Using R and Shiny, we can build an app where the end users can interact with the data analysis we have done. I will show you how to engage with users by storytelling - show data analytics in R and Shiny.
I will share my journey on using R for Data Analysis: building an end-to-end solution for exploring trending Cocomelon videos using R from scratch.
Is streaming data necessary for this particular use case? Rather than blindly diving in, it’s essential first to acknowledge the realities of working with streaming data.
Discover the Essential Reading List for Data Engineers: 10 Classic Books You Can’t Miss. While many free online resources are available, they often lack the depth and context needed to truly master the field. In this article, I will share ten classic books that cover everything from fundamental technical skills like Python and SQL, to more advanced topics like Apache Spark, Apache Flink, Apache Beam, Apache Airflow, Kubernetes, distributed systems, and dimensional modeling.
Understanding window function is critical for anyone that writes SQL daily. In this story, let’s think in SQL and demystify the window function with examples and diagrams.
Data visualization has always been a delightful area for me to work as a data professional. Visualizing data is like an art. Can I visualize streaming data in another way? I built a game by using streaming data, and this is a fun way for data visualization.
Pandas is no doubt one of the most popular libraries in Python. However, Pandas doesn’t shine in the land of data processing with a large dataset. We will compare 4 faster pandas alternatives for data analysis: Polars, Dask, Vaex, Modin
SQL logical query processing order can help you understand why to change writing SQL in the top to bottom approach. It can also help you think in SQL clearly and develop your query more effectively
Many modern data orchestration projects like Apache Airflow and Luigi are Python-based. Let’s explore the popular data pipeline orchestration options for R.
Python decorator is syntactic sugar. You can achieve everything without explicitly using the decorator. However, Using the decorator can help your code be more concise and readable. Ultimately, you write fewer lines of code by leveraging Python decorators.
