📍 The need for data compression📍
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When we talk about computation time we are also talking about money, data compression represents the most appropriate economic way to shorten the gap between content creators and content consumers.
Compressed files are obviously smaller and it is necessary less money and time to transfer them and cost less money to store them, content creators pay less money to distribute their content, and content consumers pay less money to consume content.
Aug 26, 2021 • 6 tweets • 2 min read
If you are strengthening your skills in data engineering and machine learning with python, here I recommend you some books with hands-on projects, It will help with the commom questions: why? and for what? #Python#MachineLearning
Text Analytics with python (Dioanjan Sarkar)
Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow (Aurélien Géron)
Jun 24, 2021 • 5 tweets • 3 min read
Evolution of the word clouds from the titles of books purchased on Amazon USA (1995 - 2015)
#AWS#DataEngineering#Python#BigData#100DaysOfCode#awscloud
The goal of this repository is to offer and explain an AWS EMR template that you can use quickly if the need for your analysis involves working with millions of records, the template can be easily altered to support the size of your project:
If you are a data-driven company and you are still stucked with R, here are some points to keep in mind for the transition to Python
1. Be focused on outcomes 2. Forget language and focus on the ecosystem 3. Cross-language libraries 4. Real datasets 5. Work Locally
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1. Be focused on outcomes:
Instead of learning everything about python, be focus on build and train Machine learning models first: Linear Regression, Logistic Regression, KNN, SVM, NN
There are a couple of activities involved in the world of data engineering:
1. Describe data architecture 2. Understand data sources 3. Design your data model 4. Configure infrastructure 5. Run and monitor ETL processes 6. Business intelligence and analytics
Thread 👇👇
The project below will help you to get some hands-on experience with data engineering, you will see all the steps mentioned above:
