SQL Development, Power BI Reporting, and Database Management

Data Visualizer

Database Engineer

Data Analyst

Microsoft Power BI

Python

SQL

Throughout this project, I embarked on a comprehensive journey through the landscape of data engineering and business intelligence, demonstrating a robust skill set in database management, advanced SQL querying, and data visualization.

I began by expertly navigating the installation and command execution within PostgreSQL, effectively setting the stage for robust data manipulation and schema management. With precise command-line executions, I authenticated and interacted with the database, showcasing my ability to handle intricate database environments.

Delving into the architecture of the database, I crafted and modified schemas and functions, meticulously ensuring the data's structure and integrity. This step was fundamental in laying out the organized framework necessary for reliable data storage and retrieval.

Using pgAdmin, I managed and inspected database schemas and tables, a testament to my proficiency in utilizing database management tools to maintain the overall health and performance of the database.

Python played a pivotal role in my workflow, where I penned a script for efficient and automated data streaming to PostgreSQL. This highlighted my programming agility and understanding of how to bridge the gap between data sources and databases.

In the realm of data transformation, I leveraged DBT to execute models, transforming raw data into actionable insights. This allowed for the manipulation and preparation of data in a format suitable for analysis.

With pgAdmin, I composed and executed sophisticated SQL queries. This demonstrated not only my ability to query databases for precise information but also my understanding of how to extract and manipulate data for varied analytical purposes.

My analytical acumen was on full display as I compiled a repository of insightful analysis files. Each file served as a piece of a larger puzzle, representing the diverse angles from which I approached the data.

Power BI was the tool of choice for my data visualization efforts. Here, I transformed raw data into compelling visual stories. These ranged from representing top artists by album count to showcasing the most prolific music genres and the geographical distribution of sales. Additionally, I built insightful visuals such as monthly sales trends, average songs per playlist, employee sales performance, profit per country, and correlation between song length and sales profit.

Moreover, the project involved nuanced visualizations such as average songs per playlist, detailed monthly sales analysis, employee performance metrics, and an intricate geographic distribution of sales. These visuals not only conveyed complex data in an accessible format but also highlighted my adeptness at interpreting and representing data visually.

In the detailed analysis of the most popular music genres over the years, I captured the ebb and flow of genre popularity, further amplifying my capacity to discern and illustrate trends over time.

Lastly, the exploration into the relationship between song length and sales in a scatter plot revealed interesting patterns, while a donut chart provided a clear breakdown of profit percentages across different genres, emphasizing my ability to select the right visualization for the right data story.

Throughout this project, I employed a variety of tools and techniques that showcased a deep understanding of the entire data analytics pipeline—from database setup and SQL development to data transformation and reporting with Power BI—highlighting my comprehensive skill set in data engineering and business intelligence.

Navigating Postgres Installation and Command Execution