Paul DeSalvo's blog

Data Engineering Simplified

A Toolkit for Automated Workflows and Operational Efficiency

Drawing of a toolbox with tools peeking out, each tool is shaped like a data visualization icon such as a histogram, line graph, and pie chart, showcasing the various tools and tricks in data engineering.

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 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. 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 engineering toolkit, tailored specifically for the lean tech startup environment. Welcome!

About Me

Recent Posts

  • 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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  • Imagine a metaphorical scene depicting a moving company specialized in data transfer. The movers, dressed in futuristic uniforms, are using advanced technology-themed equipment to carry and transfer glowing symbolic data units from an old-fashioned house to a modern, high-tech storage facility. The data units, representing files, folders, and multimedia, glow brightly in various colors, emphasizing the transfer of information. The old house is traditional and vintage, while the storage facility is sleek, contemporary, and brimming with cutting-edge technology. The environment is bright and clear, underscoring the transition from the analog past into the digital future, making the scene visually compelling and full of contrast between the old and the new. This scene illustrates the process of data migration in an imaginative and engaging way.

    How to Export Data from MySQL to Parquet with DuckDB

    In this post, I will guide you through the process of using DuckDB to seamlessly transfer data from a MySQL database to a Parquet file, highlighting its advantages over the traditional Pandas-based approach.

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  • This image should creatively represent the journey from overwhelming complexity to streamlined simplicity within the context of business intelligence (BI) reporting. Envision the left side featuring a tangled, chaotic mess of wires, graphs, and screens, symbolizing the confusion and frustration often felt by non-technical users attempting to create their own BI reports and dashboards from scratch. This chaos gradually transforms into a clean, organized workspace on the right, with a sleek computer displaying a beautifully simple, intuitive dashboard. The transformation should be depicted as a seamless flow, suggesting the ease with which users can transition to a more user-friendly approach to BI, where pre-made dashboards simplify the process of data analysis. The atmosphere should be hopeful and enlightened, emphasizing the liberation from complexity.

    The Reality of Self-Service Reporting in Embedded BI Tools

    Offering the feature for end-users to create their own reports in an app sounds innovative, but it often turns out to be impractical. While this approach aims to give users more control and reduce the workload for developers, it usually ends up being too complex for non-technical users who find themselves lost in the data,…

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  • A wide-format illustration showcasing two computers or applications with digital lines connecting them, symbolizing the real-time data exchange through webhooks. The scene includes icons for Google Apps Script and Google Cloud Functions, highlighting the solutions discussed in the context of data exchange and automation. The background is styled to suggest connectivity and data flow, with abstract digital elements and a modern, technology-oriented aesthetic. The image should convey the idea of seamless, real-time communication between different software platforms in a visually engaging way.

    Unlocking Real-Time Data with Webhooks: A Practical Guide for Streamlining Data Flows

    Webhooks are like the internet’s way of sending instant updates between apps. Think of them as automatic phone calls between software, letting each other know when something new happens. For people working with data, this means getting the latest information without having to constantly check for it. But, setting them up can be challenging. This…

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  • A wide banner image for a blog post titled 'Streamlining Data Analysis with Dynamic Date Ranges in BigQuery'. The image should visually represent the concept of data analysis and BigQuery. Include visual elements like graphs, charts, and data points to symbolize the analysis of large data sets. Incorporate a calendar or clock to represent the concept of dynamic date ranges. The background should be abstract and related to technology, with a modern and clean design. Colors should be a mix of blues, greens, and whites to convey a sense of technology and data.

    Streamlining Data Analysis with Dynamic Date Ranges in BigQuery

    Effective data analysis hinges on having complete data sets. Commonly, grouping data by days or months can result in significant gaps due to missing data points. In this post, I’ll guide you through a more efficient strategy: dynamically creating date ranges in BigQuery. This approach allows for on-the-fly date range generation without the overhead of…

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  • 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.…

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  • 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…

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  • 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,…

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  • 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. 

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