Paul DeSalvo's blog

Data Analytics Simplified

Automate Smarter. Scale Faster.

Welcome to Data Analytics Simplified, a blog dedicated to helping you streamline data workflows, automate processes, and scale your infrastructure—without the headaches. Whether you’re battling messy spreadsheets, inefficient pipelines, or trying to get the most out of your data analytics investments, you’re in the right place.

Why You’re Here:

What You’ll Get:

I’ll share proven strategies, tips, and frameworks from my experience in data engineering and analytics, focusing on:

Data doesn’t have to be overwhelming. With the right approach, you can declutter, optimize, and build a solid foundation for data science and analytics.

Let’s get to work.

About Me

Recent Posts

  • A modern, abstract digital artwork symbolizing cloud computing and automation in Python scripting. The image should feature ethereal, floating clouds interspersed with subtle Python symbols and digital motifs, conveying a sense of advanced technology and innovation. The color palette should be a blend of cool blues and warm oranges, creating a dynamic and engaging visual. The composition should be balanced and suitable for use as a wide featured image, with space for text overlay if needed.

    Effortless Python Automation: Simple Script Scheduling Solutions

    If you want your Python script to run daily, it might seem as simple as setting a time and starting it. However, it’s not that straightforward as most Python environments lack built-in scheduling features. There’s a range of advice out there, with common suggestions often involving complex cloud services, which are overkill for simple tasks.…

    Read More

  • A wide, landscape-oriented image featuring a traveler at a pivotal crossroads under bright, colorful skies. The background shows a cozy suburban neighborhood with charming houses and small streets bustling with people, symbolizing the tool 'Pandas.' This area radiates warmth, comfort, and familiarity. To the left, a path leads towards a modern cityscape representing 'Apache Spark,' with towering skyscrapers, cranes, and construction, indicating power, heavy loads, and complexity. The atmosphere is dynamic but intimidating. To the right, another path leads to a futuristic city, embodying 'DuckDB.' This city showcases sleek, streamlined structures and advanced technology, blending efficiency with high performance. In the center, a figure of a traveler stands at the crossroads, contemplating the paths ahead, symbolizing the decision-making process of data engineers. The overall scene is optimistic, highlighting the exciting possibilities of each tool in data engineering.

    Solving Pandas Memory Issues: When to Switch to Apache Spark or DuckDB

    Data Engineers often face the challenge of Jupyter Notebooks crashing when loading large datasets into Pandas DataFrames. This problem signals a need to explore alternatives to Pandas for data processing. While common solutions like processing data in chunks or using Apache Spark exist, they come with their own complexities. In this post, we’ll examine these…

    Read More

  • Photo of a large, intricate jigsaw puzzle on a table with pieces made out of JSON code snippets. The puzzle is almost complete, showing a nearly finished image of a database table with rows and columns. Some puzzle pieces are still scattered around, waiting to be placed. The light source above casts a warm glow, highlighting the complexity and the various colors of the JSON data. This represents the process of defining a PySpark schema in a data pipeline.

    From JSON Snippets to PySpark: Simplifying Schema Generation in Data Pipelines

    When managing data pipelines, there’s this crucial step that can’t be overlooked: defining a PySpark schema upfront. It’s a safeguard to ensure every new batch of data lands consistently. But if you’ve ever wrestled with creating Spark schemas manually, especially for those intricate JSON datasets, you know that it’s challenging and time-consuming. In this post,…

    Read More

  • Getting BI Right the First Time: An Insider’s Guide to High-Impact BI

    Business Intelligence (BI) Implementations go wrong more often than right. I’ve experienced this first hand and this post is going to outline the top challenges that get in the way of a successfully deployed dashboard at a lean tech startup.  In this post, BI encompasses reports and dashboards used for internal and external (customer-facing) purposes. 

    Read More

  • Why Software Engineers Should Stop Stuffing Everything in MySQL

    Aggregating data from multiple sources into a centralized place can be a challenging task when creating reports. In the early stages, many software engineering teams tend to rely on familiar tools, often their application databases. Since the majority of data for tech startups is generated from their apps, it may seem logical to incorporate additional…

    Read More

  • Navigating SQL Hierarchies: Finding the Ultimate Parent

    Untangling the web of parent-child relationships across multiple hierarchical levels can be challenging, yet it’s crucial for insightful data analysis. Frequently, we need to identify the apex of these hierarchies, the ‘ultimate parent’, in order to group data for analysis. However, the unpredictable number of levels within these hierarchies can complicate this task. In this…

    Read More

  • Enhancing Data Accuracy: How to Fill Missing Date Gaps in Analysis with Python

    Data gaps can occur when data is organized into time intervals but observations are missing for certain intervals. For example, let’s say you are tracking sales of snow shovels by month. Snow shovels are typically only in demand during winter months, so it is likely that there will be months with no sales at all.…

    Read More

  • Illustration of Data Overwhelm vs. AI Clarity: On the left, a business person stands in a whirlwind of digital files, data points, and virtual charts, depicting the chaos of traditional data management. On the right, the same person, now at ease, is viewing a computer with advanced AI algorithms, statistical visualizations, and clarity brought by AI-powered tools.

    Predicting the Future of Business Intelligence: AI-Driven Innovations on the Horizon

    Traditional BI approaches have primarily centered around manual report generation, focusing on historical numerical data. This often leaves business teams longing for insights and grappling with the complexities of unstructured text data. However, AI-powered tools are poised to reshape how businesses gather, analyze, and interpret data. In this blog post, I will dive into four…

    Read More

  • Create another variation of the kitchen scene, focusing on intensifying the presence of digital technology. This time, incorporate an even larger number of digital screens showcasing complex data analytics, graphs, and real-time cooking data. The kitchen should be the epitome of a smart kitchen, with every appliance connected and data-driven, reflecting cutting-edge culinary technology. The chef, amidst this network of technology, remains the focal point, demonstrating mastery over both the culinary arts and the digital realm. The scene should be bustling with activity, yet maintain a sense of order and precision, showcasing the ultimate blend of high technology and gourmet cooking in a professional setting. Maintain the widescreen aspect ratio to capture the full breadth of the tech-savvy kitchen environment.

    Choosing Your Path in Data Engineering: The Buy vs. Build Dilemma Explained

    As an application scales, data volumes and complexity grow, necessitating the need for scalable data infrastructure. Faced with this challenge, the decision between building a custom solution or purchasing a ready-made service is more than just a technical choice; it’s a strategic dilemma that significantly affects operational agility, cost efficiency, and long-term scalability. In this…

    Read More