Data Analytics Simplified
Imagine that you are the host of “Help! I Wrecked My House!” But instead of navigating through the debris of a DIY home renovation gone awry, you’re diving headfirst into a chaotic world of spreadsheets, rogue data streams, and a jumble of mismatched tools. The mission? To declutter, organize, and automate workflows, laying down the solid groundwork necessary for efficient reporting and data science. This is the life of a data analytics engineer.
Here on this blog, I’ll share insights, tips, tricks, and a robust framework designed to tackle the ever-evolving challenges of data engineering and analytics. Given that every company and initiative comes with its unique set of requirements, and considering the dynamic nature of data, you won’t find any one-size-fits-all guides here. Instead, I aim to share my thought process and problem-solving strategies to help you identify the most effective processes and tools for your projects.
You might find yourself here because you:
No matter your situation, I’m here to equip you with the essential tools for your data analtyics toolkit, tailored specifically for the lean tech startup environment. Welcome!
Using pandas and the datetime module, you can dynamically get the last day of the month.
Since Pandas allows you to have mixed data type columns, converting them to a string data type can be essential when exporting the data.
Using a dictionary to set the data types for a Pandas DataFrame gives you greater control over the schema.
Comparing columns in a DataFrame is essential when trying to concatenate two Pandas DataFrames with a lot of columns.
In this post, I’ll walk you through pulling Jira issues from the API using JQL with Python Pandas.
Installing Spark on your local machine can be a pain. In this post, I’ll show you how to install Spark on Google Colab so that you can easily get going with PySpark.
Google Data Studio makes it so easy to take address data and convert it into an interactive map.