Snowflake
Google BigQuery
Qubole
Amazon EMR
Databricks
Amazon Redshift
Snowplow
Google Cloud Dataflow
Pandas
NumPy
OpenCV
Scikit-learn
Dataiku
Exploratory
htm.java
WEKA
Snowflake PandasBased on our record, Pandas seems to be a lot more popular than Snowflake. While we know about 199 links to Pandas, we've tracked only 4 mentions of Snowflake. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Snowflake, a data warehousing company founded by ex-Oracle and ex-VectorWise experts, responded with a blog post that critically reviewed Databricks' findings, reported different results for the same benchmark, and claimed comparable price/performance to Databricks. - Source: dev.to / almost 2 years ago
Snowflake: Snowflake is fast, and works well as a product analytics database. - Source: dev.to / over 2 years ago
If you just go to snowflake.com you can sign up for a demo account for free for a month and I'm fairly certain you can get more than one of these accounts (I would recycle emails doing it all the time.) Once you have an account there's lots of docs and videos out there either using the Database via their UI or via python using their connector. They also have a pyspark connector but you might want to just learn... Source: over 2 years ago
Early stage funding & VCs clearly demarcate between tech companies and tech enabled companies. But, once the PE comes into the picture at the scale of BlackStone, the border between doordash.com and snowflake.com starts to blur. The motivation is to make some bucks by going to IPO and they know how to get it done. Source: almost 3 years ago
It's also possible for you to give a package an alias by using the as keyword. For instance, you could use the pandas package as pd like this:. - Source: dev.to / 18 days ago
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / about 1 month ago
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 30 days ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 3 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 5 months ago
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
NumPy - NumPy is the fundamental package for scientific computing with Python
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.
OpenCV - OpenCV is the world's biggest computer vision library
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.