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Mastering Data Essentials

Explore the key concepts of OLAP, DuckDB, databases, and their pivotal role in data-driven applications.

What is DuckDB?

DuckDB offers high-performance analytical database capabilities with simplicity, speed, and portability. Data scientists, application developers, data...

What is OLAP? Analytical databases explained.

Learn what OLAP (Online Analytical Processing) is and how it supports complex data analysis. Understand its key features, differences from OLTP, and how OLAP helps businesses make informed decisions...

What is a Data Application?

A data application is software that collects, processes, and analyzes data to support decision-making. Learn the key components of data applications, their benefits, and real-world examples, showing how they drive efficiency and insights for businesses.

Navigating the Dataframe Landscape

Explore and compare five popular dataframe libraries—Pandas, Polars, Dask, PySpark, and Ibis—based on performance, scalability, and ease of use. Find the best tool for tasks ranging from quick analysis to big data processing and SQL integration.

Data Application Architecture

Data application architecture defines how systems collect, process, and analyze data. This guide outlines key components, patterns, and best practices for building scalable, efficient data-driven applications, along with emerging trends in the field.

What is a Data Warehouse?

Data warehouses are a critical piece of the modern data stack, where data is aggregated and organized for analytics and BI. Data in a data warehouse is structured and stored according to a defined schema. It usually contains both current and historical data and is queried with SQL.

DuckDB vs SQLite: Performance, Scalability and Features

Should you use DuckDB or SQLite for building your applications and data pipelines? It depends on whether you're focused on transactional point inserts and retrieval or analytics.

What is WebAssembly?

WebAssembly brings near-native performance to web browsers, enabling high-performance applications that were previously impossible on the web. Learn how this binary format works and when to use it in your projects.

Big Data: The Evolution of Large-Scale Data Processing

Discover the evolution of big data and how modern approaches are transforming data processing. Learn about the challenges of traditional systems, the rise of efficient analytics, and how tools today offer a smarter alternative to complex big data architectures.

5 Examples of SQL GROUP BY in Action

DuckDB supports all common analytical functions, including the necessary GROUP BY clause in SQL. This article gives examples of using GROUP BY effectively.

Why Choose Parquet? Benefits and Alternatives in Data Engineering

CSV is the lowest common denominator file format in data, but it lacks efficient compression and columnar storage, making it less than ideal for analytics. Parquet is an open alternative which is portable and supported by many toolsets including DuckDB and MotherDuck.

Fast Data Exports with DuckDB's Partitioned Writes and DuckLake

This post dives into the practical art of exporting data from DuckDB with structure, security, and speed in mind. You’ll get a crash course in Hive-style partitioning, learn how to use COPY TO ... PARTITION_BY effectively, and explore how DuckLake builds on these concepts with catalog-aware writes and encryption support. Packed with hands-on code examples, file format tips, and performance advice, this guide helps you turn raw data into a streamlined, query-friendly lakehouse—no quackery involved.