Data Analytics Simplified
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.
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.
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…
“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,…
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…
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…
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…
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.
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…
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.
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,…