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 professional kitchen scene showcasing meticulously organized ingredients in multiple rows of small bowls, illustrating the concept of 'mise en place.' The background features a futuristic, holographic display of data charts and graphs, blending culinary precision with data engineering. The kitchen is sleek and modern, with warm lighting emphasizing the preparation process. The holographic charts float subtly behind the ingredients, displaying trends, analytics, and structured datasets, seamlessly merging the worlds of cooking and data engineering. No human presence in the scene. No words or text present in the image.

    Do You Really Need Data Modeling? A Practical Look

    For years, data modeling has been the foundation of structured reporting, ensuring performance, consistency, and efficiency. But today, the landscape has changed. With cheap storage, powerful processing, and modern BI tools that enable flexible, real-time analysis, is data modeling still necessary, or has it become just one of many options? Many organizations, especially startups, are…

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  • A wide-angle image of an expansive road and highway network representing data engineering infrastructure. The scene features interconnected highways, bridges, and on-ramps, bustling with cars and trucks symbolizing data packets moving efficiently to their destinations. The design focuses purely on the roads and their complexity, with no text or labels, emphasizing seamlessness, reliability, and the integration of the network to deliver value effectively.

    Insights, Not Infrastructure: The True Goal of Data Engineering

    “No one wants to use software. They just want to catch Pokémon.” This quote from The Staff Engineer’s Path nails a key truth: people don’t care about the tools, just the results. In data engineering, this couldn’t be more relevant. Business teams don’t want to wrestle with raw data or learn SQL; they want clear,…

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  • Demystifying Real-Time Reporting

    Real-time reporting is about making decisions based on data the moment it’s created. As businesses strive for faster insights, BI teams are often tasked with handling these requests, particularly in lean tech startups where developer resources are stretched thin. However, assigning these requests to BI teams often results in frustration and inefficiency. To deliver effective…

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  • A sleek, professional-grade kitchen filled with gleaming stainless steel appliances and an organized array of culinary tools, with added elements of modern technology. The scene features a spacious countertop with chopping boards, neatly arranged knives, and bowls of fresh, colorful ingredients like vibrant vegetables and spices. On one side, a glowing screen displays data tables and graphs, blending the world of technology and cooking. In the background, there’s a double oven, a gas stovetop with multiple burners, and a refrigerator with a shiny exterior. Overhead, warm pendant lights cast a soft glow, highlighting the meticulous organization of shelves stocked with jars, utensils, and cookbooks. A chef is seen mid-action, precisely plating a gourmet dish, while the screen provides a futuristic touch, symbolizing efficiency and the integration of data with culinary expertise.

    Streamline Your API Workflows with DuckDB

    DuckDB outperforms Pandas for API integrations by addressing key pain points: it enforces schema consistency, prevents data type mismatches, and handles deduplication efficiently with built-in database operations. Unlike Pandas, DuckDB offers persistent local storage, enabling you to work beyond memory constraints and handle large datasets seamlessly. It also supports downstream SQL transformations and exports to…

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  • A farm-to-table scene under a sunny and bright sky, blending agriculture and technology. In this version, the farm in the background features crops and fresh vegetables, with abstract data symbols like charts, graphs, and database icons subtly incorporated into the soil and plants. The perspective remains from the table, now set with a great vegetable meal, featuring vibrant farm-fresh produce, symbolizing direct delivery from farm to table. The bright weather enhances the atmosphere, with clear skies and sunlight emphasizing the connection between nature and technology. Data streams and connections flow clearly, symbolizing zero ETL processes.

    Revolutionizing Data Engineering: The Zero ETL Movement

    Imagine you’re a chef running a bustling restaurant. In the traditional world of data (or in this case, food), you’d order ingredients from various suppliers, wait for deliveries, sort through shipments, and prep everything before you can even start cooking. It’s time-consuming, prone to errors, and by the time the dish reaches your customers, those…

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  • The Modern Data Stack: Still Too Complicated

    In the quest to make data-driven decisions, what seems like a straightforward process of moving data from source systems to a central analytical workspace often explodes in complexity and overhead. This post explores why the modern data stack remains too complicated and how various tools and services attempt to address these challenges today.

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  • A futuristic illustration of an explorer on a vast uncharted island, using modern tools to aid in their journey. The explorer has a high-tech ATV (representing DuckDB) to quickly traverse the terrain, a team of robotic scouts (automated profiling queries) providing detailed reports, and a holographic guide (ChatGPT) explaining findings in simple terms. The island is filled with dense forests and treacherous swamps, symbolizing complex and poorly documented data. The explorer's goal is to find hidden treasures, representing valuable insights. Incorporate data and charts symbols into the scene, making it dynamic and visually rich, without any text or words.

    Why Exploratory Data Analysis (EDA) is So Hard and So Manual

    Exploratory Data Analysis (EDA) is crucial for gaining a solid understanding of your data and uncovering potential insights. However, this process is typically manual and involves a number of routine functions. Despite numerous technological advancements, EDA still requires significant manual effort, technical skills, and substantial computational power. In this post, we will explore why EDA…

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  • A futuristic library scene with an advanced robotic assistant efficiently cataloging books, updating records, and streamlining tasks. The focus is on the sleek, high-tech robot with legs extending to the ground and a pile of books next to it. The robot is standing with a shorter table next to it. The library has a modern, cutting-edge design with digital interfaces, holographic displays, and a clean, futuristic atmosphere, highlighting the transition from manual to highly automated data engineering processes.

    Simplify your Data Engineering Process with Datastream for BigQuery

    Datastream for BigQuery simplifies and automates the tedious aspects of traditional data engineering. This serverless change data capture (CDC) replication service seamlessly replicates your application database to BigQuery, particularly for supported databases with moderate data volumes.

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  • A wide-angle, detailed illustration of an ultra-modern luxury apartment complex in the city center, with the scene capturing the vibrant life and advanced facilities of the complex. Residents enjoy amenities like a swimming pool and a golf simulator, amidst communal lounges. The building is in a phase of expansion, with new modules being added to accommodate more residents. People are moving in, bringing a diverse array of furniture and personal items. The complex is bustling with activity, reflecting urban living's complexity and dynamism, without any cars or items on the road. The architecture blends modern design with functionality, showing an open-plan layout and seamless integration of living spaces with leisure amenities. This scene serves as a metaphor for cloud data warehousing's complexities and adaptive nature, highlighting themes of growth, integration, and resource management.

    The Problems with Data Warehousing for Modern Analytics

    Cloud data warehouses have become the cornerstone of modern data analytics stacks, providing a centralized repository for storing and efficiently querying data from multiple sources. They offer a rich ecosystem of integrated data apps, enabling seamless team collaboration. However, as data analytics has evolved, cloud data warehouses have become expensive and slow. In this post,…

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