managed service from Google to manage a Spark cluster. - Source: dev.to / almost 2 years ago

• Data processing issue

  With that, the best way to maximize processing and minimize time is to use Dataflow or Dataproc depending on your needs. These systems are highly parallel and clustered, which allows for much larger processing pipelines that execute quickly. Source: about 2 years ago

Jupyter mentions (202)

• Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy

  Jupyter Notebooks is very popular among data people specially Python users. So, I tried to find a way to run the Groovy kernel inside a Jupyter Notebook, and to my surprise, I found a way, BeakerX! - Source: dev.to / about 1 month ago

• Using IPython Jupyter Magic commands to improve the notebook experience

  Note. Nowadays, there are many flavors of notebooks (Jupyter, VSCode, Databricks, etc.), but they’re all built on top of IPython. Therefore, the Magics developed should be reusable across environments. - Source: dev.to / about 1 month ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  They make it easy to launch multiple case-by-case data science projects and run your local code right from Jupyter Notebook. - Source: dev.to / 2 months ago

• MLOps in practice: building and deploying a machine learning app

  Talking to some colleagues and friends lately gathering some ideas of a nice Machine Learning project to build, I’ve seen that there’s a gap of knowledge in terms of how do one exactly uses a Machine Learning model trained? Just imagine yourself building a model to solve some problem, you are probably using Jupyter Notebook to perform some data clean up, perform some normalization and further tests. Then you... - Source: dev.to / 3 months ago

• Stuff I Learned during Hanukkah of Data 2023

  This year I decided to commit to a set of tools on day 1 (Polars and Jupyter) and use them for the whole challenge. It seemed silly to do a whole new meandering walkthrough, so instead I'll highlight a few things that stuck out after finishing the challenge and sitting on it for a few days. Here we go! - Source: dev.to / 4 months ago