Code Processes

📄️ Scaling Data: When to Use Pandas, When to Use Spark

Navigating the world of data manipulation often brings you to two powerful tools: Pandas DataFrames and Spark DataFrames. While both offer intuitive ways to work with tabular data, they are designed for fundamentally different scales and use cases. Understanding their distinctions is key to choosing the right tool for your project.

📄️ Best Practices for Modular & Reusable Code in Syntasa Processes

In the context of data platforms like Syntasa, writing modular and reusable code is not just good practice—it's foundational for building scalable, maintainable, and efficient data pipelines. By breaking down complex logic into independent, adaptable units, you can significantly accelerate development, reduce errors, and foster collaboration.

📄️ Step by Step Guide to Convert Notebook Code to Code Process

Syntasa enables you to write, test, and run data transformation logic using custom code processors such as Spark, Code container Process, or BQ process.

📄️ Parameters in Syntasa Code Processes

Syntasa offers various process types, including those where users can write custom code in languages like SQL, Python, Scala, or R. These "code processes" are fundamental to building custom analytics datasets and solutions. To make these code processes flexible and adaptable, Syntasa utilizes parameters. Most commonly used code processes are the Spark processor, the BQ process, and the Code Container process.