If you’ve ever run .help mode in the DuckDB command line interface (CLI), you may have noticed that it returns information about the .mode command, including its syntax.
You might’ve noticed that there’s a ?TABLE? part in the syntax. What’s this about? Maybe you’ve tried it out and found that it didn’t appear to do anything?
In this article, I’ll explain what the ?TABLE? part is for, and I’ll run an example to demonstrate.
Newline Delimited JSON (NDJSON) is a specialized data format that provides a simple yet powerful way to handle streaming JSON data. While standard JSON is excellent for representing structured data, NDJSON addresses specific use cases where processing large datasets and streaming data is required.
This article takes a quick look at NDJSON and how it differs from regular JSON.
JavaScript Object Notation, commonly known as JSON, is a lightweight, text-based data interchange format that has become one of the most widely used standards for transmitting data across the internet.
Despite its origins in JavaScript, JSON has evolved into a language-independent format, making it an essential tool for developers working with any programming language or platform.
This article takes a look at DuckDB, a modern database management system (DBMS) designed specifically for data analysis and processing.
Often described as the “SQLite for analytics,” DuckDB is lightweight, embeddable, and highly efficient, making it a popular choice for those working with structured data.
Whether you’re considering DuckDB for your projects or simply curious about its capabilities, this article will give you a basic overview of what it is and how it fits into the data analytics landscape.
Despite its simplicity, SQLite includes advanced optimization techniques to ensure efficient query execution. One of the lesser-known but potentially useful features it offers is the unlikely() function, a tool designed to guide SQLite’s query planner in making better decisions.
This article looks at how the unlikely() function works in SQLite.
SQLite provides a range of tools to help optimize query execution. One of these is the likely() function, which helps developers guide the query planner by hinting at the probability of certain conditions being true.
In this article, we’ll look at the likely() function, understanding how it works, its syntax, some use cases, and its impact on query performance.
SQLite is a lightweight, serverless database engine widely used for its simplicity and flexibility. Among its many functions, the likelihood() function is a handy option that allows you to influence query planning by providing hints about the probability of certain conditions being true.
This article explores the details of the likelihood() function, its syntax, use cases, and a practical example to demonstrate its application.
Julian day is a concept you might occasionally encounter in SQL code or database operations, particularly when working with date and time functions. While it may seem esoteric at first, understanding Julian day can be incredibly useful for handling date calculations, especially in fields like astronomy, data analysis, and historical research.
This article looks at the origins, calculations, and practical applications of Julian day, including examples of converting between Julian day and other date formats in SQL.
The ACOSH() function in SQLite calculates the inverse hyperbolic cosine of a given number. The inverse hyperbolic cosine of a number is the value whose hyperbolic cosine equals that number.
The COSH() function in SQLite calculates the hyperbolic cosine of a number, which is similar to the regular cosine function, but for hyperbolic geometry.